ArticleJournal of molecular biology2018
Network-Based Disease Module Discovery by a Novel Seed Connector Algorithm with Pathobiological Implications.
Article in Journal of molecular biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Deciphering the Mechanisms of Statin-Ezetimibe Drug Combinations Using Boolean Logical Modeling and Transcriptomic Data.CPT: pharmacometrics & systems pharmacology · 2026Article
- Inferring and evaluating network medicine-based disease modules with nextflow.Bioinformatics (Oxford, England) · 2026Article
- An integrated drug repositioning analysis identifies rosiglitazone as a treatment for sarcopenia.Communications biology · 2026Article
- Article
- Topology-driven negative sampling enhances generalizability in protein-protein interaction prediction.Bioinformatics (Oxford, England) · 2025Article
- Disease gene prioritization with quantum walks.Bioinformatics (Oxford, England) · 2024Article
- Drugst.One - A plug-and-play solution for online systems medicine and network-based drug repurposing.ArXiv · 2023Article
- Network medicine: an approach to complex kidney disease phenotypes.Nature reviews. Nephrology · 2023Review
- Persistent homology analysis of type 2 diabetes genome-wide association studies in protein-protein interaction networks.Frontiers in genetics · 2023Article
- Re-Addressing Dementia by Network Medicine and Mechanism-Based Molecular Endotypes.Journal of Alzheimer's disease : JAD · 2023Article
- TOPAS, a network-based approach to detect disease modules in a top-down fashion.NAR genomics and bioinformatics · 2022Article
- Artificial intelligence in cancer target identification and drug discovery.Signal transduction and targeted therapy · 2022Review
- Protein interaction networks provide insight into fetal origins of chronic obstructive pulmonary disease.Respiratory research · 2022Article
- Efficient link prediction in the protein-protein interaction network using topological information in a generative adversarial network machine learning model.BMC bioinformatics · 2022Article
- Machine learning and network medicine approaches for drug repositioning for COVID-19.Patterns (New York, N.Y.) · 2022Article
- Construction and contextualization approaches for protein-protein interaction networks.Computational and structural biotechnology journal · 2022Review
- MOSES: A New Approach to Integrate Interactome Topology and Functional Features for Disease Gene Prediction.Genes · 2021Article
- Drug repurposing improves disease targeting 11-fold and can be augmented by network module targeting, applied to COVID-19.Scientific reports · 2021Article
- Network medicine in Cardiovascular Research.Cardiovascular research · 2021Review
- Clinical epigenetics settings for cancer and cardiovascular diseases: real-life applications of network medicine at the bedside.Clinical epigenetics · 2021Review
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2 authors.
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Abstract
Understanding the genetic basis of complex diseases is challenging. Prior work shows that disease-related proteins do not typically function in isolation. Rather, they often interact with each other to form a network module that underlies dysfunctional mechanistic pathways. Identifying such disease modules will provide insights into a systems-level understanding of molecular mechanisms of diseases. Owing to the incompleteness of our knowledge of disease proteins and limited information on the biological mediators of pathobiological processes, the key proteins (seed proteins) for many diseases appear scattered over the human protein-protein interactome and form a few small branches, rather than coherent network modules. In this paper, we develop a network-based algorithm, called the Seed Connector algorithm (SCA), to pinpoint disease modules by adding as few additional linking proteins (seed connectors) to the seed protein pool as possible. Such seed connectors are hidden disease module elements that are critical for interpreting the functional context of disease proteins. The SCA aims to connect seed disease proteins so that disease mechanisms and pathways can be decoded based on predicted coherent network modules. We validate the algorithm using a large corpus of 70 complex diseases and binding targets of over 200 drugs, and demonstrate the biological relevance of the seed connectors. Lastly, as a specific proof of concept, we apply the SCA to a set of seed proteins for coronary artery disease derived from a meta-analysis of large-scale genome-wide association studies and obtain a coronary artery disease module enriched with important disease-related signaling pathways and drug targets not previously recognized.
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