ArticleFrontiers in genetics2017
Quantifying Gene Regulatory Relationships with Association Measures: A Comparative Study.
Article in Frontiers in genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
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- Tumor draining lymph nodes connected to cold triple-negative breast cancers are characterized by Th2-associated microenvironment.Nature communications · 2024Article
- Advances in computational and experimental approaches for deciphering transcriptional regulatory networks: Understanding the roles of cis-regulatory elements is essential, and recent research utilizing MPRAs, STARR-seq, CRISPR-Cas9, and machine learning has yielded valuable insights.BioEssays : news and reviews in molecular, cellular and developmental biology · 2024Review
- Scalable Causal Structure Learning: Scoping Review of Traditional and Deep Learning Algorithms and New Opportunities in Biomedicine.JMIR medical informatics · 2023Article
- Phosphoproteomics data-driven signalling network inference: Does it work?Computational and structural biotechnology journal · 2023Article
- scGENA: A Single-Cell Gene Coexpression Network Analysis Framework for Clustering Cell Types and Revealing Biological Mechanisms.Bioengineering (Basel, Switzerland) · 2022Article
- Inference of Molecular Regulatory Systems Using Statistical Path-Consistency Algorithm.Entropy (Basel, Switzerland) · 2022Article
- Detecting prognostic biomarkers of breast cancer by regularized Cox proportional hazards models.Journal of translational medicine · 2021Article
- Detection of Genes inGenes · 2021Article
- Comparative Analyses of Gene Co-expression Networks: Implementations and Applications in the Study of Evolution.Frontiers in genetics · 2021Review
- A Novel Calibration Step in Gene Co-Expression Network Construction.Frontiers in bioinformatics · 2021Article
- State of the Field in Multi-Omics Research: From Computational Needs to Data Mining and Sharing.Frontiers in genetics · 2020Review
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- KF-finder: identification of key factors from host-microbial networks in cervical cancer.BMC systems biology · 2018Article
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Abstract
In this work, we provide a comparative study of the main available association measures for characterizing gene regulatory strengths. Detecting the association between genes (as well as RNAs, proteins, and other molecules) is very important to decipher their functional relationship from genomic data in bioinformatics. With the availability of more and more high-throughput datasets, the quantification of meaningful relationships by employing association measures will make great sense of the data. There are various quantitative measures have been proposed for identifying molecular associations. They are depended on different statistical assumptions, for different intentions, as well as with different computational costs in calculating the associations in thousands of genes. Here, we comprehensively summarize these association measures employed and developed for describing gene regulatory relationships. We compare these measures in their consistency and specificity of detecting gene regulations from both simulation and real gene expression profiling data. Obviously, these measures used in genes can be easily extended in other biological molecules or across them.
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