Bioinformatics and biological data mining

Harbola, Aditya; Negi, Deepti; Manchanda, Mahesh; Kesharwani, Rajesh K.

doi:10.1016/b978-0-323-89775-4.00019-5

Cited by 5 publications

(1 citation statement)

References 31 publications

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“…Despite the technical differences, results from microarray and RNA-seq analyses have been shown to be highly consistent with each other [ 7 ]. In the context of data mining, microarray analysis is still widely adopted due to its low cost, high efficiency, limited bias [ 8 ], greater statistical power [ 9 ], and vast number of public neuroscience datasets available for data mining [ 3 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Data Mining of Microarray Datasets in Translational Neuroscience

O’Connor,

Zeng

et al. 2023

Brain Sciences

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Data mining involves the computational analysis of a plethora of publicly available datasets to generate new hypotheses that can be further validated by experiments for the improved understanding of the pathogenesis of neurodegenerative diseases. Although the number of sequencing datasets is on the rise, microarray analysis conducted on diverse biological samples represent a large collection of datasets with multiple web-based programs that enable efficient and convenient data analysis. In this review, we first discuss the selection of biological samples associated with neurological disorders, and the possibility of a combination of datasets, from various types of samples, to conduct an integrated analysis in order to achieve a holistic understanding of the alterations in the examined biological system. We then summarize key approaches and studies that have made use of the data mining of microarray datasets to obtain insights into translational neuroscience applications, including biomarker discovery, therapeutic development, and the elucidation of the pathogenic mechanisms of neurodegenerative diseases. We further discuss the gap to be bridged between microarray and sequencing studies to improve the utilization and combination of different types of datasets, together with experimental validation, for more comprehensive analyses. We conclude by providing future perspectives on integrating multi-omics, to advance precision phenotyping and personalized medicine for neurodegenerative diseases.

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