Abstract
The multitude of multi-omics data generated cost-effectively using advanced high-throughput technologies has imposed challenging domain for research in Artificial Intelligence (AI). Data curation poses a significant challenge as different parameters, instruments, and sample preparations approaches are employed for generating these big data sets. AI could reduce the fuzziness and randomness in data handling and build a platform for the data ecosystem, and thus serve as the primary choice for data mining and big data analysis to make informed decisions. However, AI implication remains intricate for researchers/clinicians lacking specific training in computational tools and informatics. Cancer is a major cause of death worldwide, accounting for an estimated 9.6 million deaths in 2018. Certain cancers, such as pancreatic and gastric cancers, are detected only after they have reached their advanced stages with frequent relapses. Cancer is one of the most complex diseases affecting a range of organs with diverse disease progression mechanisms and the effectors ranging from gene-epigenetics to a wide array of metabolites. Hence a comprehensive study, including genomics, epi-genomics, transcriptomics, proteomics, and metabolomics, along with the medical/mass-spectrometry imaging, patient clinical history, treatments provided, genetics, and disease endemicity, is essential. Cancer Moonshot℠ Research Initiatives by NIH National Cancer Institute aims to collect as much information as possible from different regions of the world and make a cancer data repository. AI could play an immense role in (a) analysis of complex and heterogeneous data sets (multi-omics and/or inter-omics), (b) data integration to provide a holistic disease molecular mechanism, (c) identification of diagnostic and prognostic markers, and (d) monitor patient’s response to drugs/treatments and recovery. AI enables precision disease management well beyond the prevalent disease stratification patterns, such as differential expression and supervised classification. This review highlights critical advances and challenges in omics data analysis, dealing with data variability from lab-to-lab, and data integration. We also describe methods used in data mining and AI methods to obtain robust results for precision medicine from “big” data. In the future, AI could be expanded to achieve ground-breaking progress in disease management.
Highlights
Artificial intelligence (AI) is a branch of computer science with enhanced analytical or predictive capabilities to perform interdisciplinary tasks that otherwise require human intellect
There is an urgent need to evaluate in silico technologies like transfer learning (TrLe) methods employing different machine learning (ML) algorithms and applications that utilize predictive feature learned in cell line trained model to build a new model or leverage information from auxiliary data not directly belonging to the problem being handled, that can be used in real clinical settings
Yoon et al showed the potential of AI models for personalized oncology treatments that can estimate individualized treatment effects based on the analysis of counterfactual clinical outcomes (Yoon et al, 2018)
Summary
Artificial intelligence (AI) is a branch of computer science with enhanced analytical or predictive capabilities to perform interdisciplinary tasks that otherwise require human intellect. Integration of “multi-omics” (genomics, epi-genomics, transcriptomics, proteomics, and metabolomics), and “non-omics” (medical/mass-spectrometry imaging, patient clinical history, treatments, and disease endemicity) data could help overcome the challenges in the accurate detection, characterization, and monitoring of cancers. AI could play an immense role in the analysis of complex and heterogeneous data sets, from multi-omics and inter-omics approaches and data integration to provide a holistic disease molecular mechanism, identification of novel dynamic diagnostic and prognostic markers and enable precision cancer management, well beyond the prevalent disease stratification patterns such as differential expression, and supervised classification (Figure 1).
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