MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification

Effective integration of multi-omics with prior knowledge to identify biomarkers via explainable graph neural networks

The rapid growth of multi-omics datasets and the wealth of biological knowledge necessitates the development of effective methods for their integration. Such methods are essential for building predictive models and identifying drug targets based on a limited number of samples. We propose a framework called GNNRAI for the supervised integration of multi-omics data with biological priors represented as knowledge graphs. Our framework leverages graph neural networks (GNNs) to model the correlation structures among features from high-dimensional 'omics data, which reduces the effective dimensions in data and enables us to analyze thousands of genes simultaneously using hundreds of samples. Furthermore, our framework incorporates explainability methods to elucidate informative biomarkers. We apply our framework to Alzheimer's disease (AD) multi-omics data, showing that the integration of transcriptomics and proteomics data with prior AD knowledge is effective, improving the prediction accuracy of AD status over single-omics analyses and highlighting both known and novel AD-predictive biomarkers.

How did we get there? AI applications to biological networks and sequences

The rapidly advancing field of artificial intelligence (AI) has transformed numerous scientific domains, including biology, where a vast and complex volume of data is available for analysis. This paper provides a comprehensive overview of the current state of AI-driven methodologies in genomics, proteomics, and systems biology. We discuss how machine learning algorithms, particularly deep learning models, have enhanced the accuracy and efficiency of embedding sequences, motif discovery, and the prediction of gene expression and protein structure. Additionally, we explore the integration of AI in the embedding and analysis of biological networks, including protein-protein interaction networks and multi-layered networks. By leveraging large-scale biological data, AI techniques have enabled unprecedented insights into complex biological processes and disease mechanisms. This work underlines the potential of applying AI to complex biological data, highlighting current applications and suggesting directions for future research to further explore AI in this rapidly evolving field.

Artificial intelligence-driven clinical decision support systems for early detection and precision therapy in oral cancer: a mini review

Oral cancer (OC) is a significant global health burden, with life-saving improvements in survival and outcomes being dependent on early diagnosis and precise treatment planning. However, diagnosis and treatment planning are predicated on the synthesis of complicated information derived from clinical assessment, imaging, histopathology and patient histories. Artificial intelligence-based clinical decision support systems (AI-CDSS) provides a viable solution that can be implemented via advanced methodologies for data analysis, and synthesis for better diagnostic and prognostic evaluation. This review presents AI-CDSS as a promising solution through advanced methodologies for comprehensive data analysis. In addition, it examines current implementations of AI-CDSS that facilitate early OC detection, precise staging, and personalized treatment planning by processing multimodal patient information through machine learning, computer vision, and natural language processing. These systems effectively interpret clinical results, identify critical disease patterns (including clinical stage, site, tumor dimensions, histopathologic grading, and molecular profiles), and construct comprehensive patient profiles. This comprehensive AI-CDSS approach allows for early cancer detection, a reduction in diagnostic delays and improved intervention outcomes. Moreover, the AI-CDSS also optimizes treatment plans on the basis of unique patient parameters, tumor stages and risk factors, providing personalized therapy.

Navigating the Multiverse: a Hitchhiker's guide to selecting harmonization methods for multimodal biomedical data

The application of machine learning (ML) techniques in predictive modelling has greatly advanced our comprehension of biological systems. There is a notable shift in the trend towards integration methods that specifically target the simultaneous analysis of multiple modes or types of data, showcasing superior results compared to individual analyses. Despite the availability of diverse ML architectures for researchers interested in embracing a multimodal approach, the current literature lacks a comprehensive taxonomy that includes the pros and cons of these methods to guide the entire process. Closing this gap is imperative, necessitating the creation of a robust framework. This framework should not only categorize the diverse ML architectures suitable for multimodal analysis but also offer insights into their respective advantages and limitations. Additionally, such a framework can serve as a valuable guide for selecting an appropriate workflow for multimodal analysis. This comprehensive taxonomy would provide a clear guidance and support informed decision-making within the progressively intricate landscape of biomedical and clinical data analysis. This is an essential step towards advancing personalized medicine. The aims of the work are to comprehensively study and describe the harmonization processes that are performed and reported in the literature and present a working guide that would enable planning and selecting an appropriate integrative model. We present harmonization as a dual process of representation and integration, each with multiple methods and categories. The taxonomy of the various representation and integration methods are classified into six broad categories and detailed with the advantages, disadvantages and examples. A guide flowchart describing the step-by-step processes that are needed to adopt a multimodal approach is also presented along with examples and references. This review provides a thorough taxonomy of methods for harmonizing multimodal data and introduces a foundational 10-step guide for newcomers to implement a multimodal workflow.

New horizons at the interface of artificial intelligence and translational cancer research

Artificial intelligence (AI) is increasingly being utilized in cancer research as a computational strategy for analyzing multiomics datasets. Advances in single-cell and spatial profiling technologies have contributed significantly to our understanding of tumor biology, and AI methodologies are now being applied to accelerate translational efforts, including target discovery, biomarker identification, patient stratification, and therapeutic response prediction. Despite these advancements, the integration of AI into clinical workflows remains limited, presenting both challenges and opportunities. This review discusses AI applications in multiomics analysis and translational oncology, emphasizing their role in advancing biological discoveries and informing clinical decision-making. Key areas of focus include cellular heterogeneity, tumor microenvironment interactions, and AI-aided diagnostics. Challenges such as reproducibility, interpretability of AI models, and clinical integration are explored, with attention to strategies for addressing these hurdles. Together, these developments underscore the potential of AI and multiomics to enhance precision oncology and contribute to advancements in cancer care.

Multimodal data integration in early-stage breast cancer

The use of biomarkers in breast cancer has significantly improved patient outcomes through targeted therapies, such as hormone therapy anti-Her2 therapy and CDK4/6 or PARP inhibitors. However, existing knowledge does not fully encompass the diverse nature of breast cancer, particularly in triple-negative tumors. The integration of multi-omics and multimodal data has the potential to provide new insights into biological processes, to improve breast cancer patient stratification, enhance prognosis and response prediction, and identify new biomarkers. This review presents a comprehensive overview of the state-of-the-art multimodal (including molecular and image) data integration algorithms developed and with applicability to breast cancer stratification, prognosis, or biomarker identification. We examined the primary challenges and opportunities of these multimodal data integration algorithms, including their advantages, limitations, and critical considerations for future research. We aimed to describe models that are not only academically and preclinically relevant, but also applicable to clinical settings.

Benchmarking multi-omics integrative clustering methods for subtype identification in colorectal cancer

BACKGROUND AND OBJECTIVE

Colorectal cancer (CRC) represents a heterogeneous malignancy that has concerned global burden of incidence and mortality. The traditional tumor-node-metastasis staging system has exhibited certain limitations. With the advancement of omics technologies, researchers are directing their focus on developing a more precise multi-omics molecular classification. Therefore, the utilization of unsupervised multi-omics integrative clustering methods in CRC, advocating for the establishment of a comprehensive benchmark with practical guidelines.

METHODS

In this study, we obtained CRC multi-omics data, encompassing DNA methylation, gene expression, and protein expression from the cancer genome atlas (TCGA)database. We then generated interrelated CRC multi-omics data with various structures based on realistic multi-omics correlations, and performed a comprehensive evaluation of eight representative methods categorized as early integration, intermediate integration, and late integration using complementary benchmarks for subtype classification accuracy. Lastly, we employed these methods to integrate real-world CRC multi-omics data, survival and differential analysis were used to highlight differences among newly identified multi-omics subtypes.

RESULTS

Through in-depth comparisons, we observed that similarity network fusion (SNF) exhibited exceptional performance in integrating multi-omics data derived from simulations. Additionally, SNF effectively distinguished CRC patients into five subgroups with the highest classification accuracy. Moreover, we found significant survival differences and molecular distinctions among SNF subtypes.

CONCLUSIONS

The findings consistently demonstrate that SNF outperforms other methods in CRC multi-omics integrative clustering. The significant survival differences and molecular distinctions among SNF subtypes provide novel insights into the multi-omics perspective on CRC heterogeneity with potential clinical treatment.

miss-SNF: a multimodal patient similarity network integration approach to handle completely missing data sources

MOTIVATION

Precision medicine leverages patient-specific multimodal data to improve prevention, diagnosis, prognosis, and treatment of diseases. Advancing precision medicine requires the non-trivial integration of complex, heterogeneous, and potentially high-dimensional data sources, such as multi-omics and clinical data. In the literature, several approaches have been proposed to manage missing data, but are usually limited to the recovery of subsets of features for a subset of patients. A largely overlooked problem is the integration of multiple sources of data when one or more of them are completely missing for a subset of patients, a relatively common condition in clinical practice.

RESULTS

We propose miss-Similarity Network Fusion (miss-SNF), a novel general-purpose data integration approach designed to manage completely missing data in the context of patient similarity networks. miss-SNF integrates incomplete unimodal patient similarity networks by leveraging a non-linear message-passing strategy borrowed from the SNF algorithm. miss-SNF is able to recover missing patient similarities and is "task agnostic", in the sense that can integrate partial data for both unsupervised and supervised prediction tasks. Experimental analyses on nine cancer datasets from The Cancer Genome Atlas (TCGA) demonstrate that miss-SNF achieves state-of-the-art results in recovering similarities and in identifying patients subgroups enriched in clinically relevant variables and having differential survival. Moreover, amputation experiments show that miss-SNF supervised prediction of cancer clinical outcomes and Alzheimer's disease diagnosis with completely missing data achieves results comparable to those obtained when all the data are available.

AVAILABILITY AND IMPLEMENTATION

miss-SNF code, implemented in R, is available at https://github.com/AnacletoLAB/missSNF.

Amogel: a multi-omics classification framework using associative graph neural networks with prior knowledge for biomarker identification

The advent of high-throughput sequencing technologies, such as DNA microarray and DNA sequencing, has enabled effective analysis of cancer subtypes and targeted treatment. Furthermore, numerous studies have highlighted the capability of graph neural networks (GNN) to model complex biological systems and capture non-linear interactions in high-throughput data. GNN has proven to be useful in leveraging multiple types of omics data, including prior biological knowledge from various sources, such as transcriptomics, genomics, proteomics, and metabolomics, to improve cancer classification. However, current works do not fully utilize the non-linear learning potential of GNN and lack of the integration ability to analyse high-throughput multi-omics data simultaneously with prior biological knowledge. Nevertheless, relying on limited prior knowledge in generating gene graphs might lead to less accurate classification due to undiscovered significant gene-gene interactions, which may require expert intervention and can be time-consuming. Hence, this study proposes a graph classification model called associative multi-omics graph embedding learning (AMOGEL) to effectively integrate multi-omics datasets and prior knowledge through GNN coupled with association rule mining (ARM). AMOGEL employs an early fusion technique using ARM to mine intra-omics and inter-omics relationships, forming a multi-omics synthetic information graph before the model training. Moreover, AMOGEL introduces multi-dimensional edges, with multi-omics gene associations or edges as the main contributors and prior knowledge edges as auxiliary contributors. Additionally, it uses a gene ranking technique based on attention scores, considering the relationships between neighbouring genes. Several experiments were performed on BRCA and KIPAN cancer subtypes to demonstrate the integration of multi-omics datasets (miRNA, mRNA, and DNA methylation) with prior biological knowledge of protein-protein interactions, KEGG pathways and Gene Ontology. The experimental results showed that the AMOGEL outperformed the current state-of-the-art models in terms of classification accuracy, F1 score and AUC score. The findings of this study represent a crucial step forward in advancing the effective integration of multi-omics data and prior knowledge to improve cancer subtype classification.

Molecular insights fast-tracked: AI in biosynthetic pathway research

Covering: 2000 to 2025This review explores the potential of artificial intelligence (AI) in addressing challenges and accelerating molecular insights in biosynthetic pathway research, which is crucial for developing bioactive natural products with applications in pharmacology, agriculture, and biotechnology. It provides an overview of various AI techniques relevant to this research field, including machine learning (ML), deep learning (DL), natural language processing, network analysis, and data mining. AI-powered applications across three main areas, namely, pathway discovery and mining, pathway design, and pathway optimization, are discussed, and the benefits and challenges of integrating omics data and AI for enhanced pathway research are also elucidated. This review also addresses the current limitations, future directions, and the importance of synergy between AI and experimental approaches in unlocking rapid advancements in biosynthetic pathway research. The review concludes with an evaluation of AI's current capabilities and future outlook, emphasizing the transformative impact of AI on biosynthetic pathway research and the potential for new opportunities in the discovery and optimization of bioactive natural products.

M3NetFlow: A multi-scale multi-hop graph AI model for integrative multi-omic data analysis

Multi-omic data-driven studies are at the forefront of precision medicine by characterizing complex disease signaling systems across multiple views and levels. The integration and interpretation of multi-omic data are critical for identifying disease targets and deciphering disease signaling pathways. However, it remains an open problem due to the complex signaling interactions among many proteins. Herein, we propose a multi-scale multi-hop multi-omic network flow model, M3NetFlow, to facilitate both hypothesis-guided and generic multi-omic data analysis tasks. We evaluated M3NetFlow using two independent case studies: (1) uncovering mechanisms of synergy of drug combinations (hypothesis/anchor-target guided multi-omic analysis) and (2) identifying biomarkers of Alzheimer's disease (generic multi-omic analysis). The evaluation and comparison results showed that M3NetFlow achieved the best prediction accuracy and identified a set of drug combination synergy- and disease-associated targets. The model can be directly applied to other multi-omic data-driven studies.

An Integrative Multi-Omics Random Forest Framework for Robust Biomarker Discovery

High-throughput technologies now produce a wide array of omics data, from genomic and transcriptomic profiles to epigenomic and proteomic measurements. Integrating these diverse data types can yield deeper insights into the biological mechanisms driving complex traits and diseases. Yet, extracting key shared biomarkers from multiple data layers remains a major challenge. We present a multivariate random forest (MRF)-based framework enhanced by a novel inverse minimal depth (IMD) metric for integrative variable selection. By assigning response variables to tree nodes and employing IMD to rank predictors, our approach efficiently identifies essential features across different omics types, even when confronted with high-dimensionality and noise. Through extensive simulations and analyses of multi-omics datasets from The Cancer Genome Atlas, we demonstrate that our method outperforms established integrative techniques in uncovering biologically meaningful biomarkers and pathways. Our findings show that selected biomarkers not only correlate with known regulatory and signaling networks but can also stratify patient subgroups with distinct clinical outcomes. The method's scalable, interpretable, and user-friendly implementation ensures broad applicability to a range of research questions. This MRF-based framework advances robust biomarker discovery and integrative multi-omics analyses, accelerating the translation of complex molecular data into tangible biological and clinical insights.

GCBRGCN: Integration of ceRNA and RGCN to Identify Gastric Cancer Biomarkers

Gastric cancer (GC) is a prevalent malignancy, and the discovery of biomarkers plays a crucial role in the diagnosis and prognosis of GC. However, current strategies for identifying GC biomarkers often focus on a single ribonucleic acid (RNA) class, neglecting the potential for multiple RNA types to collectively serve as biomarkers with improved predictive capabilities. To bridge this gap, our study introduces the GC biomarker relation graph convolution neural network (GCBRGCN) model which integrates the competing endogenous RNA (ceRNA) network with GC clinical informations and whole transcriptomics data, leveraging the relational graph convolutional network (RGCN) to predict GC biomarkers. It demonstrates exceptional performance, surpassing traditional machine learning and graph neural network algorithms with an area under the curve (AUC) of 0.8172 in the task of predicting GC biomarkers. Our study identified three unreported potential novel GC biomarkers: CCNG1, CYP1B1, and CITED2. Moreover, FOXC1 and LINC00324 were characterized as biomarkers with significance in both prognosis and diagnosis. Our work offers a novel framework for GC biomarker identification, highlighting the critical role of multiple types RNA interaction in oncological research.

HEK-omics: The promise of omics to optimize HEK293 for recombinant adeno-associated virus (rAAV) gene therapy manufacturing

Gene therapy is poised to transition from niche to mainstream medicine, with recombinant adeno-associated virus (rAAV) as the vector of choice. However, robust, scalable, industrialized production is required to meet demand and provide affordable patient access, which has not yet materialized. Closing the chasm between demand and supply requires innovation in biomanufacturing to achieve the essential step change in rAAV product yield and quality. Omics provides a rich source of mechanistic knowledge that can be applied to HEK293, the most commonly used cell line for rAAV production. In this review, the findings from a growing number of diverse studies that apply genomics, epigenomics, transcriptomics, proteomics, and metabolomics to HEK293 bioproduction are explored. Learnings from CHO-omics, application of omics approaches to improve CHO bioproduction, provide a framework to explore the potential of "HEK-omics" as a multi-omics-informed approach providing actionable mechanistic insights for improved transient and stable production of rAAV and other recombinant products in HEK293.

Heterogeneous Clustering of Multiomics Data for Breast Cancer Subgroup Classification and Detection

The rapid growth of diverse -omics datasets has made multiomics data integration crucial in cancer research. This study adapts the expectation-maximization routine for the joint latent variable modeling of multiomics patient profiles. By combining this approach with traditional biological feature selection methods, this study optimizes latent distribution, enabling efficient patient clustering from well-studied cancer types with reduced computational expense. The proposed optimization subroutines enhance survival analysis and improve runtime performance. This article presents a framework for distinguishing cancer subtypes and identifying potential biomarkers for breast cancer. Key insights into individual subtype expression and function were obtained through differentially expressed gene analysis and pathway enrichment for BRCA patients. The analysis compared 302 tumor samples to 113 normal samples across 60,660 genes. The highly upregulated gene COL10A1, promoting breast cancer progression and poor prognosis, and the consistently downregulated gene CDG300LG, linked to brain metastatic cancer, were identified. Pathway enrichment analysis revealed similarities in cellular matrix organization pathways across subtypes, with notable differences in functions like cell proliferation regulation and endocytosis by host cells. GO Semantic Similarity analysis quantified gene relationships in each subtype, identifying potential biomarkers like MATN2, similar to COL10A1. These insights suggest deeper relationships within clusters and highlight personalized treatment potential based on subtypes.

Synergizing metabolomics and artificial intelligence for advancing precision oncology

Metabolomics has emerged as a transformative tool in precision oncology, with substantial potential for advancing biomarker discovery, monitoring treatment responses, and aiding drug development. Integrating artificial intelligence (AI) into metabolomics optimizes data acquisition and analysis, facilitating the interpretation of complex metabolic networks and enabling more effective multiomics integration. In this opinion, we explore recent advances in the application of metabolomics within precision oncology, emphasizing the unique advantages that AI-driven metabolomics offers. We propose that AI not only complements but also amplifies the potential of current platforms, accelerating research progress and ultimately improving patient outcomes. Finally, we discuss the opportunities and challenges involved in translating AI-driven metabolomics into clinical practice for precision oncology.

Comparative Analysis of Multi-Omics Integration Using Graph Neural Networks for Cancer Classification

Recent studies on integrating multiple omics data highlighted the potential to advance our understanding of the cancer disease process. Computational models based on graph neural networks and attention-based architectures have demonstrated promising results for cancer classification due to their ability to model complex relationships among biological entities. However, challenges related to addressing the high dimensionality and complexity in integrating multi-omics data, as well as in constructing graph structures that effectively capture the interactions between nodes, remain active areas of research. This study evaluates graph neural network architectures for multi-omics (MO) data integration based on graph-convolutional networks (GCN), graph-attention networks (GAT), and graph-transformer networks (GTN). Differential gene expression and LASSO (Least Absolute Shrinkage and Selection Operator) regression are employed for reducing the omics data dimensionality and feature selection; hence, the developed models are referred to as LASSO-MOGCN, LASSO-MOGAT, and LASSO-MOGTN. Graph structures constructed using sample correlation matrices and protein-protein interaction networks are investigated. Experimental validation is performed with a dataset of 8,464 samples from 31 cancer types and normal tissue, comprising messenger-RNA, micro-RNA, and DNA methylation data. The results show that the models integrating multi-omics data outperformed the models trained on single omics data, where LASSO-MOGAT achieved the best overall performance, with an accuracy of 95.9%. The findings also suggest that correlation-based graph structures enhance the models' ability to identify shared cancer-specific signatures across patients in comparison to protein-protein interaction networks-based graph structures. The code and data used in this study are available in the link (https://github.com/FadiAlharbi2024/Graph_Based_Architecture.git).

MOCapsNet: Multiomics Data Integration for Cancer Subtype Analysis Based on Dynamic Self-Attention Learning and Capsule Networks

Background and Objective: With the rapid development of the accumulation of large-scale multiomics data sets, integrating various omics data to provide a thorough study from multiple perspectives can significantly provide stronger support for precise treatment of diseases. However, due to the complexity of multiomics data, issues of feature redundancy and noise often do not receive sufficient attention when processing high-dimensional data. Moreover, simple concatenation strategies may overlook the correlations between different omics data, failing to effectively capture the unique information inherent in multiomics data. Meanwhile, deep neural networks often rely on complex structures and numerous parameters for training and inference, making their internal feature representations and decision-making processes difficult to interpret. Methods: We propose an interpretable multiomics data integration method for cancer subtype classification, named MOCapsNet, based on self-attention and capsule networks. Specifically, the self-attention confidence learning module is implemented to assess the feature information within each omic and to assign weights to the embedded representations of various groups, resulting in more targeted integrated information. Furthermore, the capsule network structure is employed for the final cancer classification task. Results: The model achieved strong performance on both tasks: 87.8% accuracy on the BRCA multiclassification data set and 83.6% accuracy with an AUC of 88.8% on the LGG data set. Conclusions: The proposed framework has undergone extensive testing on omics data sets, consistently proving its effectiveness in integrating multiomics data. It improves classification accuracy while enhancing the interpretability of results by fully utilizing the feature information.

Targeting ferroptosis in prostate cancer management: molecular mechanisms, multidisciplinary strategies and translational perspectives

Prostate cancer (PCa) is a kind of malignant solid tumor commonly observed among males worldwide. The dilemma of increasing incidence with therapeutic resistance has become the leading issue in PCa clinical management. Ferroptosis is a new form of regulatory cell death caused by iron-dependent lipid peroxidation, which has a dual role in PCa evolution and treatment due to the multi-omics cascade of interactions among pathways and environmental stimuli. Hence deciphering the role of ferroptosis in carcinogenesis would provide novel insights and strategies for precision medicine and personalized healthcare against PCa. In this study, the mechanisms of ferroptosis during cancer development were summarized both at the molecular and tumor microenvironment level. Then literature-reported ferroptosis-related signatures in PCa, e.g., genes, non-coding RNAs, metabolites, natural products and drug components, were manually collected and functionally compared as drivers/inducers, suppressors/inhibitors, and biomarkers according to their regulatory patterns in PCa ferroptosis and pathogenesis. The state-of-the-art techniques for ferroptosis-related data integration, knowledge identification, and translational application to PCa theranostics were discussed from a combinative perspective of artificial intelligence-powered modelling and advanced material-oriented therapeutic scheme design. The prospects and challenges in ferroptosis-based PCa researches were finally highlighted to light up future wisdoms for the flourishing of current findings from bench to bedside.

The Society for Immunotherapy of Cancer Perspective on Tissue-Based Technologies for Immuno-Oncology Biomarker Discovery and Application

With immuno-oncology becoming the standard of care for a variety of cancers, identifying biomarkers that reliably classify patient response, resistance, or toxicity becomes the next critical barrier toward improving care. Multiparametric, multi-omics, and computational platforms generating an unprecedented depth of data are poised to usher in the discovery of increasingly robust biomarkers for enhanced patient selection and personalized treatment approaches. Deciding which developing technologies to implement in clinical settings ultimately, applied either alone or in combination, relies on weighing pros and cons, from minimizing patient sampling to maximizing data outputs, and assessing the reproducibility and representativeness of findings, while lessening data fragmentation toward harmonization. These factors are all assessed while taking into consideration the shortest turnaround time. The Society for Immunotherapy of Cancer Biomarkers Committee convened to identify important advances in biomarker technologies and to address advances in biomarker discovery using multiplexed IHC and immunofluorescence, their coupling to single-cell transcriptomics, along with mass spectrometry-based quantitative and spatially resolved proteomics imaging technologies. We summarize key metrics obtained, ease of interpretation, limitations and dependencies, technical improvements, and outward comparisons of these technologies. By highlighting the most interesting recent data contributed by these technologies and by providing ways to improve their outputs, we hope to guide correlative research directions and assist in their evolution toward becoming clinically useful in immuno-oncology.

Intrinsic-dimension analysis for guiding dimensionality reduction and data fusion in multi-omics data processing

Multi-omics data have revolutionized biomedical research by providing a comprehensive understanding of biological systems and the molecular mechanisms of disease development. However, analyzing multi-omics data is challenging due to high dimensionality and limited sample sizes, necessitating proper data-reduction pipelines to ensure reliable analyses. Additionally, its multimodal nature requires effective data-integration pipelines. While several dimensionality reduction and data fusion algorithms have been proposed, crucial aspects are often overlooked. Specifically, the choice of projection space dimension is typically heuristic and uniformly applied across all omics, neglecting the unique high dimension small sample size challenges faced by individual omics. This paper introduces a novel multi-modal dimensionality reduction pipeline tailored to individual views. By leveraging intrinsic dimensionality estimators, we assess the curse-of-dimensionality impact on each view and propose a two-step reduction strategy for significantly affected views, combining feature selection with feature extraction. Compared to traditional uniform reduction pipelines in a crucial and supervised multi-omics analysis setting, our approach shows significant improvement. Additionally, we explore three effective unsupervised multi-omics data fusion methods rooted in the main data fusion strategies to gain insights into their performance under crucial, yet overlooked, settings.

MDMNI-DGD: A novel graph neural network approach for druggable gene discovery based on the integration of multi-omics data and the multi-view network

Accurately predicting druggable genes is of paramount importance for enhancing the efficacy of targeted therapies, reducing drug-related toxicities and improving patients' survival rates. Nevertheless, accurately predicting candidate cancer-druggable genes remains a critical challenge in translational medicine due to the high heterogeneity and complexity of cancer data. In this study, we proposed a novel graph neural approach called Druggable Gene Discovery based on the Integration of Multi-omics Data and the Multi-view Network (MDMNI-DGD), aiming to predict and evaluate cancer-druggable genes. MDMNI-DGD integrated a comprehensive set of multi-omics data, including copy number variations, DNA methylation, somatic mutations, and gene expression profiles. Simultaneously, it constructed the multi-view gene association network based on protein-protein interactions (PPI), protein structural domains, gene co-expression, pathway co-occurrence, gene sequence and gene ontology. Compared to other state-of-the-art approaches, MDMNI-DGD exhibits excellent performance in key evaluation metrics such as AUROC and AUPR. Moreover, the case study has also demonstrated the efficacy of our approach in discovering potentially druggable genes. Among more than 20,000 protein-coding genes, MDMNI-DGD successfully identified 872 potentially druggable genes. The findings from this investigation may serve to bolster the assessment of pan-cancer druggable genes, potentially catalyzing the development of more personalized and efficacious therapeutic interventions.

The Era of Preemptive Medicine: Developing Medical Digital Twins through Omics, IoT, and AI Integration

Preemptive medicine represents a paradigm shift from reactive treatment to proactive disease prevention. The integration of omics technologies, the Internet of Things (IoT), and artificial intelligence (AI) has facilitated the development of personalized, predictive, and preemptive healthcare strategies. Omic technologies, such as genomics, proteomics, and metabolomics, provide comprehensive insights into molecular profile of an individual, revealing potential disease predispositions and health trajectories. IoT devices, such as wearables and smartphones, enable continuous and periodic monitoring of physiological parameters, thus providing a dynamic view of an individual's health status. AI algorithms analyze comprehensive and complex data from omics and IoT technologies to identify patterns and correlations that inform predictive models of disease risk, progression, and response to interventions. Medical digital twins, or virtual replicas of an individual's biological processes, have emerged as the cornerstone of preemptive medicine. The integration of omics, IoT, and AI enables the development of medical digital twins, which in turn allows for precise simulation of human physiological profiles, prediction of future health outcomes, and virtual individual clinical trials, facilitating personalized proactive interventions and preemptive disease control. This review demonstrates the convergence of omics, IoT, and AI in preemptive medicine, highlighting their potential to revolutionize healthcare by enabling early disease detection, personalized treatment strategies, and chronic disease prevention. We show how AI leverages omics and IoT in preemptive medicine through several case studies while also discussing the necessary data for developing medical digital twins and addressing ethical and social aspects that warrant consideration. Medical digital twins signify a fundamental transformation in health management, shifting from treating diseases after their occurrence to controlling them before their occurrence. This approach enhances the effectiveness of medical interventions and improves overall health outcomes, preparing for a healthier future.

G2PDeep-v2: a web-based deep-learning framework for phenotype prediction and biomarker discovery for all organisms using multi-omics data

The G2PDeep-v2 server is a web-based platform powered by deep learning, for phenotype prediction and markers discovery from multi-omics data in any organisms including humans, plants, animals, and viruses. The server provides multiple services for researchers to create deep-learning models through an interactive interface and train these models using an automated hyperparameter tuning algorithm on high-performance computing resources. Users can visualize the results of phenotype and markers predictions and perform Gene Set Enrichment Analysis for the significant markers to provide insights into the molecular mechanisms underlying complex diseases, conditions and other biological phenotypes being studied. The G2PDeep-v2 server is publicly available at https://g2pdeep.org/ and can be utilized for all organisms.

MiRS-HF: A Novel Deep Learning Predictor for Cancer Classification and miRNA Expression Patterns

Cancer classification and biomarker identification are crucial for guiding personalized treatment. To make effective use of miRNA associations and expression data, we have developed a deep learning model for cancer classification and biomarker identification. We propose an approach for cancer classification called MiRNA Selection and Hybrid Fusion (MiRS-HF), which consists of early fusion and intermediate fusion. The early fusion involves applying a Layer Attention Graph Convolutional Network (LAGCN) to a miRNA-disease heterogeneous network, resulting in a miRNA-disease association degree score matrix. The intermediate fusion employs a Graph Convolutional Network (GCN) in the classification tasks, weighting the expression data based on the miRNA-disease association degree score. Furthermore, MiRS-HF can identify the important miRNA biomarkers and their expression patterns. The proposed method demonstrates superior performance in the classification tasks of six cancers compared to other methods. Simultaneously, we incorporated the feature weighting strategy into the comparison algorithm, leading to a significant improvement in the algorithm's results, highlighting the extreme importance of this strategy.

Development and validation of a cross-modality tensor fusion model using multi-modality MRI radiomics features and clinical radiological characteristics for the prediction of microvascular invasion in hepatocellular carcinoma

OBJECTIVES

To develop and validate a cross-modality tensor fusion (CMTF) model using multi-modality MRI radiomics features and clinical radiological characteristics for the prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

This study included 174 HCC patients (47 MVI-positive and 127 MVI-negative) confirmed by postoperative pathology. The synthetic minority over-sampling technique was used to augment MVI-positive samples. The amplified dataset of 254 samples (127 MVI-positive and 127 MVI-negative) was randomly divided into training and test cohorts in a 7:3 ratio. Radiomics features were respectively extracted from arterial phase, delayed phase, diffusion-weighted imaging, and fat-suppressed T2-weighted imaging. The least absolute shrinkage and selection operator was used for feature selection. Univariate and multivariate logistic regression analyses were employed to identify clinical and radiological independent predictors. The selected multi-modality MRI radiomics features, clinical and radiological characteristics were used to construct the CMTF model, single modality (SM) model, early fusion (EF) model.

RESULTS

The CMTF model demonstrated superior performance in predicting MVI compared to the SM and EF models. When integrating four MRI modalities, the CMTF model achieved a high area under the curve (AUC) with 95 % confidence interval (95 % CI) of 0.894 (0.820-0.968). Additionally, incorporating clinical and radiological characteristics further enhanced the predictive performance of CMTF model, the AUC (95 % CI) value increased to 0.945 (0.892-0.998).

CONCLUSION

The CMTF model showed promising performance in preoperative MVI prediction, providing a more effective non-invasive detection tool for HCC patients.

CrossIsoFun: predicting isoform functions using the integration of multi-omics data

MOTIVATION

Isoforms spliced from the same gene may carry distinct biological functions. Therefore, annotating functions at the isoform level provides valuable insights into the functional diversity of genomes. Since experimental approaches for determining isoform functions are time- and cost-demanding, computational methods have been proposed. In this case, multi-omics data integration helps enhance the model performance, providing complementary insights for isoform functions. However, current methods underperform in leveraging diverse omics data, primarily due to the limited power to integrate the heterogeneous feature domains. Besides, among the multi-omics data, isoform-isoform interactions (IIIs) are a key data source, as isoforms interact with each other to perform functions. Unfortunately, IIIs remain largely underutilized in isoform function predictions until now.

RESULTS

We introduce CrossIsoFun, a multi-omics data analysis framework for isoform function prediction. CrossIsoFun combines omics-specific and cross-omics learning for data integration and function prediction. In detail, CrossIsoFun uses a graph convolutional network (GCN) as the omics-specific classifier for each data source. The initial label predictions from GCNs are forwarded to the View Correlation Discovery Network (VCDN) and processed as a cross-omics integrative representation. The representation is then used to produce final predictions of isoform functions. In addition, an antoencoder within a cycle-consistency generative adversarial network (cycleGAN) is designed to generate IIIs from PPIs and thereby enrich the interactomics data. Our method outperforms the state-of-the-art methods on three tissue-naive datasets and 15 tissue-specific datasets with mRNA expression, sequence, and PPI data. The prediction of CrossIsoFun is further validated by its consistency with subcellular localization and isoform-level annotations with literature support.

AVAILABILITY AND IMPLEMENTATION

CrossIsoFun is freely available at https://github.com/genemine/CrossIsoFun.

DeepMoIC: multi-omics data integration via deep graph convolutional networks for cancer subtype classification

BACKGROUND

Achieving precise cancer subtype classification is imperative for effective prognosis and treatment. Multi-omics studies, encompassing diverse data modalities, have emerged as powerful tools for unraveling the complexities of cancer. However, owing to the intricacies of biological data, multi-omics datasets generally show variations in data types, scales, and distributions. These intractable problems lead to challenges in exploring intact representations from heterogeneous data, which often result in inaccuracies in multi-omics information analysis.

RESULTS

To address the challenges of multi-omics research, our approach DeepMoIC presents a novel framework derived from deep Graph Convolutional Network (GCN). Leveraging autoencoder modules, DeepMoIC extracts compact representations from omics data and incorporates a patient similarity network through the similarity network fusion algorithm. To handle non-Euclidean data and explore high-order omics information effectively, we design a Deep GCN module with two strategies: residual connection and identity mapping. With extracted higher-order representations, our approach consistently outperforms state-of-the-art models on a pan-cancer dataset and 3 cancer subtype datasets.

CONCLUSION

The introduction of Deep GCN shows encouraging performance in terms of supervised multi-omics feature learning, offering promising insights for precision medicine in cancer research. DeepMoIC can potentially be an important tool in the field of cancer subtype classification because of its capacity to handle complex multi-omics data and produce reliable classification findings.

DFASGCNS: A prognostic model for ovarian cancer prediction based on dual fusion channels and stacked graph convolution

Ovarian cancer is a malignant tumor with different clinicopathological and molecular characteristics. Due to its nonspecific early symptoms, the majority of patients are diagnosed with local or extensive metastasis, severely affecting treatment and prognosis. The occurrence of ovarian cancer is influenced by multiple complex mechanisms including genomics, transcriptomics, and proteomics. Integrating multiple types of omics data aids in predicting the survival rate of ovarian cancer patients. However, existing methods only fuse multi-omics data at the feature level, neglecting the shared and complementary neighborhood information among samples of multi-omics data, and failing to consider the potential interactions between different omics data at the molecular level. In this paper, we propose a prognostic model for ovarian cancer prediction named Dual Fusion Channels and Stacked Graph Convolutional Neural Network (DFASGCNS). The DFASGCNS utilizes dual fusion channels to learn feature representations of different omics data and the associations between samples. Stacked graph convolutional network is used to comprehensively learn the deep and intricate correlation networks present in multi-omics data, enhancing the model's ability to represent multi-omics data. An attention mechanism is introduced to allocate different weights to important features of different omics data, optimizing the feature representation of multi-omics data. Experimental results demonstrate that compared to existing methods, the DFASGCNS model exhibits significant advantages in ovarian cancer prognosis prediction and survival analysis. Kaplan-Meier curve analysis results indicate significant differences in the survival subgroups predicted by the DFASGCNS model, contributing to a deeper understanding of the pathogenesis of ovarian cancer and providing more reliable auxiliary diagnostic information for the prognosis assessment of ovarian cancer patients.

Multi-view BLUP: a promising solution for post-omics data integrative prediction

Phenotypic prediction is a promising strategy for accelerating plant breeding. Data from multiple sources (called multi-view data) can provide complementary information to characterize a biological object from various aspects. By integrating multi-view information into phenotypic prediction, a multi-view best linear unbiased prediction (MVBLUP) method is proposed in this paper. To measure the importance of multiple data views, the differential evolution algorithm with an early stopping mechanism is used, by which we obtain a multi-view kinship matrix and then incorporate it into the BLUP model for phenotypic prediction. To further illustrate the characteristics of MVBLUP, we perform the empirical experiments on four multi-view datasets in different crops. Compared to the single-view method, the prediction accuracy of the MVBLUP method has improved by 0.038-0.201 on average. The results demonstrate that the MVBLUP is an effective integrative prediction method for multi-view data.

TMODINET: A trustworthy multi-omics dynamic learning integration network for cancer diagnostic

Multiple types of omics data contain a wealth of biomedical information which reflect different aspects of clinical samples. Multi-omics integrated analysis is more likely to lead to more accurate clinical decisions. Existing cancer diagnostic methods based on multi-omics data integration mainly focus on the classification accuracy of the model, while neglecting the interpretability of the internal mechanism and the reliability of the results, which are crucial in specific domains such as precision medicine and the life sciences. To overcome this limitation, we propose a trustworthy multi-omics dynamic learning framework (TMODINET) for cancer diagnostic. The framework employs multi-omics adaptive dynamic learning to process each sample to provide patient-centered personality diagnosis by using self-attentional learning of features and modalities. To characterize the correlation between samples well, we introduce a graph dynamic learning method which can adaptively adjust the graph structure according to the specific classification results for specific graph convolutional networks (GCN) learning. Moreover, we utilize an uncertainty mechanism by employing Dirichlet distribution and Dempster-Shafer theory to obtain uncertainty and integrate multi-omics data at the decision level, ensuring trustworthy for cancer diagnosis. Extensive experiments on four real-world multimodal medical datasets are conducted. Compared to state-of-the-art methods, the superior performance and trustworthiness of our proposed algorithm are clearly validated. Our model has great potential for clinical diagnosis.

Supervised graph contrastive learning for cancer subtype identification through multi-omics data integration

Cancer is one of the most deadly diseases in the world. Accurate cancer subtype classification is critical for patient diagnosis, treatment, and prognosis. Ever-increasing multi-omics data describes the characteristics of the patients from different views and serves as complementary information to promote cancer subtype identification. However, omics data generally have different distributions and high dimensions. How to effectively integrate multiple omics data to classify cancer subtypes accurately is a challenge for researchers. This work proposes a method integrating multi-omics data based on supervised graph contrast learning (MCRGCN) to classify cancer subtypes. The method considers the unique feature distribution of each omics data and the interaction of different omics data features to improve the accuracy of cancer subtype classification. To achieve this, MCRGCN first constructs different sample networks based on the multi-omics data of the samples. Then, it puts the omics data and adjacency matrix of the sample into different residual graph convolution models to get multi-omics features of the samples, which are trained with a supervised comparison loss to maintain that the sample features of each omics should be as consistent as possible. Finally, we input the sample features combining multi-omics features into a classifier to obtain the cancer subtypes. We applied MCRGCN to the invasive breast carcinoma (BRCA) and glioblastoma multiforme (GBM) datasets, integrating gene expression, miRNA expression, and DNA methylation data. The results demonstrate that our model is superior to other methods in integrating multi-omics data. Moreover, the results of survival analysis experiments demonstrate that the cancer subtypes identified by our model have significant clinical features. Furthermore, our model can help to identify potential biomarkers and pathways associated with cancer subtypes.

Emerging trends and hotspots in lung cancer-prediction models research

Objective

In recent years, lung cancer-prediction models have become popular. However, few bibliometric analyses have been performed in this field.

Methods

This study aimed to reveal the scientific output and trends in lung cancer-prediction models from a global perspective. In this study, publications were retrieved and extracted from the Web of Science Core Collection (WoSCC) database. CiteSpace 6.1.R3 and VOSviewer 1.6.18 were used to analyze hotspots and theme trends.

Results

A marked increase in the number of publications related to lung cancer-prediction models was observed. A total of 2711 institutions from in 64 countries/regions published 2139 documents in 566 academic journals. China and the United States were the leading country in the field of lung cancer-prediction models. The institutions represented by Fudan University had significant academic influence in the field. Analysis of keywords revealed that lncRNA, tumor microenvironment, immune, cancer statistics, The Cancer Genome Atlas, nomogram, and machine learning were the current focus of research in lung cancer-prediction models.

Conclusions

Over the last two decades, research on risk-prediction models for lung cancer has attracted increasing attention. Prognosis, machine learning, and multi-omics technologies are both current hotspots and future trends in this field. In the future, in-depth explorations using different omics should increase the sensitivity and accuracy of lung cancer-prediction models and reduce the global burden of lung cancer.

Multi-omics data integration and analysis pipeline for precision medicine: Systematic review

Precision medicine has gained considerable popularity since the "one-size-fits-all" approach did not seem very effective or reflective of the complexity of the human body. Subsequently, since single-omics does not reflect the complexity of the human body's inner workings, it did not result in the expected advancement in the medical field. Therefore, the multi-omics approach has emerged. The multi-omics approach involves integrating data from different omics technologies, such as DNA sequencing, RNA sequencing, mass spectrometry, and others, using computational methods and then analyzing the integrated result for different downstream analysis applications such as survival analysis, cancer classification, or biomarker identification. Most of the recent reviews were constrained to discussing one aspect of the multi-omics analysis pipeline, such as the dimensionality reduction step, the integration methods, or the interpretability aspect; however, very few provide a comprehensive review of every step of the analysis. This study aims to give an overview of the multi-omics analysis pipeline, starting with the most popular multi-omics databases used in recent literature, dimensionality reduction techniques, details the different types of data integration techniques and their downstream analysis applications, describes the most commonly used evaluation metrics, highlights the importance of model interpretability, and lastly discusses the challenges and potential future work for multi-omics data integration in precision medicine.

Cancer molecular subtyping using limited multi-omics data with missingness

Diagnosing cancer subtypes is a prerequisite for precise treatment. Existing multi-omics data fusion-based diagnostic solutions build on the requisite of sufficient samples with complete multi-omics data, which is challenging to obtain in clinical applications. To address the bottleneck of collecting sufficient samples with complete data in clinical applications, we proposed a flexible integrative model (CancerSD) to diagnose cancer subtype using limited samples with incomplete multi-omics data. CancerSD designs contrastive learning tasks and masking-and-reconstruction tasks to reliably impute missing omics, and fuses available omics data with the imputed ones to accurately diagnose cancer subtypes. To address the issue of limited clinical samples, it introduces a category-level contrastive loss to extend the meta-learning framework, effectively transferring knowledge from external datasets to pretrain the diagnostic model. Experiments on benchmark datasets show that CancerSD not only gives accurate diagnosis, but also maintains a high authenticity and good interpretability. In addition, CancerSD identifies important molecular characteristics associated with cancer subtypes, and it defines the Integrated CancerSD Score that can serve as an independent predictive factor for patient prognosis.

Deep Learning of radiology-genomics integration for computational oncology: A mini review

In the field of computational oncology, patient status is often assessed using radiology-genomics, which includes two key technologies and data, such as radiology and genomics. Recent advances in deep learning have facilitated the integration of radiology-genomics data, and even new omics data, significantly improving the robustness and accuracy of clinical predictions. These factors are driving artificial intelligence (AI) closer to practical clinical applications. In particular, deep learning models are crucial in identifying new radiology-genomics biomarkers and therapeutic targets, supported by explainable AI (xAI) methods. This review focuses on recent developments in deep learning for radiology-genomics integration, highlights current challenges, and outlines some research directions for multimodal integration and biomarker discovery of radiology-genomics or radiology-omics that are urgently needed in computational oncology.

DEDUCE: Multi-head attention decoupled contrastive learning to discover cancer subtypes based on multi-omics data

BACKGROUND AND OBJECTIVE

Given the high heterogeneity and clinical diversity of cancer, substantial variations exist in multi-omics data and clinical features across different cancer subtypes.

METHODS

We propose a model, named DEDUCE, based on a symmetric multi-head attention encoders (SMAE), for unsupervised contrastive learning to analyze multi-omics cancer data, with the aim of identifying and characterizing cancer subtypes. This model adopts a unsupervised SMAE that can deeply extract contextual features and long-range dependencies from multi-omics data, thereby mitigating the impact of noise. Importantly, DEDUCE introduces a subtype decoupled contrastive learning method based on a multi-head attention mechanism to simultaneously learn features from multi-omics data and perform clustering for identifying cancer subtypes. Subtypes are clustered by calculating the similarity between samples in both the feature space and sample space of multi-omics data. The fundamental concept involves decoupling various attributes of multi-omics data features and learning them as contrasting terms. A contrastive loss function is constructed to quantify the disparity between positive and negative examples, and the model minimizes this difference, thereby promoting the acquisition of enhanced feature representation.

RESULTS

The DEDUCE model undergoes extensive experiments on simulated multi-omics datasets, single-cell multi-omics datasets, and cancer multi-omics datasets, outperforming 10 deep learning models. The DEDUCE model outperforms state-of-the-art methods, and ablation experiments demonstrate the effectiveness of each module in the DEDUCE model. Finally, we applied the DEDUCE model to identify six cancer subtypes of AML.

CONCLUSION

In this paper, we proposed DEDUCE model learns features from multi-omics data through SMAE, and the subtype decoupled contrastive learning consistently optimizes the model for clustering and identifying cancer subtypes. The DEDUCE model demonstrates a significant capability in discovering new cancer subtypes. We applied the DEDUCE model to identify six subtypes of AML. Through the analysis of GO function enrichment, subtype-specific biological functions, and GSEA of AML using the DEDUCE model, the interpretability of the DEDUCE model in identifying cancer subtypes is further enhanced.

From Omics to Multi-Omics: A Review of Advantages and Tradeoffs

Bioinformatics is a rapidly evolving field charged with cataloging, disseminating, and analyzing biological data. Bioinformatics started with genomics, but while genomics focuses more narrowly on the genes comprising a genome, bioinformatics now encompasses a much broader range of omics technologies. Overcoming barriers of scale and effort that plagued earlier sequencing methods, bioinformatics adopted an ambitious strategy involving high-throughput and highly automated assays. However, as the list of omics technologies continues to grow, the field of bioinformatics has changed in two fundamental ways. Despite enormous success in expanding our understanding of the biological world, the failure of bulk methods to account for biologically important variability among cells of the same or different type has led to a major shift toward single-cell and spatially resolved omics methods, which attempt to disentangle the conflicting signals contained in heterogeneous samples by examining individual cells or cell clusters. The second major shift has been the attempt to integrate two or more different classes of omics data in a single multimodal analysis to identify patterns that bridge biological layers. For example, unraveling the cause of disease may reveal a metabolite deficiency caused by the failure of an enzyme to be phosphorylated because a gene is not expressed due to aberrant methylation as a result of a rare germline variant. Conclusions: There is a fine line between superficial understanding and analysis paralysis, but like a detective novel, multi-omics increasingly provides the clues we need, if only we are able to see them.

OmicsFootPrint: a framework to integrate and interpret multi-omics data using circular images and deep neural networks

The OmicsFootPrint framework addresses the need for advanced multi-omics data analysis methodologies by transforming data into intuitive two-dimensional circular images and facilitating the interpretation of complex diseases. Utilizing deep neural networks and incorporating the SHapley Additive exPlanations algorithm, the framework enhances model interpretability. Tested with The Cancer Genome Atlas data, OmicsFootPrint effectively classified lung and breast cancer subtypes, achieving high area under the curve (AUC) scores-0.98 ± 0.02 for lung cancer subtype differentiation and 0.83 ± 0.07 for breast cancer PAM50 subtypes, and successfully distinguished between invasive lobular and ductal carcinomas in breast cancer, showcasing its robustness. It also demonstrated notable performance in predicting drug responses in cancer cell lines, with a median AUC of 0.74, surpassing nine existing methods. Furthermore, its effectiveness persists even with reduced training sample sizes. OmicsFootPrint marks an enhancement in multi-omics research, offering a novel, efficient and interpretable approach that contributes to a deeper understanding of disease mechanisms.

Exploring molecular mechanisms of postoperative delirium through multi-omics strategies in plasma exosomes

Currently, the diagnosis of delirium is solely based on clinical observation, lacking objective diagnostic tools, and the regulatory networks and pathological mechanisms behind it are not yet fully understood. Exosomes have garnered considerable interest as potential biomarkers for a variety of illnesses. This research aimed to delineate both the proteomic and metabolomic landscapes inherent to exosomes, assessing their diagnostic utility in postoperative delirium (POD) and understanding the underlying pathophysiological frameworks. Integrated analyses of proteomics and metabolomics were conducted on exosomes derived from plasma of individuals from both the non-postoperative delirium (NPOD) control group and the POD group. Subsequently, the study utilized the Connectivity Map (CMap) methodology for the identification of promising small-molecule drugs and carried out molecular docking assessments to explore the binding affinities with the enzyme MMP9 of these identified molecules. We identified significant differences in exosomal metabolites and proteins between the POD and control groups, highlighting pathways related to neuroinflammation and blood-brain barrier (BBB) integrity. Our CMap analysis identified potential small-molecule therapeutics, and molecular docking studies revealed two compounds with high affinity to MMP9, suggesting a new therapeutic avenue for POD. This study highlights MMP9, TLR2, ICAM1, S100B, and glutamate as key biomarkers in the pathophysiology of POD, emphasizing the roles of neuroinflammation and BBB integrity. Notably, molecular docking suggests mirin and orantinib as potential inhibitors targeting MMP9, providing new therapeutic avenues. The findings broaden our understanding of POD mechanisms and suggest targeted strategies for its management, reinforcing the importance of multidimensional biomarker analysis and molecular targeting in POD intervention.

Supervised multiple kernel learning approaches for multi-omics data integration

BACKGROUND

Advances in high-throughput technologies have originated an ever-increasing availability of omics datasets. The integration of multiple heterogeneous data sources is currently an issue for biology and bioinformatics. Multiple kernel learning (MKL) has shown to be a flexible and valid approach to consider the diverse nature of multi-omics inputs, despite being an underused tool in genomic data mining.

RESULTS

We provide novel MKL approaches based on different kernel fusion strategies. To learn from the meta-kernel of input kernels, we adapted unsupervised integration algorithms for supervised tasks with support vector machines. We also tested deep learning architectures for kernel fusion and classification. The results show that MKL-based models can outperform more complex, state-of-the-art, supervised multi-omics integrative approaches.

CONCLUSION

Multiple kernel learning offers a natural framework for predictive models in multi-omics data. It proved to provide a fast and reliable solution that can compete with and outperform more complex architectures. Our results offer a direction for bio-data mining research, biomarker discovery and further development of methods for heterogeneous data integration.

A deep learning feature importance test framework for integrating informative high-dimensional biomarkers to improve disease outcome prediction

Many human diseases result from a complex interplay of behavioral, clinical, and molecular factors. Integrating low-dimensional behavioral and clinical features with high-dimensional molecular profiles can significantly improve disease outcome prediction and diagnosis. However, while some biomarkers are crucial, many lack informative value. To enhance prediction accuracy and understand disease mechanisms, it is essential to integrate relevant features and identify key biomarkers, separating meaningful data from noise and modeling complex associations. To address these challenges, we introduce the High-dimensional Feature Importance Test (HdFIT) framework for machine learning models. HdFIT includes a feature screening step for dimension reduction and leverages machine learning to model complex associations between biomarkers and disease outcomes. It robustly evaluates each feature's impact. Extensive Monte Carlo experiments and a real microbiome study demonstrate HdFIT's efficacy, especially when integrated with advanced models like deep neural networks. Our framework shows significant improvements in identifying crucial features and enhancing prediction accuracy, even in high-dimensional settings.

MORE: a multi-omics data-driven hypergraph integration network for biomedical data classification and biomarker identification

High-throughput sequencing methods have brought about a huge change in omics-based biomedical study. Integrating various omics data is possibly useful for identifying some correlations across data modalities, thus improving our understanding of the underlying biological mechanisms and complexity. Nevertheless, most existing graph-based feature extraction methods overlook the complementary information and correlations across modalities. Moreover, these methods tend to treat the features of each omics modality equally, which contradicts current biological principles. To solve these challenges, we introduce a novel approach for integrating multi-omics data termed Multi-Omics hypeRgraph integration nEtwork (MORE). MORE initially constructs a comprehensive hyperedge group by extensively investigating the informative correlations within and across modalities. Subsequently, the multi-omics hypergraph encoding module is employed to learn the enriched omics-specific information. Afterward, the multi-omics self-attention mechanism is then utilized to adaptatively aggregate valuable correlations across modalities for representation learning and making the final prediction. We assess MORE's performance on datasets characterized by message RNA (mRNA) expression, Deoxyribonucleic Acid (DNA) methylation, and microRNA (miRNA) expression for Alzheimer's disease, invasive breast carcinoma, and glioblastoma. The results from three classification tasks highlight the competitive advantage of MORE in contrast with current state-of-the-art (SOTA) methods. Moreover, the results also show that MORE has the capability to identify a greater variety of disease-related biomarkers compared to existing methods, highlighting its advantages in biomedical data mining and interpretation. Overall, MORE can be investigated as a valuable tool for facilitating multi-omics analysis and novel biomarker discovery. Our code and data can be publicly accessed at https://github.com/Wangyuhanxx/MORE.

Multi-fusion strategy network-guided cancer subtypes discovering based on multi-omics data

Introduction

The combination of next-generation sequencing technology and Cancer Genome Atlas (TCGA) data provides unprecedented opportunities for the discovery of cancer subtypes. Through comprehensive analysis and in-depth analysis of the genomic data of a large number of cancer patients, researchers can more accurately identify different cancer subtypes and reveal their molecular heterogeneity.

Methods

In this paper, we propose the SMMSN (Self-supervised Multi-fusion Strategy Network) model for the discovery of cancer subtypes. SMMSN can not only fuse multi-level data representations of single omics data by Graph Convolutional Network (GCN) and Stacked Autoencoder Network (SAE), but also achieve the organic fusion of multi- -omics data through multiple fusion strategies. In response to the problem of lack label information in multi-omics data, SMMSN propose to use dual self-supervise method to cluster cancer subtypes from the integrated data.

Results

We conducted experiments on three labeled and five unlabeled multi-omics datasets to distinguish potential cancer subtypes. Kaplan Meier survival curves and other results showed that SMMSN can obtain cancer subtypes with significant differences.

Discussion

In the case analysis of Glioblastoma Multiforme (GBM) and Breast Invasive Carcinoma (BIC), we conducted survival time and age distribution analysis, drug response analysis, differential expression analysis, functional enrichment analysis on the predicted cancer subtypes. The research results showed that SMMSN can discover clinically meaningful cancer subtypes.

Machine learning for precision diagnostics of autoimmunity

Early and accurate diagnosis is crucial to prevent disease development and define therapeutic strategies. Due to predominantly unspecific symptoms, diagnosis of autoimmune diseases (AID) is notoriously challenging. Clinical decision support systems (CDSS) are a promising method with the potential to enhance and expedite precise diagnostics by physicians. However, due to the difficulties of integrating and encoding multi-omics data with clinical values, as well as a lack of standardization, such systems are often limited to certain data types. Accordingly, even sophisticated data models fall short when making accurate disease diagnoses and presenting data analyses in a user-friendly form. Therefore, the integration of various data types is not only an opportunity but also a competitive advantage for research and industry. We have developed an integration pipeline to enable the use of machine learning for patient classification based on multi-omics data in combination with clinical values and laboratory results. The application of our framework resulted in up to 96% prediction accuracy of autoimmune diseases with machine learning models. Our results deliver insights into autoimmune disease research and have the potential to be adapted for applications across disease conditions.

GREMI: An Explainable Multi-Omics Integration Framework for Enhanced Disease Prediction and Module Identification

Multi-omics integration has demonstrated promising performance in complex disease prediction. However, existing research typically focuses on maximizing prediction accuracy, while often neglecting the essential task of discovering meaningful biomarkers. This issue is particularly important in biomedicine, as molecules often interact rather than function individually to influence disease outcomes. To this end, we propose a two-phase framework named GREMI to assist multi-omics classification and explanation. In the prediction phase, we propose to improve prediction performance by employing a graph attention architecture on sample-wise co-functional networks to incorporate biomolecular interaction information for enhanced feature representation, followed by the integration of a joint-late mixed strategy and the true-class-probability block to adaptively evaluate classification confidence at both feature and omics levels. In the interpretation phase, we propose a multi-view approach to explain disease outcomes from the interaction module perspective, providing a more intuitive understanding and biomedical rationale. We incorporate Monte Carlo tree search (MCTS) to explore local-view subgraphs and pinpoint modules that highly contribute to disease characterization from the global-view. Extensive experiments demonstrate that the proposed framework outperforms state-of-the-art methods in seven different classification tasks, and our model effectively addresses data mutual interference when the number of omics types increases. We further illustrate the functional- and disease-relevance of the identified modules, as well as validate the classification performance of discovered modules using an independent cohort.

Pre-gating and contextual attention gate - A new fusion method for multi-modal data tasks

Multi-modal representation learning has received significant attention across diverse research domains due to its ability to model a scenario comprehensively. Learning the cross-modal interactions is essential to combining multi-modal data into a joint representation. However, conventional cross-attention mechanisms can produce noisy and non-meaningful values in the absence of useful cross-modal interactions among input features, thereby introducing uncertainty into the feature representation. These factors have the potential to degrade the performance of downstream tasks. This paper introduces a novel Pre-gating and Contextual Attention Gate (PCAG) module for multi-modal learning comprising two gating mechanisms that operate at distinct information processing levels within the deep learning model. The first gate filters out interactions that lack informativeness for the downstream task, while the second gate reduces the uncertainty introduced by the cross-attention module. Experimental results on eight multi-modal classification tasks spanning various domains show that the multi-modal fusion model with PCAG outperforms state-of-the-art multi-modal fusion models. Additionally, we elucidate how PCAG effectively processes cross-modality interactions.

SGUQ: Staged Graph Convolution Neural Network for Alzheimer's Disease Diagnosis using Multi-Omics Data

Alzheimer's disease (AD) is a chronic neurodegenerative disorder and the leading cause of dementia, significantly impacting cost, mortality, and burden worldwide. The advent of high-throughput omics technologies, such as genomics, transcriptomics, proteomics, and epigenomics, has revolutionized the molecular understanding of AD. Conventional AI approaches typically require the completion of all omics data at the outset to achieve optimal AD diagnosis, which are inefficient and may be unnecessary. To reduce the clinical cost and improve the accuracy of AD diagnosis using multi-omics data, we propose a novel staged graph convolutional network with uncertainty quantification (SGUQ). SGUQ begins with mRNA and progressively incorporates DNA methylation and miRNA data only when necessary, reducing overall costs and exposure to harmful tests. Experimental results indicate that 46.23% of the samples can be reliably predicted using only single-modal omics data (mRNA), while an additional 16.04% of the samples can achieve reliable predictions when combining two omics data types (mRNA + DNA methylation). In addition, the proposed staged SGUQ achieved an accuracy of 0.858 on ROSMAP dataset, which outperformed existing methods significantly. The proposed SGUQ can not only be applied to AD diagnosis using multi-omics data, but also has the potential for clinical decision making using multi-viewed data. Our implementation is publicly available at https://github.com/chenzhao2023/multiomicsuncertainty.

mosGraphGen: a novel tool to generate multi-omics signaling graphs to facilitate integrative and interpretable graph AI model development

Motivation

Multi-omics data, i.e. genomics, epigenomics, transcriptomics, proteomics, characterize cellular complex signaling systems from multi-level and multi-view and provide a holistic view of complex cellular signaling pathways. However, it remains challenging to integrate and interpret multi-omics data for mining critical biomarkers. Graph AI models have been widely used to analyze graph-structure datasets, and are ideal for integrative multi-omics data analysis because they can naturally integrate and represent multi-omics data as a biologically meaningful multi-level signaling graph and interpret multi-omics data via graph node and edge ranking analysis. Nevertheless, it is nontrivial for graph-AI model developers to pre-analyze multi-omics data and convert the data into biologically meaningful graphs, which can be directly fed into graph-AI models.

Results

To resolve this challenge, we developed mosGraphGen (multi-omics signaling graph generator), generating Multi-omics Signaling graphs (mos-graph) of individual samples by mapping multi-omics data onto a biologically meaningful multi-level background signaling network with data normalization by aggregating measurements and aligning to the reference genome. With mosGraphGen, AI model developers can directly apply and evaluate their models using these mos-graphs. In the results, mosGraphGen was used and illustrated using two widely used multi-omics datasets of The Cancer Genome Atlas (TCGA) and Alzheimer's disease (AD) samples.

Availability and implementation

The code of mosGraphGen is open-source and publicly available via GitHub: https://github.com/FuhaiLiAiLab/mosGraphGen.

Colon or semicolon: gut sampling microdevices for omics insights

Ingestible microdevices represent a breakthrough in non-invasive sampling of the human gastrointestinal (GI) tract. By capturing the native spatiotemporal microbiome and intricate biochemical gradients, these devices allow a non-invasive multi-omic access to the unperturbed host-microbiota crosstalk, immune/nutritional landscapes and gut-organ connections. We present the current progress of GI sampling microdevices towards personalized metabolism and fostering collaboration among clinicians, engineers, and data scientists.