Artificial intelligence and machine learning approaches using gene expression and variant data for personalized medicine

Abstract

Precision medicine uses genetic, environmental and lifestyle factors to more accurately diagnose and treat disease in specific groups of patients, and it is considered one of the most promising medical efforts of our time. The use of genetics is arguably the most data-rich and complex components of precision medicine. The grand challenge today is the successful assimilation of genetics into precision medicine that translates across different ancestries, diverse diseases and other distinct populations, which will require clever use of artificial intelligence (AI) and machine learning (ML) methods. Our goal here was to review and compare scientific objectives, methodologies, datasets, data sources, ethics and gaps of AI/ML approaches used in genomics and precision medicine. We selected high-quality literature published within the last 5 years that were indexed and available through PubMed Central. Our scope was narrowed to articles that reported application of AI/ML algorithms for statistical and predictive analyses using whole genome and/or whole exome sequencing for gene variants, and RNA-seq and microarrays for gene expression. We did not limit our search to specific diseases or data sources. Based on the scope of our review and comparative analysis criteria, we identified 32 different AI/ML approaches applied in variable genomics studies and report widely adapted AI/ML algorithms for predictive diagnostics across several diseases.

Integrated ACMG approved genes and ICD codes for the translational research and precision medicine

“…Some of the applications of NGS include but are not limited genomic data models to support clinical decision making, identification of robust epigenic biomarkers as well as clinical translation [3]. Additionally, the latest research indicates that there is merit in integrating untargeted metabolomic profiling with genomic analysis for individuals at the ends of phenotypic expression [45]. This approach demonstrates that integrated genomics helps narrow the gap between treatment and disease by leveraging streamlined analysis on a patient’s genome.…”

“…However, there is no system that integrates the two data types and standardizes the data according to international academic standards [1, 23, 26]. This shortcoming allows symptom-based treatments to be normalized as the default approach to patient care, and to challenge the standard model a solid connection must be made between clinical and genomic data [23, 26]. Even with the latest sequencing technologies, the format and robustness of raw DNA and RNA files, especially WES, are not well suited for current EHR systems [27].…”

Editorial: Artificial intelligence for personalized and predictive genomics data analysis

“…By identifying the novel risk factors and disease biomarkers, genomics and precision medicine has the potential to translate scientific discovery into clinically actionable personal healthcare (Ahmed, 2022). Nevertheless, we still require innovative and intelligent solutions to advance genomics and precision medicine, such as creating new models of medicine where physicians use clinical decision support systems based on Artificial Intelligence (AI) and Machine Learning (ML) to choose the best treatment for a patient guided by the genomics variants that each of us has (Vadapalli et al, 2022).…”

Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis

“…Unfortunately, clinicopathological features poorly characterise ER+/ LN-tumours and immunohistochemical techniques cannot be relied on to make treatment decisions (Fitzgibbons et al, 2000;Eifel et al, 2001). The standard practice has been to use a combination of hormonal and chemotherapy regimens, despite evidence suggesting that around 80% of patients were overtreated and unnecessarily exposed to chemotherapy and the potential toxicity (van 't Veer et al, 2002). Hence, identification of gene expression signatures able to predict risk of recurrence, and therefore stratify treatment, was a breakthrough in early breast cancer management (Schaafsma et al, 2021).…”

“…Machine learning is a very powerful tool for harnessing large-scale data with the aim of identifying predictive biomarkers (Zhang et al, 2021). The use of diverse methods analysing transcriptomic data in various conditions has been previously reviewed (Vadapalli et al, 2022). Appreciation of common pitfalls and focus on interpretable findings has transformed these complex computational approaches into comprehensive tools (Sidak et al, 2022;Whalen et al, 2022).…”