ArticlePLoS computational biology2017
Vicus: Exploiting local structures to improve network-based analysis of biological data.
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Metabolite-disease interaction prediction based on logistic matrix factorization and local neighborhood constraints.Frontiers in psychiatry · 2023Article
- An Inductive Logistic Matrix Factorization Model for Predicting Drug-Metabolite Association With Vicus Regularization.Frontiers in microbiology · 2021Article
- Microbiome Data Analysis by Symmetric Non-negative Matrix Factorization With Local and Global Regularization.Frontiers in molecular biosciences · 2021Article
- Enhanced Integrated Gradients: improving interpretability of deep learning models using splicing codes as a case study.Genome biology · 2020Article
- Constructing knowledge graphs and their biomedical applications.Computational and structural biotechnology journal · 2020Review
- To Embed or Not: Network Embedding as a Paradigm in Computational Biology.Frontiers in genetics · 2019Review
- Transient Cognitive Impairment in Epilepsy.Frontiers in molecular neuroscience · 2018Article
- Article
- Network approaches and applications in biology.PLoS computational biology · 2017Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biological networks entail important topological features and patterns critical to understanding interactions within complicated biological systems. Despite a great progress in understanding their structure, much more can be done to improve our inference and network analysis. Spectral methods play a key role in many network-based applications. Fundamental to spectral methods is the Laplacian, a matrix that captures the global structure of the network. Unfortunately, the Laplacian does not take into account intricacies of the network's local structure and is sensitive to noise in the network. These two properties are fundamental to biological networks and cannot be ignored. We propose an alternative matrix Vicus. The Vicus matrix captures the local neighborhood structure of the network and thus is more effective at modeling biological interactions. We demonstrate the advantages of Vicus in the context of spectral methods by extensive empirical benchmarking on tasks such as single cell dimensionality reduction, protein module discovery and ranking genes for cancer subtyping. Our experiments show that using Vicus, spectral methods result in more accurate and robust performance in all of these tasks.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.