NetCAD: a network analysis tool for coronary artery disease-associated PPI network

Explainable coronary artery disease prediction model based on AutoGluon from AutoML framework

Objective

This study focuses on the innovative application of Automated Machine Learning (AutoML) technology in cardiovascular medicine to construct an explainable Coronary Artery Disease (CAD) prediction model to support the clinical diagnosis of CAD.

Methods

This study utilizes a combined data set of five public data sets related to CAD. An ensemble model is constructed using the AutoML open-source framework AutoGluon to evaluate the feasibility of AutoML in constructing a disease prediction model in cardiovascular medicine. The performance of the ensemble model is compared against individual baseline models. Finally, the disease prediction ensemble model is explained using SHapley Additive exPlanations (SHAP).

Results

The experimental results show that the AutoGluon-based ensemble model performs better than the individual baseline models in predicting CAD. It achieved an accuracy of 0.9167 and an AUC of 0.9562 in 4-fold cross-bagging. SHAP measures the importance of each feature to the prediction of the model and explains the prediction results of the model.

Conclusion

This study demonstrates the feasibility and efficacy of AutoML technology in cardiovascular medicine and highlights its potential in disease prediction. AutoML reduces the barriers to model building and significantly improves prediction accuracy. Additionally, the integration of SHAP enhances model transparency and explainability, which is critical to ensuring model credibility and widespread adoption in cardiovascular medicine.

piRNAs as emerging biomarkers and physiological regulatory molecules in cardiovascular disease

Cardiovascular diseases (CVD) represent one of the most considerable global health threats, owing to their high incidence and mortality rates. Despite the ongoing advancements in detection, prevention, treatment, and prognosis of CVD, which have resulted in a decline in both incidence and mortality rates, CVD remains a major public health concern. Therefore, novel diagnostic biomarkers and therapeutic interventions are imperative to minimise the risk of CVD. Non-coding RNAs (ncRNAs) have recently gained increasing attention, with PIWI-interacting RNAs (piRNAs) emerging as a class of small ncRNAs traditionally recognised for their role in silencing transposons within cells. Although the functional roles of PIWI proteins and piRNAs in human cells remain unclear, growing evidence suggests that these molecules are gradually becoming valuable biomarkers for the diagnosis and treatment of CVD. This review provides a comprehensive summary of the latest studies on piRNAs in CVD. This review discusses the roles of piRNAs in various cardiovascular subtypes, including myocardial hypertrophy, heart failure, myocardial infarction, and cardiac regeneration. The perceived insights may contribute novel perspectives for the diagnosis and treatment of CVD.

Artificial intelligence in cancer target identification and drug discovery

Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.

Potential biomarkers of acute myocardial infarction based on weighted gene co-expression network analysis

BACKGROUND

Acute myocardial infarction (AMI) is the common cause of mortality in developed countries. The feasibility of whole-genome gene expression analysis to identify outcome-related genes and dysregulated pathways remains unknown. Molecular marker such as BNP, CRP and other serum inflammatory markers have got the notice at this point. However, these biomarkers exhibit elevated levels in patients with thyroid disease, renal failure and congestive heart failure. In this study, three groups of microarray data sets (GES66360, GSE48060, GSE29532) were collected from GEO, a total of 99, 52 and 55 samples, respectively. Weighted gene co-expression network analysis (WGCNA) was performed to obtain a classifier which composed of related genes that best characterize the AMI.

RESULTS

Here, this study obtained three groups of microarray data sets (GES66360, GSE48060, GSE29532) on AMI blood samples, a total of 99, 52 and 24 samples, respectively. In all, 4672 genes, 3185 genes, 3660 genes were identified in GSE66360, GSE48060, GSE60993 modules, respectively. We preformed WGCNA, GO and KEGG pathway enrichment analysis on these three data sets, finding function enrichment of the differential expression gene on inflammation and immune response. Transcriptome analysis were performed in AMI patients at four time points compared to CAD patients with no history of MI, to determine gene expression profiles and their possible changes during the recovery from myocardial infarction.

CONCLUSIONS

The results suggested that three overlapping genes (FGFBP2, GFOD1 and MLC1) between two modules could be a potential use of gene biomarkers for the diagnose of AMI.

An integrated framework for identification of effective and synergistic anti-cancer drug combinations

Combination drug therapy is considered a better treatment option for various diseases, such as cancer, HIV, hypertension, and infections as compared to targeted drug therapies. Combination or synergism helps to overcome drug resistance, reduction in drug toxicity and dosage. Considering the complexity and heterogeneity among cancer types, drug combination provides promising treatment strategy. Increase in drug combination data raises a challenge for developing a computational approach that can effectively predict drugs synergism. There is a need to model the combination drug screening data to predict new synergistic drug combinations for successful cancer treatment. In such a scenario, machine learning approaches can be used to alleviate the process of drugs synergy prediction. Experimental data from a single-agent or multi-agent drug screens provides feature data for model training. On the contrary, identification of effective drug combination using clinical trials is a time consuming and resource intensive task. This paper attempts to address the aforementioned challenges by developing a computational approach to effectively predict drug synergy. Single-drug efficacy is used for predicting drug synergism. Our approach obviates the need to understand the underlying drug mechanism to predict drug combination synergy. For this purpose, nine machine learning algorithms are trained. It is observed that the Random forest models, in comparison to other models, have shown significant performance. The <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>K</mml:mi></mml:math> -fold cross-validation is performed to evaluate the robustness of the best predictive model. The proposed approach is applied to mutant-BRAF melanoma and further validated using melanoma cell-lines from AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge dataset.

TarNet: An Evidence-Based Database for Natural Medicine Research

Background

Complex diseases seriously threaten human health. Drug discovery approaches based on “single genes, single drugs, and single targets” are limited in targeting complex diseases. The development of new multicomponent drugs for complex diseases is imperative, and the establishment of a suitable solution for drug group-target protein network analysis is a key scientific problem that must be addressed. Herbal medicines have formed the basis of sophisticated systems of traditional medicine and have given rise to some key drugs that remain in use today. The search for new molecules is currently taking a different route, whereby scientific principles of ethnobotany and ethnopharmacognosy are being used by chemists in the discovery of different sources and classes of compounds.

Results

In this study, we developed TarNet, a manually curated database and platform of traditional medicinal plants with natural compounds that includes potential bio-target information. We gathered information on proteins that are related to or affected by medicinal plant ingredients and data on protein–protein interactions (PPIs). TarNet includes in-depth information on both plant–compound–protein relationships and PPIs. Additionally, TarNet can provide researchers with network construction analyses of biological pathways and protein–protein interactions (PPIs) associated with specific diseases. Researchers can upload a gene or protein list mapped to our PPI database that has been manually curated to generate relevant networks. Multiple functions are accessible for network topological calculations, subnetwork analyses, pathway analyses, and compound–protein relationships.

Conclusions

TarNet will serve as a useful analytical tool that will provide information on medicinal plant compound-affected proteins (potential targets) and system-level analyses for systems biology and network pharmacology researchers. TarNet is freely available at http://www.herbbol.org:8001/tarnet, and detailed tutorials on the program are also available.

HRGRN: A Graph Search-Empowered Integrative Database of Arabidopsis Signaling Transduction, Metabolism and Gene Regulation Networks

The biological networks controlling plant signal transduction, metabolism and gene regulation are composed of not only tens of thousands of genes, compounds, proteins and RNAs but also the complicated interactions and co-ordination among them. These networks play critical roles in many fundamental mechanisms, such as plant growth, development and environmental response. Although much is known about these complex interactions, the knowledge and data are currently scattered throughout the published literature, publicly available high-throughput data sets and third-party databases. Many 'unknown' yet important interactions among genes need to be mined and established through extensive computational analysis. However, exploring these complex biological interactions at the network level from existing heterogeneous resources remains challenging and time-consuming for biologists. Here, we introduce HRGRN, a graph search-empowered integrative database of Arabidopsis signal transduction, metabolism and gene regulatory networks. HRGRN utilizes Neo4j, which is a highly scalable graph database management system, to host large-scale biological interactions among genes, proteins, compounds and small RNAs that were either validated experimentally or predicted computationally. The associated biological pathway information was also specially marked for the interactions that are involved in the pathway to facilitate the investigation of cross-talk between pathways. Furthermore, HRGRN integrates a series of graph path search algorithms to discover novel relationships among genes, compounds, RNAs and even pathways from heterogeneous biological interaction data that could be missed by traditional SQL database search methods. Users can also build subnetworks based on known interactions. The outcomes are visualized with rich text, figures and interactive network graphs on web pages. The HRGRN database is freely available at http://plantgrn.noble.org/hrgrn/.

Integrated Genomic and Network-Based Analyses of Complex Diseases and Human Disease Network

A disease phenotype generally reflects various pathobiological processes that interact in a complex network. The highly interconnected nature of the human protein interaction network (interactome) indicates that, at the molecular level, it is difficult to consider diseases as being independent of one another. Recently, genome-wide molecular measurements, data mining and bioinformatics approaches have provided the means to explore human diseases from a molecular basis. The exploration of diseases and a system of disease relationships based on the integration of genome-wide molecular data with the human interactome could offer a powerful perspective for understanding the molecular architecture of diseases. Recently, subnetwork markers have proven to be more robust and reliable than individual biomarker genes selected based on gene expression profiles alone, and achieve higher accuracy in disease classification. We have applied one of these methodologies to idiopathic dilated cardiomyopathy (IDCM) data that we have generated using a microarray and identified significant subnetworks associated with the disease. In this paper, we review the recent endeavours in this direction, and summarize the existing methodologies and computational tools for network-based analysis of complex diseases and molecular relationships among apparently different disorders and human disease network. We also discuss the future research trends and topics of this promising field.

Systematic synergy modeling: understanding drug synergy from a systems biology perspective

Owing to drug synergy effects, drug combinations have become a new trend in combating complex diseases like cancer, HIV and cardiovascular diseases. However, conventional synergy quantification methods often depend on experimental dose–response data which are quite resource-demanding. In addition, these methods are unable to interpret the explicit synergy mechanism. In this review, we give representative examples of how systems biology modeling offers strategies toward better understanding of drug synergy, including the protein-protein interaction (PPI) network-based methods, pathway dynamic simulations, synergy network motif recognitions, integrative drug feature calculations, and “omic”-supported analyses. Although partially successful in drug synergy exploration and interpretation, more efforts should be put on a holistic understanding of drug-disease interactions, considering integrative pharmacology and toxicology factors. With a comprehensive and deep insight into the mechanism of drug synergy, systems biology opens a novel avenue for rational design of effective drug combinations.

Using systems biology approaches to understand cardiac inflammation and extracellular matrix remodeling in the setting of myocardial infarction

Inflammation and extracellular matrix (ECM) remodeling are important components regulating the response of the left ventricle to myocardial infarction (MI). Significant cellular- and molecular-level contributors can be identified by analyzing data acquired through high-throughput genomic and proteomic technologies that provide expression levels for thousands of genes and proteins. Large-scale data provide both temporal and spatial information that need to be analyzed and interpreted using systems biology approaches in order to integrate this information into dynamic models that predict and explain mechanisms of cardiac healing post-MI. In this review, we summarize the systems biology approaches needed to computationally simulate post-MI remodeling, including data acquisition, data analysis for biomarker classification and identification, data integration to build dynamic models, and data interpretation for biological functions. An example for applying a systems biology approach to ECM remodeling is presented as a reference illustration.

Bias tradeoffs in the creation and analysis of protein-protein interaction networks

Networks constructed from aggregated protein-protein interaction data are commonplace in biology. But the studies these data are derived from were conducted with their own hypotheses and foci. Focusing on data from budding yeast present in BioGRID, we determine that many of the downstream signals present in network data are significantly impacted by biases in the original data. We determine the degree to which selection bias in favor of biologically interesting bait proteins goes down with study size, while we also find that promiscuity in prey contributes more substantially in larger studies. We analyze interaction studies over time with respect to data in the Gene Ontology and find that reproducibly observed interactions are less likely to favor multifunctional proteins. We find that strong alignment between co-expression and protein-protein interaction data occurs only for extreme co-expression values, and use this data to suggest candidates for targets likely to reveal novel biology in follow-up studies.

BIOLOGICAL SIGNIFICANCE

Protein-protein interaction data finds particularly heavy use in the interpretation of disease-causal variants. In principle, network data allows researchers to find novel commonalities among candidate genes. In this study, we detail several of the most salient biases contributing to aggregated protein-protein interaction databases. We find strong evidence for the role of selection and laboratory biases. Many of these effects contribute to the commonalities researchers find for disease genes. In order for characterization of disease genes and their interactions to not simply be an artifact of researcher preference, it is imperative to identify data biases explicitly. Based on this, we also suggest ways to move forward in producing candidates less influenced by prior knowledge. This article is part of a Special Issue entitled: Can Proteomics Fill the Gap Between Genomics and Phenotypes?

Sharp bounds and normalization of Wiener-type indices

Complex networks abound in physical, biological and social sciences. Quantifying a network's topological structure facilitates network exploration and analysis, and network comparison, clustering and classification. A number of Wiener type indices have recently been incorporated as distance-based descriptors of complex networks, such as the R package QuACN. Wiener type indices are known to depend both on the network's number of nodes and topology. To apply these indices to measure similarity of networks of different numbers of nodes, normalization of these indices is needed to correct the effect of the number of nodes in a network. This paper aims to fill this gap. Moreover, we introduce an f-Wiener index of network G, denoted by Wf(G). This notion generalizes the Wiener index to a very wide class of Wiener type indices including all known Wiener type indices. We identify the maximum and minimum of Wf(G) over a set of networks with n nodes. We then introduce our normalized-version of f-Wiener index. The normalized f-Wiener indices were demonstrated, in a number of experiments, to improve significantly the hierarchical clustering over the non-normalized counterparts.

Effects of common polymorphism rs11614913 in Hsa-miR-196a2 on lung cancer risk

Background

Emerging evidence suggests that single nucleotide polymorphisms (SNPs) in microRNA-coding genes may participate in the pathogenesis of lung cancer by altering the expression of tumor-related microRNAs. Several studies were investigated in recent years to evaluate the association between hsa-miR-196a2 rs11614913 polymorphism and increased/decreased lung cancer risk. In the present study, we performed a meta-analysis to systematically summarize the possible association.

Methodology/Principal Findings

We performed a meta-analysis of 4 case-control studies that included 2219 lung-cancer cases and 2232 cancer-free controls. We evaluated the strength of the association using odds ratios (ORs) with 95% confidence intervals (CIs). In the overall analysis, it was found that the rs11614913 polymorphism significantly elevated the risk of lung cancer (CC versus (vs.) TT OR = 1.26, 95% CI 1.07–1.49, P = 0.007; CC/CT vs. TT: OR = 1.13, 95% CI 0.98–1.29, P = 0.007; C vs. T: OR = 1.12, 95% CI 1.03–1.22, P = 0.008). In the subgroup analysis by ethnicity, statistically significantly increased cancer risk was found among Asians (CC vs. TT: OR = 1.30, 95% CI 1.10–1.54, P = 0.003; CT vs. TT: OR = 1.16, 95% CI 1.01–1.34, P = 0.039; CC vs. CT/TT: OR = 1.21, 95% CI 1.04–1.41, P = 0.012; C vs. T: OR = 1.14, 95% CI 1.05–1.25, P = 0.002). For Europeans, a significant association with lung cancer risk was found in recessive model (CC vs. CT/TT: OR = 0.63, 95% CI 0.40–0.98, P = 0.040). No publication bias was found in this study.

Conclusions/Significance

Our meta-analysis suggests that the rs11614913 polymorphism is significant associated with the increased risk of lung cancer, especially in Asians. Besides, the C allele of rs11614913 polymorphism may contribute to increased lung cancer risk.