Abstract
Cancer is the manifestation of abnormalities of different physiological processes involving genes, DNAs, RNAs, proteins, and other biomolecules whose profiles are reflected in different omics data types. As these bio-entities are very much correlated, integrative analysis of different types of omics data, multi-omics data, is required to understanding the disease from the tumorigenesis to the disease progression. Artificial intelligence (AI), specifically machine learning algorithms, has the ability to make decisive interpretation of “big”-sized complex data and, hence, appears as the most effective tool for the analysis and understanding of multi-omics data for patient-specific observations. In this review, we have discussed about the recent outcomes of employing AI in multi-omics data analysis of different types of cancer. Based on the research trends and significance in patient treatment, we have primarily focused on the AI-based analysis for determining cancer subtypes, disease prognosis, and therapeutic targets. We have also discussed about AI analysis of some non-canonical types of omics data as they have the capability of playing the determiner role in cancer patient care. Additionally, we have briefly discussed about the data repositories because of their pivotal role in multi-omics data storing, processing, and analysis.
Highlights
Cancer is a complex heterogeneous disease [1]
Radiomics data deals with the analysis of different types of radiological images of tumor sites, and it is not a direct outcome of the aforementioned bio-entities, the inclusion of radiomics with the multi-omics analysis is going to be a powerful tool for identifying distinct cellular subtypes in a given type of cancer [27, 28]
We have observed successful implementations of varieties of algorithms aiming toward precision oncology
Summary
Cancer is a complex heterogeneous disease [1]. It is a consequence of malfunction and alteration of different biological entities, namely, genes, proteins, mRNAs, miRNAs, metabolites, etc., at a global scale. We are trying to discuss the methodologies and outcomes of AI on the analysis of multi-omics data, specific to cancers. Radiomics data deals with the analysis of different types of radiological images of tumor sites, and it is not a direct outcome of the aforementioned bio-entities, the inclusion of radiomics with the multi-omics analysis is going to be a powerful tool for identifying distinct cellular subtypes in a given type of cancer [27, 28]. The supervised learning-based support vector machine (SVM) algorithm is one of the widely used approaches for the analysis of multi-omics data. Apart from supervised learning-based SVM and RF algorithms, unsupervised learning methods like autoencoders are used to reduce the “big” size of multi-omics data.
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