scholarly journals Applications of Machine Learning in Drug Discovery II: Biomarker Discovery, Patient Stratification and Pharmacoeconomics

2020 ◽  
Author(s):  
John W. Cassidy
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Biomarker Discovery ◽  
Patient Stratification ◽  
Applications Of Machine Learning

scholarly journals Biomarker discovery studies for patient stratification using machine learning analysis of omics data: a scoping review

BMJ Open ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. e053674
Author(s):  
Enrico Glaab ◽  
Armin Rauschenberger ◽  
Rita Banzi ◽  
Chiara Gerardi ◽  
Paula Garcia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Scoping Review ◽  
Biomarker Discovery ◽  
Biomedical Literature ◽  
Molecular Signature ◽  
Biological Knowledge ◽  
Measurement Technology ◽  
Omics Data ◽  
Patient Stratification ◽  
Complex Disorders

ObjectiveTo review biomarker discovery studies using omics data for patient stratification which led to clinically validated FDA-cleared tests or laboratory developed tests, in order to identify common characteristics and derive recommendations for future biomarker projects.DesignScoping review.MethodsWe searched PubMed, EMBASE and Web of Science to obtain a comprehensive list of articles from the biomedical literature published between January 2000 and July 2021, describing clinically validated biomarker signatures for patient stratification, derived using statistical learning approaches. All documents were screened to retain only peer-reviewed research articles, review articles or opinion articles, covering supervised and unsupervised machine learning applications for omics-based patient stratification. Two reviewers independently confirmed the eligibility. Disagreements were solved by consensus. We focused the final analysis on omics-based biomarkers which achieved the highest level of validation, that is, clinical approval of the developed molecular signature as a laboratory developed test or FDA approved tests.ResultsOverall, 352 articles fulfilled the eligibility criteria. The analysis of validated biomarker signatures identified multiple common methodological and practical features that may explain the successful test development and guide future biomarker projects. These include study design choices to ensure sufficient statistical power for model building and external testing, suitable combinations of non-targeted and targeted measurement technologies, the integration of prior biological knowledge, strict filtering and inclusion/exclusion criteria, and the adequacy of statistical and machine learning methods for discovery and validation.ConclusionsWhile most clinically validated biomarker models derived from omics data have been developed for personalised oncology, first applications for non-cancer diseases show the potential of multivariate omics biomarker design for other complex disorders. Distinctive characteristics of prior success stories, such as early filtering and robust discovery approaches, continuous improvements in assay design and experimental measurement technology, and rigorous multicohort validation approaches, enable the derivation of specific recommendations for future studies.

scholarly journals Current Developments in Machine Learning Techniques in Biological Data Mining

2017 ◽  
Vol 11 ◽  
pp. 117793221668754 ◽  
Author(s):  
Gerard G Dumancas ◽  
Indra Adrianto ◽  
Ghalib Bello ◽  
Mikhail Dozmorov
Keyword(s):  
Machine Learning ◽  
Biomarker Discovery ◽  
Wide Spectrum ◽  
Specific Area ◽  
Biological Data ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Indispensable Tool ◽  
Underlying Mechanisms ◽  
Applications Of Machine Learning

This supplement is intended to focus on the use of machine learning techniques to generate meaningful information on biological data. This supplement under Bioinformatics and Biology Insights aims to provide scientists and researchers working in this rapid and evolving field with online, open-access articles authored by leading international experts in this field. Advances in the field of biology have generated massive opportunities to allow the implementation of modern computational and statistical techniques. Machine learning methods in particular, a subfield of computer science, have evolved as an indispensable tool applied to a wide spectrum of bioinformatics applications. Thus, it is broadly used to investigate the underlying mechanisms leading to a specific disease, as well as the biomarker discovery process. With a growth in this specific area of science comes the need to access up-to-date, high-quality scholarly articles that will leverage the knowledge of scientists and researchers in the various applications of machine learning techniques in mining biological data.

Applications of machine learning in the chemical pathology laboratory

2021 ◽  
pp. jclinpath-2021-207393
Author(s):  
Rivak Punchoo ◽  
Sachin Bhoora ◽  
Nelishia Pillay
Keyword(s):  
Machine Learning ◽  
Error Detection ◽  
Biomarker Discovery ◽  
Large Data ◽  
Clinical Decision ◽  
Data Sets ◽  
Pathology Laboratory ◽  
Enhance Efficiency ◽  
Total Testing Process ◽  
Applications Of Machine Learning

Machine learning (ML) is an area of artificial intelligence that provides computer programmes with the capacity to autodidact and learn new skills from experience, without continued human programming. ML algorithms can analyse large data sets quickly and accurately, by supervised and unsupervised learning techniques, to provide classification and prediction value outputs. The application of ML to chemical pathology can potentially enhance efficiency at all phases of the laboratory’s total testing process. Our review will broadly discuss the theoretical foundation of ML in laboratory medicine. Furthermore, we will explore the current applications of ML to diverse chemical pathology laboratory processes, for example, clinical decision support, error detection in the preanalytical phase, and ML applications in gel-based image analysis and biomarker discovery. ML currently demonstrates exploratory applications in chemical pathology with promising advancements, which have the potential to improve all phases of the chemical pathology total testing pathway.

scholarly journals Applications of machine learning in drug discovery and development

2019 ◽  
Vol 18 (6) ◽  
pp. 463-477 ◽  
Author(s):  
Jessica Vamathevan ◽  
Dominic Clark ◽  
Paul Czodrowski ◽  
Ian Dunham ◽  
Edgardo Ferran ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Drug Discovery And Development ◽  
Applications Of Machine Learning
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