ArticlePLoS computational biology2012
Chapter 2: Data-driven view of disease biology.
Article in PLoS computational biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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
5 citing papers in PubMed.
- The assessment of efficient representation of drug features using deep learning for drug repositioning.BMC bioinformatics · 2019Article
- A data-driven interactome of synergistic genes improves network-based cancer outcome prediction.PLoS computational biology · 2019Article
- The mechanistic implications of gene expression studies in SSc: Insights from Systems Biology.Current treatment options in rheumatology · 2017Article
- Prediction of gene-phenotype associations in humans, mice, and plants using phenologs.BMC bioinformatics · 2013Article
- Biology: The big challenges of big data.Nature · 2013Article
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Authors and funding
2 authors.
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Abstract
Modern experimental strategies often generate genome-scale measurements of human tissues or cell lines in various physiological states. Investigators often use these datasets individually to help elucidate molecular mechanisms of human diseases. Here we discuss approaches that effectively weight and integrate hundreds of heterogeneous datasets to gene-gene networks that focus on a specific process or disease. Diverse and systematic genome-scale measurements provide such approaches both a great deal of power and a number of challenges. We discuss some such challenges as well as methods to address them. We also raise important considerations for the assessment and evaluation of such approaches. When carefully applied, these integrative data-driven methods can make novel high-quality predictions that can transform our understanding of the molecular-basis of human disease.
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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.