Machine learning in cardiovascular genomics, proteomics, and drug discovery

Yeung, Ming Wai; Benjamins, Jan-Walter; Harst, Pim van der; Juárez-Orozco, Luis Eduardo

doi:10.1016/b978-0-12-820273-9.00014-2

Cited by 2 publications

(1 citation statement)

References 91 publications

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“…Even though there are still many obstacles to overcome, current initiatives for the discovery and development of disease-related biomarkers will help with the best decision-making during the medication development process and further our comprehension of the disease processes. To the benefit of patients, healthcare professionals, and the biopharmaceutical industry, good preclinical biomarker translation into the clinic will pave the path for the effective execution of personalized therapies across a range of complex disease areas [63,64]. Figure 5 illustrates the distribution of the all studies focusing on each area of application.…”

Section: Cmentioning

confidence: 99%

Biomarker discovery in multi-omics datasets using tensor decompositions; A comprehensive review

Koleini¹,

Gemperline²,

Tabrizi³

EPiC Series in Computing

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A multi-omics dataset combining clinical features with the discovery of biomarkers could contribute significantly to the timely identification of mortality risk and the develop- ment of personalized therapies for a wide range of diseases, including cancer and stroke. As well, new advances in “omics” technologies can open up a lot of possibilities for researchers to find disease biomarkers through system-level analysis. Machine learning methods, es- pecially based on tensor decomposition methods (TD-based), are becoming more popular because the integrative analysis of multi-omics data is challenging due to biological com- plexity. Therefore, it is important to identify future research directions and opportunities on the topic of biomarker discovery using tensor decompositions in multi-omics datasets by integrating literature reviews. This article systematically reviews the research trends from 2015 to 2022. Several themes are discussed, including challenges and problems of de- veloping and applying tensor decompactions, application areas for biomarker discovery in “omics” datasets, proposed methodologies, key evaluation criteria used in deciding whether the new methods are effective, and the limitations and shortcomings of this field, which call for further research and development. This review helps researchers who are interested in this field understand what research has already been done and where potential areas for future research might lie.

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Section: Cmentioning

confidence: 99%

Biomarker discovery in multi-omics datasets using tensor decompositions; A comprehensive review

Koleini¹,

Gemperline²,

Tabrizi³

EPiC Series in Computing

View full text Add to dashboard Cite

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Cardiovascular Applications of Artificial Intelligence in Research, Diagnosis, and Disease Management

Rajagopalan

Cao

2022

Advances in Computational Intelligence and Robotics

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Despite significant advancements in diagnosis and disease management, cardiovascular (CV) disorders remain the No. 1 killer both in the United States and across the world, and innovative and transformative technologies such as artificial intelligence (AI) are increasingly employed in CV medicine. In this chapter, the authors introduce different AI and machine learning (ML) tools including support vector machine (SVM), gradient boosting machine (GBM), and deep learning models (DL), and their applicability to advance CV diagnosis and disease classification, and risk prediction and patient management. The applications include, but are not limited to, electrocardiogram, imaging, genomics, and drug research in different CV pathologies such as myocardial infarction (heart attack), heart failure, congenital heart disease, arrhythmias, valvular abnormalities, etc.

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Machine learning in cardiovascular genomics, proteomics, and drug discovery

Cited by 2 publications

References 91 publications

Biomarker discovery in multi-omics datasets using tensor decompositions; A comprehensive review

Biomarker discovery in multi-omics datasets using tensor decompositions; A comprehensive review

Cardiovascular Applications of Artificial Intelligence in Research, Diagnosis, and Disease Management

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