A heterogeneous information network learning model with neighborhood-level structural representation for predicting lncRNA-miRNA interactions

micRoclean: an R package for decontaminating low-biomass 16S-rRNA microbiome data

In 16S-rRNA microbiome studies, cross-contamination and environmental contamination can obscure true biological signal. This contamination is particularly problematic in low-biomass studies, which are characterized by samples with a small amount of microbial DNA. Although multiple methods and packages for decontaminating microbiome data exist, there is no consensus on the most appropriate tool for decontamination based on the individual research study design and how to quantify the impact of removing identified contaminants to avoid over-filtering. To address these gaps, we introduce micRoclean, an open-source R package that contains two distinct microbiome decontamination pipelines with guidance on which to select based on the downstream goals of the research study and study design. This package integrates and expands on existing packages for microbiome decontamination and analysis for convenience of users. Furthermore, micRoclean also implements a filtering loss statistic to quantify the impact of decontamination on the overall covariance structure of the data. In this paper, we demonstrate the utility of micRoclean through implementation on example data, illustrating that micRoclean effectively and intuitively decontaminates microbiome data. Further, we demonstrate through a multi-batch simulated microbiome sample that micRoclean matches or outperforms tools with similar objectives. This package is freely available from GitHub repository rachelgriffard/micRoclean.

Urinary based biomarkers identification and genetic profiling in Parkinson's disease: a systematic review of metabolomic studies

Background

Parkinson's disease is a complex, age-related, neurodegenerative disease associated with dopamine deficiency and both motor and nonmotor deficits. Therapeutic pathways remain challenging in Parkinson's disease due to the low accuracy of early diagnosis, the difficulty in monitoring disease progression, and the limited availability of treatment options.

Objectives

Few data are present to identify urinary biomarkers for various ailments, potentially aiding in the diagnosis and tracking of illness progression in individuals with Parkinson's disease. Thus, the analysis of urinary metabolomic biomarkers (UMB) for early and mid-stage idiopathic Parkinson's disease (IPD) is the main goal of this systematic review.

Methods

For this study, six electronic databases were searched for articles published up to 23 February 2024: PubMed, Ovid Medline, Embase, Scopus, Science Direct, and Cochrane. 5,377 articles were found and 40 articles were screened as per the eligibility criteria. Out of these, 7 controlled studies were selected for this review. Genetic profiling for gene function and biomarker interactions between urinary biomarkers was conducted using the STRING and Cytoscape database.

Results

A total of 40 metabolites were identified to be related to the early and mid-stage of the disease pathology out of which three metabolites, acetyl phenylalanine (a subtype of phenylalanine), tyrosine and kynurenine were common and most significant in three studies. These metabolites cause impaired dopamine synthesis along with mitochondrial disturbances and brain energy metabolic disturbances which are considered responsible for neurodegenerative disorders. Furoglycine, Cortisol, Hydroxyphenylacetic acid, Glycine, Tiglyglycine, Aminobutyric acid, Hydroxyprogesterone, Phenylacetylglutamine, and Dihydrocortisol were also found commonly dysregulated in two of the total 7 studies. 158 genes were found which are responsible for the occurrence of PD and metabolic regulation of the corresponding biomarkers from our study.

Conclusion

The current review identified acetyl phenylalanine (a subtype of phenylalanine), tyrosine and kynurenine as potential urinary metabolomic biomarkers for diagnosing PD and identifying disease progression.

Deciphering spatial domains from spatially resolved transcriptomics through spatially regularized deep graph networks

BACKGROUND

Recent advancements in spatially resolved transcriptomics (SRT) have opened up unprecedented opportunities to explore gene expression patterns within spatial contexts. Deciphering spatial domains is a critical task in spatial transcriptomic data analysis, aiding in the elucidation of tissue structural heterogeneity and biological functions. However, existing spatial domain detection methods ignore the consistency of expression patterns and spatial arrangements between spots, as well as the severe gene dropout phenomenon present in SRT data, resulting in suboptimal performance in identifying tissue spatial heterogeneity.

RESULTS

In this paper, we introduce a novel framework, spatially regularized deep graph networks (SR-DGN), which integrates gene expression profiles with spatial information to learn spatially-consistent and informative spot representations. Specifically, SR-DGN employs graph attention networks (GAT) to adaptively aggregate gene expression information from neighboring spots, considering local expression patterns between spots. In addition, the spatial regularization constraint ensures the consistency of neighborhood relationships between physical and embedded spaces in an end-to-end manner. SR-DGN also employs cross-entropy (CE) loss to model gene expression states, effectively mitigating the impact of noisy gene dropouts.

CONCLUSIONS

Experimental results demonstrate that SR-DGN outperforms state-of-the-art methods in spatial domain identification across SRT data from different sequencing platforms. Moreover, SR-DGN is capable of recovering known microanatomical structures, yielding clearer low-dimensional visualizations and more accurate spatial trajectory inferences.

Sex differences and testosterone interfere with the structure of the gut microbiota through the bile acid signaling pathway

Background

The gut microbiome has a significant impact on human wellness, contributing to the emergence and progression of a range of health issues including inflammatory and autoimmune conditions, metabolic disorders, cardiovascular problems, and psychiatric disorders. Notably, clinical observations have revealed that these illnesses can display differences in incidence and presentation between genders. The present study aimed to evaluate whether the composition of gut microbiota is associated with sex-specific differences and to elucidate the mechanism.

Methods

16S-rRNA-sequencing technology, hormone analysis, gut microbiota transplantation, gonadectomy, and hormone treatment were employed to investigate the correlation between the gut microbiome and sex or sex hormones. Meanwhile, genes and proteins involved bile acid signaling pathway were analyzed both in the liver and ileum tissues.

Results

The composition and diversity of the microbiota from the jejunum and feces and the level of sex hormones in the serum differed between the sexes in young and middle-aged Sprague Dawley (SD) rats. However, no similar phenomenon was found in geriatric rats. Interestingly, whether in young, middle-aged, or old rats, the composition of the microbiota and bacterial diversity differed between the jejunum and feces in rats. Gut microbiota transplantation, gonadectomy, and hormone replacement also suggested that hormones, particularly testosterone (T), influenced the composition of the gut microbiota in rats. Meanwhile, the mRNA and protein level of genes involved bile acid signaling pathway (specifically SHP, FXR, CYP7A1, and ASBT) exhibited gender-specific differences, and T may play a significant role in mediating the expression of this pathway.

Conclusion

Sex-specific differences in the structure of the gut microbiota are mediated by T through the bile acid signaling pathway, pointing to potential targets for disease prevention and management techniques by indicating that sex differences and T levels may alter the composition of the gut microbiota via the bile acid signaling pathway.

A multi-task prediction method based on neighborhood structure embedding and signed graph representation learning to infer the relationship between circRNA, miRNA, and cancer

MOTIVATION

Research shows that competing endogenous RNA is widely involved in gene regulation in cells, and identifying the association between circular RNA (circRNA), microRNA (miRNA), and cancer can provide new hope for disease diagnosis, treatment, and prognosis. However, affected by reductionism, previous studies regarded the prediction of circRNA-miRNA interaction, circRNA-cancer association, and miRNA-cancer association as separate studies. Currently, few models are capable of simultaneously predicting these three associations.

RESULTS

Inspired by holism, we propose a multi-task prediction method based on neighborhood structure embedding and signed graph representation learning, CMCSG, to infer the relationship between circRNA, miRNA, and cancer. Our method aims to extract feature descriptors of all molecules from the circRNA-miRNA-cancer regulatory network using known types of association information to predict unknown types of molecular associations. Specifically, we first constructed the circRNA-miRNA-cancer association network (CMCN), which is constructed based on the experimentally verified biomedical entity regulatory network; next, we combine topological structure embedding methods to extract feature representations in CMCN from local and global perspectives, and use denoising autoencoder for enhancement; then, combined with balance theory and state theory, molecular features are extracted from the point of social relations through the propagation and aggregation of signed graph attention network; finally, the GBDT classifier is used to predict the association of molecules. The results show that CMCSG can effectively predict the relationship between circRNA, miRNA, and cancer. Additionally, the case studies also demonstrate that CMCSG is capable of accurately identifying biomarkers across various types of cancer. The data and source code can be found at https://github.com/1axin/CMCSG.

A multichannel graph neural network based on multisimilarity modality hypergraph contrastive learning for predicting unknown types of cancer biomarkers

Identifying potential cancer biomarkers is a key task in biomedical research, providing a promising avenue for the diagnosis and treatment of human tumors and cancers. In recent years, several machine learning-based RNA-disease association prediction techniques have emerged. However, they primarily focus on modeling relationships of a single type, overlooking the importance of gaining insights into molecular behaviors from a complete regulatory network perspective and discovering biomarkers of unknown types. Furthermore, effectively handling local and global topological structural information of nodes in biological molecular regulatory graphs remains a challenge to improving biomarker prediction performance. To address these limitations, we propose a multichannel graph neural network based on multisimilarity modality hypergraph contrastive learning (MML-MGNN) for predicting unknown types of cancer biomarkers. MML-MGNN leverages multisimilarity modality hypergraph contrastive learning to delve into local associations in the regulatory network, learning diverse insights into the topological structures of multiple types of similarities, and then globally modeling the multisimilarity modalities through a multichannel graph autoencoder. By combining representations obtained from local-level associations and global-level regulatory graphs, MML-MGNN can acquire molecular feature descriptors benefiting from multitype association properties and the complete regulatory network. Experimental results on predicting three different types of cancer biomarkers demonstrate the outstanding performance of MML-MGNN. Furthermore, a case study on gastric cancer underscores the outstanding ability of MML-MGNN to gain deeper insights into molecular mechanisms in regulatory networks and prominent potential in cancer biomarker prediction.

Collaborative weighting in federated graph neural networks for disease classification with the human-in-the-loop

The authors introduce a novel framework that integrates federated learning with Graph Neural Networks (GNNs) to classify diseases, incorporating Human-in-the-Loop methodologies. This advanced framework innovatively employs collaborative voting mechanisms on subgraphs within a Protein-Protein Interaction (PPI) network, situated in a federated ensemble-based deep learning context. This methodological approach marks a significant stride in the development of explainable and privacy-aware Artificial Intelligence, significantly contributing to the progression of personalized digital medicine in a responsible and transparent manner.

A deep convolutional neural network approach using medical image classification

The epidemic diseases such as COVID-19 are rapidly spreading all around the world. The diagnosis of epidemic at initial stage is of high importance to provide medical care to and recovery of infected people as well as protecting the uninfected population. In this paper, an automatic COVID-19 detection model using respiratory sound and medical image based on internet of health things (IoHT) is proposed. In this model, primarily to screen those people having suspected Coronavirus disease, the sound of coughing used to detect healthy people and those suffering from COVID-19, which finally obtained an accuracy of 94.999%. This approach not only expedites diagnosis and enhances accuracy but also facilitates swift screening in public places using simple equipment. Then, in the second step, in order to help radiologists to interpret medical images as best as possible, we use three pre-trained convolutional neural network models InceptionResNetV2, InceptionV3 and EfficientNetB4 and two data sets of chest radiology medical images, and CT Scan in a three-class classification. Utilizing transfer learning and pre-existing knowledge in these models leads to notable improvements in disease diagnosis and identification compared to traditional techniques. Finally, the best result obtained for CT-Scan images belonging to InceptionResNetV2 architecture with 99.414% accuracy and for radiology images related to InceptionV3 and EfficientNetB4 architectures with the accuracy is 96.943%. Therefore, the proposed model can help radiology specialists to confirm the initial assessments of the COVID-19 disease.