ArticleProceedings. International Conference on Data Engineering2017
Integrated Theory- and Data-driven Feature Selection in Gene Expression Data Analysis.
Article in Proceedings. International Conference on Data Engineering, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Applying a GAN-based classifier to improve transcriptome-based prognostication in breast cancer.PLoS computational biology · 2023Article
- Review
- Trust in the scientific research community predicts intent to comply with COVID-19 prevention measures: An analysis of a large-scale international survey dataset.Epidemiology and infection · 2022Article
- CogNet: classification of gene expression data based on ranked active-subnetwork-oriented KEGG pathway enrichment analysis.PeerJ. Computer science · 2021Article
- Application of Biological Domain Knowledge Based Feature Selection on Gene Expression Data.Entropy (Basel, Switzerland) · 2020Review
- CausalMGM: an interactive web-based causal discovery tool.Nucleic acids research · 2020Article
- Integrative Gene Selection on Gene Expression Data: Providing Biological Context to Traditional Approaches.Journal of integrative bioinformatics · 2018Article
- A Pipeline for Integrated Theory and Data-Driven Modeling of Biomedical Data.IEEE/ACM transactions on computational biology and bioinformaticsArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The exponential growth of high dimensional biological data has led to a rapid increase in demand for automated approaches for knowledge production. Existing methods rely on two general approaches to address this challenge: 1) the Theory-driven approach, which utilizes prior accumulated knowledge, and 2) the Data-driven approach, which solely utilizes the data to deduce scientific knowledge. Both of these approaches alone suffer from bias toward past/present knowledge, as they fail to incorporate all of the current knowledge that is available to make new discoveries. In this paper, we show how an integrated method can effectively address the high dimensionality of big biological data, which is a major problem for pure data-driven analysis approaches. We realize our approach in a novel two-step analytical workflow that incorporates a new feature selection paradigm as the first step to handling high-throughput gene expression data analysis and that utilizes graphical causal modeling as the second step to handle the automatic extraction of causal relationships. Our results, on real-world clinical datasets from The Cancer Genome Atlas (TCGA), demonstrate that our method is capable of intelligently selecting genes for learning effective causal networks.
Identifiers
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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.