Machine Learning: A New Prospect in Multi-Omics Data Analysis of Cancer

Abstract

Cancer is defined as a large group of diseases that is associated with abnormal cell growth, uncontrollable cell division, and may tend to impinge on other tissues of the body by different mechanisms through metastasis. What makes cancer so important is that the cancer incidence rate is growing worldwide which can have major health, economic, and even social impacts on both patients and the governments. Thereby, the early cancer prognosis, diagnosis, and treatment can play a crucial role at the front line of combating cancer. The onset and progression of cancer can occur under the influence of complicated mechanisms and some alterations in the level of genome, proteome, transcriptome, metabolome etc. Consequently, the advent of omics science and its broad research branches (such as genomics, proteomics, transcriptomics, metabolomics, and so forth) as revolutionary biological approaches have opened new doors to the comprehensive perception of the cancer landscape. Due to the complexities of the formation and development of cancer, the study of mechanisms underlying cancer has gone beyond just one field of the omics arena. Therefore, making a connection between the resultant data from different branches of omics science and examining them in a multi-omics field can pave the way for facilitating the discovery of novel prognostic, diagnostic, and therapeutic approaches. As the volume and complexity of data from the omics studies in cancer are increasing dramatically, the use of leading-edge technologies such as machine learning can have a promising role in the assessments of cancer research resultant data. Machine learning is categorized as a subset of artificial intelligence which aims to data parsing, classification, and data pattern identification by applying statistical methods and algorithms. This acquired knowledge subsequently allows computers to learn and improve accurate predictions through experiences from data processing. In this context, the application of machine learning, as a novel computational technology offers new opportunities for achieving in-depth knowledge of cancer by analysis of resultant data from multi-omics studies. Therefore, it can be concluded that the use of artificial intelligence technologies such as machine learning can have revolutionary roles in the fight against cancer.

Keywords: artificial intelligence; cancer; data analysis; machine learning; multi-omics.

FIGURE 1

Cancer treatment approaches (Tacón, 2003; Roffe et al., 2005; Jones and Demark-Wahnefried, 2006; Sagar et al., 2007; Lu et al., 2008; Giustini et al., 2010; Masafi et al., 2011; Stanczyk, 2011; Fleisher et al., 2014; Drãgãnescu and Carmocan, 2017; Bilgin et al., 2018; Carlson et al., 2018; Hojman et al., 2018; Yadav et al., 2018; Bidram et al., 2019; Psihogios et al., 2019; Pucci et al., 2019; Laoudikou and McCarthy, 2020; Najafpour and Shayanfard, 2020).

FIGURE 2

ML approaches. The main approaches of machine learning include: 1) Supervised learning, 2) Unsupervised learning, 3) Semi-supervised Learning, and 4) Reinforcement learning. In supervised learning, the input and output are specified and the data is labeled. In unsupervised learning, specific data does not already exist and is not intended to be an input-output connection, but only to categorize them. Semi-supervised learning uses both labeled and unlabeled data simultaneously to improve learning accuracy. Reinforcement learning loop has a sequence of modes, actions, and rewards (Sedghi et al., 2020).