Emerging Trends of Bioinformatics in Health Informatics

Sharma, Mahi; Mondal, Shuvhra; Bhattacharjee, Sudeshna; Jabalia, Neetu

doi:10.1007/978-3-030-68723-6_19

Cited by 5 publications

(4 citation statements)

References 81 publications

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“…These specialized tools and databases are crucial in helping us understand disease mechanisms, find potential biomarkers, and create targeted treatments in the context of chronic conditions like cancer, diabetes, CVDs, and neurodegenerative disorders. Large-scale genomic, transcriptomic, and proteomic datasets can be analyzed more easily in cancer research, thanks to disease-specific bioinformatics tools (Sharma et al, 2021). Numerous cancer types are covered in-depth molecular profiles on websites like The Cancer Genome Atlas.…”

Section: Disease-specific Bioinformatics Tools and Databasesmentioning

confidence: 99%

Bioinformatics Applications in Chronic Diseases: A Comprehensive Review of Genomic, Transcriptomics, Proteomic, Metabolomics, and Machine Learning Approaches

Ogunjobi,

Ohaeri,

Akintola

et al. 2024

MEDIN

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This manuscript provides a detailed exploration of the pivotal role that bioinformatics plays in elucidating the intricate molecular landscape associated with chronic diseases. Emphasizing the significance and prevalence of these enduring health issues, the introduction establishes the broader context of bioinformatics in chronic disease research. This review systematically covers the application of bioinformatics tools and techniques in comprehending, identifying, and managing chronic illnesses. The first section highlights the importance of genetics and genomics, detailing the utilization of genomic data and advancements in genetic biomarker discovery. Subsequently, the discussion extends to transcriptomics and gene expression, encompassing profiling methods, the identification of dysregulated genes, and the regulatory functions of non-coding RNA in long-term illnesses.Moving forward, the manuscript delves into proteomics, elucidating protein-protein interaction networks, associated tools and techniques, and post-translational modifications. This comprehensive coverage aims to provide readers with a nuanced understanding of the molecular complexities underlying chronic diseases. The subsequent section focuses on metabolomics and metabolic pathways, with an emphasis on the clinical utility of metabolite biomarkers, changes in metabolic pathways, and techniques for characterizing diseases. Following this, the manuscript explores machine learning applications in bioinformatics, providing insights into their role in enhancing our understanding of chronic diseases. The later part of the manuscript addresses practical applications and case studies, showcasing disease-specific bioinformatics tools, databases, and the broader utility of research findings. Additionally, the penultimate section examines privacy, ethical considerations, and data quality concerns, addressing challenges and potential paths for the field of bioinformatics. In conclusion, the manuscript discusses forthcoming trends and prospective research directions, contributing to the advancement of bioinformatics research in chronic illnesses. Overall, this review provides a comprehensive overview of the multifaceted applications of bioinformatics in chronic disease research.

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Section: Disease-specific Bioinformatics Tools and Databasesmentioning

confidence: 99%

Bioinformatics Applications in Chronic Diseases: A Comprehensive Review of Genomic, Transcriptomics, Proteomic, Metabolomics, and Machine Learning Approaches

Ogunjobi,

Ohaeri,

Akintola

et al. 2024

MEDIN

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“…With the expansion in volume and complexity of biological data, ML algorithms have been successfully applied to their analysis (LIU et al, 2015;GREENER et al, 2021;. ML algorithms can extract new and useful knowledge from biological data (CHEN et al, 2021), allowing complex analyses, speeding up new findings and reducing research costs (SHARMA et al, 2021). These advances bring important social and economical benefits, such as improving diagnosis, treatment and the design of new medications (SHARMA et al, 2021;CANNATARO;HARRISON, 2021;GHANNAM;TECHTMANN, 2021a) Moreover, with the advancement of next generation sequencing technologies and multiomics analysis (TURNER et al, 2019), studies have focused on discovering and characterizing small non-coding RNAs (sRNAs) in bacteria and archaea, expanding the understanding of gene regulation and elucidating new biological mechanisms (STAV et al, 2019).…”

Section: Bioautoml: Automated Feature Engineering and Metalearning Fo...mentioning

confidence: 99%

“…Due to the expansion and inherent complexity of biological data, ML methods have also shown broad applicability in the biology field (LIU et al, 2015;GREENER et al, 2021;VOLKAMER et al, 2023). ML algorithms can extract useful and meaningful knowledge from biological sequence data (CHEN et al, 2021), accelerating discoveries, reducing research expenses, and increasing scientific efficiency (SHARMA et al, 2021). These advances directly benefit society, the economy, and people's lives.…”

Section: Introduction and Problem Statementmentioning

confidence: 99%

“…These advances directly benefit society, the economy, and people's lives. Furthermore, ML has been successfully used to mitigate the impact of health-related problems (SHARMA et al, 2021;CANNATARO;HARRISON, 2021;GHANNAM;TECHT-MANN, 2021a), e.g., COVID-19 pandemic (CANNATARO;HARRISON, 2021;KAMALOV et al, 2023), cancer diagnosis (PAINULI;BHARDWAJ et al, 2022), and CRISPR/Cas9-based gene-editing technology TROYANSKAYA, 2021;MITROFANOV et al, 2020). Despite its wide application, designing robust and trustworthy ML solutions usually requires expertise not commonly found in health researchers, causing severe inequalities (AHMED; MULA; DHAVALA, 2020; RUBEIS; DUBBALA; METZLER, 2022).…”

Section: Introduction and Problem Statementmentioning

confidence: 99%

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BioAutoML: Democratizing Machine Learning in Life Sciences

Bonidia

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BioAutoML: automated feature engineering and metalearning to predict noncoding RNAs in bacteria

Bonidia

Santos

Almeida

et al. 2022

Briefings in Bioinformatics

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Recent technological advances have led to an exponential expansion of biological sequence data and extraction of meaningful information through Machine Learning (ML) algorithms. This knowledge has improved the understanding of mechanisms related to several fatal diseases, e.g. Cancer and coronavirus disease 2019, helping to develop innovative solutions, such as CRISPR-based gene editing, coronavirus vaccine and precision medicine. These advances benefit our society and economy, directly impacting people’s lives in various areas, such as health care, drug discovery, forensic analysis and food processing. Nevertheless, ML-based approaches to biological data require representative, quantitative and informative features. Many ML algorithms can handle only numerical data, and therefore sequences need to be translated into a numerical feature vector. This process, known as feature extraction, is a fundamental step for developing high-quality ML-based models in bioinformatics, by allowing the feature engineering stage, with design and selection of suitable features. Feature engineering, ML algorithm selection and hyperparameter tuning are often manual and time-consuming processes, requiring extensive domain knowledge. To deal with this problem, we present a new package: BioAutoML. BioAutoML automatically runs an end-to-end ML pipeline, extracting numerical and informative features from biological sequence databases, using the MathFeature package, and automating the feature selection, ML algorithm(s) recommendation and tuning of the selected algorithm(s) hyperparameters, using Automated ML (AutoML). BioAutoML has two components, divided into four modules: (1) automated feature engineering (feature extraction and selection modules) and (2) Metalearning (algorithm recommendation and hyper-parameter tuning modules). We experimentally evaluate BioAutoML in two different scenarios: (i) prediction of the three main classes of noncoding RNAs (ncRNAs) and (ii) prediction of the eight categories of ncRNAs in bacteria, including housekeeping and regulatory types. To assess BioAutoML predictive performance, it is experimentally compared with two other AutoML tools (RECIPE and TPOT). According to the experimental results, BioAutoML can accelerate new studies, reducing the cost of feature engineering processing and either keeping or improving predictive performance. BioAutoML is freely available at https://github.com/Bonidia/BioAutoML.

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Emerging Trends of Bioinformatics in Health Informatics

Cited by 5 publications

References 81 publications

Bioinformatics Applications in Chronic Diseases: A Comprehensive Review of Genomic, Transcriptomics, Proteomic, Metabolomics, and Machine Learning Approaches

Bioinformatics Applications in Chronic Diseases: A Comprehensive Review of Genomic, Transcriptomics, Proteomic, Metabolomics, and Machine Learning Approaches

BioAutoML: Democratizing Machine Learning in Life Sciences

BioAutoML: automated feature engineering and metalearning to predict noncoding RNAs in bacteria

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