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Machine Learning and Integrative Analysis of Biomedical Big Data
Open accessAbstract
Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues.
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“Machine learning (ML) and artificial intelligence (AI) have shown promise in overcoming some of these hindrances (e.g., Mirza et al., 2019; Zhu, 2020) and have gained popularity in recent years.”
From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Mirza et al., 2019
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Metadata
- Authors
- Mirza, B., Wang, W., Wang, J., Choi, H., Chung, N. C., & Ping, P.
- Affiliations
- University of California, Los Angeles (US)
- Corresponding author
- Mirza, Bilal
- Ping, Peipei
- Venue
- Genes
- Publisher
- MDPI AG
- Published
- 2019-01-28
- Volume
- 10
- Issue
- 2
- Pages
- 87
- ISSN
- 2073-4425 (ISSN-L)
- PMC
- PMC6410075
- OpenAlex
- W2912411326
- Cited by
- 380
- References
- 231
- Citations by year
- 2026: 36 · 2025: 68 · 2024: 51 · 2023: 58 · 2022: 72 · 2021: 54 · 2020: 31 · 2019: 9
- FWCI
- 3.66
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Genes (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institutes of Health (R35-HL135772, U54-GM114833)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- multi-omics integration, integrative analysis, machine learning, precision medicine, curse of dimensionality, data heterogeneity, missing data, class imbalance, scalability, omics data, clinical data
- MeSH terms
- Machine Learning, Big Data, Animals, Humans, Computational Biology
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