Machine Learning and Integrative Analysis of Biomedical Big Data

By Bilal Mirza, Wei Wang, Jie Wang, Howard Choi, N. C. Chung, P. Ping. Genes, 2019

304 citations recorded by the source corpus.

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.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/cb2bdd57842ca4144d120f5cb357069e8d5e63bd

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Citations in the corpus

Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources (2020, background): “suffer from the curse of dimensionality problem, where the number of samples is far lower than the number of analytes and hence the dimensions, thereby increasing the risk of overfitting, the application of neural networks is a challenge in multi-omics contexts [188].”
Integration strategies of multi-omics data for machine learning analysis (2021, unknown): “(2019) [41].”
Predicting Surgical Outcome in Drug-Resistant Epilepsy by Combining Interictal Biomarkers within a Machine Learning Framework (2026, unknown): “50,51 52–59 However, few studies have further compared the performance of individual features with multi-feature ML-based frameworks’ �ndings.”