Mining genetic and transcriptomic data using machine learning approaches in Parkinson’s disease

Abstract

High-throughput techniques have generated abundant genetic and transcriptomic data of Parkinson’s disease (PD) patients but data analysis approaches such as traditional statistical methods have not provided much in the way of insightful integrated analysis or interpretation of the data. As an advanced computational approach, machine learning, which enables people to identify complex patterns and insight from data, has consequently been harnessed to analyze and interpret large, highly complex genetic and transcriptomic data toward a better understanding of PD. In particular, machine learning models have been developed to integrate patient genotype data alone or combined with demographic, clinical, neuroimaging, and other information, for PD outcome study. They have also been used to identify biomarkers of PD based on transcriptomic data, e.g., gene expression profiles from microarrays. This study overviews the relevant literature on using machine learning models for genetic and transcriptomic data analysis in PD, points out remaining challenges, and suggests future directions accordingly. Undoubtedly, the use of machine learning is amplifying PD genetic and transcriptomic achievements for accelerating the study of PD. Existing studies have demonstrated the great potential of machine learning in discovering hidden patterns within genetic or transcriptomic information and thus revealing clues underpinning pathology and pathogenesis. Moving forward, by addressing the remaining challenges, machine learning may advance our ability to precisely diagnose, prognose, and treat PD.

Exploring photoacoustic spectroscopy-based machine learning together with metabolomics to assess breast tumor progression in a xenograft model ex vivo

“…The experimental data obtained from biological specimens are the multivariate types, which arise mostly from the samples’ heterogeneity nature. Various statistical approaches have been used in the past and present to extract meaningful information from such data, uncovering obscure patterns within datasets and revealing the underlying pathology and pathogenesis [ 21 ]. Among the tested statistical approaches, Machine learning is gaining more popularity because of its performance and sensitivity to data classification.…”

Severe COVID-19 shares a common neutrophil activation signature with other acute inflammatory states

“…The use of high-throughput techniques to identify biomarkers, molecular pathways and pathophysiological information derived from genetic and transcriptomic data has contributed to the understanding of the immunopathology of diseases 94 . In this regard, patients with severe COVID-19 have been associated with mutations in genes involved in the regulation of type I and III IFN immunity pointing to the role of structural genomics in determining the course of COVID-19 95 .…”

Bias Investigation in Artificial Intelligence Systems for Early Detection of Parkinson’s Disease: A Narrative Review

“…The Low-Moderate (LM) studies [ 1 , 5 , 9 , 37 , 62 , 65 ] observations are the articles containing information such as (i) high data count of the PD vs. normal; (ii) performance measures; (iii) comparative analysis with various ML, DL, and HDL algorithms; (iv) explanations of the benchmarking studies. The Moderate-bias studies [ 1 , 5 , 9 , 37 , 62 , 65 , 66 ] observations were (i) sufficient data, (ii) average impact factor, and (iii) comparison of the input parameters. The High-Moderate (HM) studies [ 3 , 6 , 54 , 60 , 67 , 68 ] observations associated with the articles were (i) a smaller number of data, (ii) insufficient dissuasion on the selected model, (iii) improper explanation of the algorithm, (iv) insufficient performance analysis, (v) lack of demographic discussion, and (vi) insufficient discussion on clinical evaluation.…”