Journal Article10.2217/PME.15.5
Machine learning for biomarker identification in cancer research - developments toward its clinical application
Zeenia Jagga,Dinesh Gupta +1 more
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TL;DR: This review focuses on the current status of machine learning applications in cancer research, highlighting trends and analyzing major achievements, roadblocks and challenges toward its implementation in clinics.
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Abstract: The patterns identified from the systematically collected molecular profiles of patient tumor samples, along with clinical metadata, can assist personalized treatments for effective management of cancer patients with similar molecular subtypes. There is an unmet need to develop computational algorithms for cancer diagnosis, prognosis and therapeutics that can identify complex patterns and help in classifications based on plethora of emerging cancer research outcomes in public domain. Machine learning, a branch of artificial intelligence, holds a great potential for pattern recognition in cryptic cancer datasets, as evident from recent literature survey. In this review, we focus on the current status of machine learning applications in cancer research, highlighting trends and analyzing major achievements, roadblocks and challenges toward its implementation in clinics.
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Citations
Large-Scale Automatic Feature Selection for Biomarker Discovery in High-Dimensional OMICs Data.
Mickael Leclercq,Benjamin Vittrant,Marie Laure Martin-Magniette,Marie Pier Scott Boyer,Olivier Perin,Alain Bergeron,Yves Fradet,Arnaud Droit +7 more
TL;DR: A biomarker discovery tool that uses a large variety of machine learning algorithms to select the best combination of biomarkers for predicting categorical or continuous outcomes from highly unbalanced datasets.
A hybrid gene selection algorithm for microarray cancer classification using genetic algorithm and learning automata
TL;DR: A hybrid meta-heuristic algorithm, which is an integration of Genetic Algorithm and Learning Automata (GALA), is proposed for gene selection in cancer classification and it has acceptable accuracy and performance on some well-known cancer datasets.
114
Potential applications and performance of machine learning techniques and algorithms in clinical practice: A systematic review
TL;DR: In this article , a systematic review examines the performance of machine learning algorithms and evaluates the progress made to date towards their implementation in clinical practice, concluding that the XGBoost (extreme gradient boosting) algorithm showed the highest potential for clinical applications.
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A Systematic Review on Biomarker Identification for Cancer Diagnosis and Prognosis in Multi-omics: From Computational Needs to Machine Learning and Deep Learning
TL;DR: This study examines the current state of the art and computational methods, including feature selection strategies, ML and DL approaches, and accessible tools to uncover markers in single and multi-omics data.
57
A systematic literature review of data science, data analytics and machine learning applied to healthcare engineering systems
Roberto Salazar-Reyna,Fernando González-Aleu,Edgar M.A. Granda-Gutierrez,Jenny Díaz-Ramírez,Jose Arturo Garza-Reyes,Anil Kumar +5 more
TL;DR: This paper represents the most comprehensive literature-based study on the fields of data analytics, big data, data mining, and machine learning applied to healthcare engineering systems.
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References
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TL;DR: It is argued that primary prevention is a particularly effective way to fight cancer, with between a third and a half of cancers being preventable on the basis of present knowledge of risk factors.
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What are decision trees
TL;DR: Decision trees have been applied to problems such as assigning protein function and predicting splice sites and what types of problems can they solve and what are their advantages over alternatives?
Robust biomarker identification for cancer diagnosis with ensemble feature selection methods
TL;DR: Saeys et al. as discussed by the authors proposed a large-scale analysis of ensemble feature selection, where multiple feature selections are combined in order to increase the robustness of the final set of selected features.
The use of artificial neural networks in decision support in cancer: A systematic review
Paulo J. G. Lisboa,Azzam Taktak +1 more
TL;DR: The clinical fields where neural network methods figure most prominently, the main algorithms featured, methodologies for model selection and the need for rigorous evaluation of results are reviewed.
507
Data mining in the Life Sciences with Random Forest: a walk in the park or lost in the jungle?
Wouter G. Touw,Jumamurat R. Bayjanov,Lex Overmars,Lennart Backus,Jos Boekhorst,Michiel Wels,Sacha A. F. T. van Hijum +6 more
TL;DR: Some of the to the best of the authors' knowledge rarely or never used RF properties that allow maximizing the biological insights that can be extracted from complex omics data sets using RF are detailed.
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