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 9 publications

(2 citation statements)

References 31 publications

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“…Data mining and bioinformatics play essential roles in analyzing and extracting valuable information from large-scale biological datasets [67]. Data mining techniques, such as clustering, classification, and pattern recognition, help identify meaningful relationships, similarities, and patterns within complex datasets [68].…”

Section: Role Of Data Mining and Bioinformatics In Analyzing Large-sc...mentioning

confidence: 99%

Emerging trends in computational approaches for drug discovery in molecular biology

Yakubu Magaji Yuguda,

Israel Ebehitale Unuebho,

Tejonidhi kolli

et al. 2023

GSC Biol. Pharm. Sci.

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Purpose of Research: This review paper delves into the transformative impact of computational approaches on drug discovery within molecular biology. It explores how these methods offer efficient and cost-effective solutions to identify and optimize potential drug candidates, addressing the shortcomings of traditional drug discovery methods. Scope of the Experiments: Algorithms, machine learning, artificial intelligence, and quantum mechanics are studied. These approaches analyse large datasets, anticipate drug- target interactions, and improve drug design. Supervised and unsupervised learning techniques enable chemical space exploration, target identification, and compound classification. Bioinformatics and data mining enable target identification, drug discovery, and personalised treatment by analysing large biological databases. Quantum mechanics- based techniques reveal molecular structures, interactions, and reactions, improving drug design and optimisation. Results and Findings: The review demonstrates that computational approaches have the potential to expedite drug discovery by leveraging machine learning, artificial intelligence, quantum mechanics, Big Data, and omics methods. These techniques enable accurate prediction of drug-target interactions and efficient exploration of chemical and biological spaces. The integration of diverse datasets enhances target identification and personalized medicine, while quantum mechanics-based insights improve drug design. Conclusions: Despite their benefits, computational approaches face challenges such as model accuracy, efficiency, and validation. Nonetheless, this review underscores the significance of these approaches and their applications in drug discovery. By addressing challenges and embracing emerging technologies, the field can propel advancements in computational drug discovery. This progress will not only benefit patients but also advance the overall landscape of healthcare.

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Section: Role Of Data Mining and Bioinformatics In Analyzing Large-sc...mentioning

confidence: 99%

Emerging trends in computational approaches for drug discovery in molecular biology

Yakubu Magaji Yuguda,

Israel Ebehitale Unuebho,

Tejonidhi kolli

et al. 2023

GSC Biol. Pharm. Sci.

View full text Add to dashboard Cite

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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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