ArticleBriefings in bioinformatics2022
High dimensionality reduction by matrix factorization for systems pharmacology.
Article in Briefings in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Bibliometric analysis and core target identification of network pharmacology on neuroinflammation in central nervous system disorders: Trends, collaborations, and future directions.Neuroprotection (Chichester, England) · 2025Review
- Identification of Gene Signatures Associated with COVID-19 across Children, Adolescents, and Adults in the Nasopharynx and Peripheral Blood by Using a Machine Learning Approach.Current gene therapy · 2025Article
- Article
- CTGF, FN1, IL-6, THBS1, and WISP1 genes and PI3K-Akt signaling pathway as prognostic and therapeutic targets in gastric cancer identified by gene network modeling.Discover oncology · 2024Article
- A systems biology approach to pathogenesis of gastric cancer: gene network modeling and pathway analysis.BMC gastroenterology · 2023Article
- Multi-objective optimization determines when, which and how to fuse deep networks: An application to predict COVID-19 outcomes.Computers in biology and medicine · 2023Article
- Face mask detection and social distance monitoring system for COVID-19 pandemic.Multimedia tools and applications · 2023Article
- Comparative Study on International Research Hotspots and National-Level Policy Keywords of Dynamic Disaster Monitoring and Early Warning in China (2000-2021).International journal of environmental research and public health · 2022Article
- Leveraging Reddit for Suicidal Ideation Detection: A Review of Machine Learning and Natural Language Processing Techniques.International journal of environmental research and public health · 2022Review
- Decoding clinical biomarker space of COVID-19: Exploring matrix factorization-based feature selection methods.Computers in biology and medicine · 2022Article
- Decoding Clinical Biomarker Space of COVID-19: Exploring Matrix Factorization-based Feature Selection Methods.medRxiv : the preprint server for health sciences · 2021Article
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Authors and funding
7 authors.
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Abstract
The extraction of predictive features from the complex high-dimensional multi-omic data is necessary for decoding and overcoming the therapeutic responses in systems pharmacology. Developing computational methods to reduce high-dimensional space of features in in vitro, in vivo and clinical data is essential to discover the evolution and mechanisms of the drug responses and drug resistance. In this paper, we have utilized the matrix factorization (MF) as a modality for high dimensionality reduction in systems pharmacology. In this respect, we have proposed three novel feature selection methods using the mathematical conception of a basis for features. We have applied these techniques as well as three other MF methods to analyze eight different gene expression datasets to investigate and compare their performance for feature selection. Our results show that these methods are capable of reducing the feature spaces and find predictive features in terms of phenotype determination. The three proposed techniques outperform the other methods used and can extract a 2-gene signature predictive of a tyrosine kinase inhibitor treatment response in the Cancer Cell Line Encyclopedia.
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