ArticleBriefings in bioinformatics2014
Integrative approaches for predicting protein function and prioritizing genes for complex phenotypes using protein interaction networks.
Article in Briefings in bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
10 citing papers in PubMed.
- Optimizing network propagation for multi-omics data integration.PLoS computational biology · 2021Article
- Article
- Article
- Incorporating interaction networks into the determination of functionally related hit genes in genomic experiments with Markov random fields.Bioinformatics (Oxford, England) · 2017Article
- HAPPI-2: a Comprehensive and High-quality Map of Human Annotated and Predicted Protein Interactions.BMC genomics · 2017Article
- Methods for biological data integration: perspectives and challenges.Journal of the Royal Society, Interface · 2015Review
- Network analysis of gene essentiality in functional genomics experiments.Genome biology · 2015Article
- Quo vadis computational analysis of PPI data or why the future isn't here yet.Frontiers in genetics · 2015Article
- Network tuned multiple rank aggregation and applications to gene ranking.BMC bioinformatics · 2015Article
- Dynamic modularity of host protein interaction networks in Salmonella Typhi infection.PloS one · 2014Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
With the rapid development of biotechnologies, many types of biological data including molecular networks are now available. However, to obtain a more complete understanding of a biological system, the integration of molecular networks with other data, such as molecular sequences, protein domains and gene expression profiles, is needed. A key to the use of networks in biological studies is the definition of similarity among proteins over the networks. Here, we review applications of similarity measures over networks with a special focus on the following four problems: (i) predicting protein functions, (ii) prioritizing genes related to a phenotype given a set of seed genes that have been shown to be related to the phenotype, (iii) prioritizing genes related to a phenotype by integrating gene expression profiles and networks and (iv) identification of false positives and false negatives from RNAi experiments. Diffusion kernels are demonstrated to give superior performance in all these tasks, leading to the suggestion that diffusion kernels should be the primary choice for a network similarity metric over other similarity measures such as direct neighbors and shortest path distance.
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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.