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Literature DB >> 36227554

A Machine Learning-Based Approach Using Multi-omics Data to Predict Metabolic Pathways.

Aakaanksha Kaul¹, Maryanne Varghese¹, Vidya Niranjan², Akshay Uttarkar¹.

Abstract

The integrative method approaches are continuously evolving to provide accurate insights from the data that is received through experimentation on various biological systems. Multi-omics data can be integrated with predictive machine learning algorithms in order to provide results with high accuracy. This protocol chapter defines the steps required for the ML-multi-omics integration methods that are applied on biological datasets for its analysis and the visual interpretation of the results thus obtained.

Entities: Chemical

Keywords: Algorithms; Integration; Machine learning; Multi-omics; Supervised learning; Unsupervised learning

Mesh：

Year: 2023 PMID： 36227554 DOI： 10.1007/978-1-0716-2617-7_19

Source DB: PubMed Journal: Methods Mol Biol ISSN： 1064-3745

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A Machine Learning-Based Approach Using Multi-omics Data to Predict Metabolic Pathways.

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