Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer

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.

Omics in Systems Biology: Current Progress and Future Outlook

“…For those interested in greater details, they are encouraged to visit the sites listed or read a number of excellent reviews on databases for accessing and analyzing multi‐omic datasets. [ 110–115 ]…”

Cloud Computing Enabled Big Multi-Omics Data Analytics

“…Implementing a large-scale data environment to analyze large-scale genomics data in health care necessitates the effective combination and application of various technologies, such as artificial intelligence, 19 parallel processing techniques, such as Hadoop MapReduce, and data mining tools. Several large data applications, such as the Apache Hadoop software library, are used in biomedical research to overcome scalability, accuracy, and computational complexity issues.…”

How will artificial intelligence impact breast cancer research efficiency?

“…In both groups, they identified a cluster considerably more aggressive than its peers, a finding consistent with the clinical notion that treatment and survival outcomes vary greatly even between patients with the same tumor type. Multi-omic analysis therefore leads to the discovery of new kinds of breast cancer subtypes, and has shown its superiority over traditional classification methods, with a significant impact on patient prognostic scoring [15]. Predicting the tumor's response to drug administration is also a major field of artificial intelligence application, and computational resources integrating big data to discover efficient drug-response patterns are in development [16].…”