Machine Learning and Integrative Analysis of Biomedical Big Data
| Autor: | Bilal Mirza, Jie Wang, Howard Choi, Peipei Ping, Wei Wang, Neo Christopher Chung |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Big Data lcsh:QH426-470 Computer science Big data Bioengineering Review Machine learning computer.software_genre curse of dimensionality Machine Learning 03 medical and health sciences missing data 0302 clinical medicine class imbalance Genetics Animals Humans data integration Genetics (clinical) scalability Modalities business.industry Human Genome Computational Biology Epigenome multi-omics heterogeneous data Missing data Precision medicine 3. Good health lcsh:Genetics 030104 developmental biology Networking and Information Technology R&D Scalability Artificial intelligence Generic health relevance business computer 030217 neurology & neurosurgery Curse of dimensionality Data integration Biotechnology |
| Zdroj: | Genes Genes, vol 10, iss 2 Genes, Vol 10, Iss 2, p 87 (2019) |
| ISSN: | 2073-4425 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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