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
Grid Binary LOgistic REgression (GLORE): building shared models without sharing data.
TL;DR: The results suggest that GLORE performs as well as LR and allows data to remain protected at their original sites and is computationally efficient.
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An introduction to artificial neural networks in bioinformatics—application to complex microarray and mass spectrometry datasets in cancer studies
TL;DR: This review introduces and describes the concepts related to neural networks, the advantages and caveats to their use, examples of their applications in mass spectrometry and microarray research (with a particular focus on cancer studies), and illustrations from recent literature showing where neural networks have performed well in comparison to other machine learning methods.
International Cancer Genome Consortium
Doris Berger
- 06 Jun 2013
TL;DR: Das Bundesministerium für Bildung and Forschung and die Deutsche Krebshilfe e.
Development of a Prognostic Model for Breast Cancer Survival in an Open Challenge Environment
TL;DR: A computational modeling approach that combined several molecular features yielded a robust breast cancer prognostic model that was independently validated in a new patient data set and was described in a Research Article that described the winning model.
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A critical assessment of feature selection methods for biomarker discovery in clinical proteomics
Christin Christin,Huub C. J. Hoefsloot,Huub C. J. Hoefsloot,Age K. Smilde,Age K. Smilde,Berend Hoekman,Frank Suits,Rainer Bischoff,Peter Horvatovich +8 more
TL;DR: It is concluded that the univariate t test and the mww test with multiple testing corrections are not applicable to data sets with small sample sizes, but their performance improves markedly with increasing sample size up to a point at which they outperform the other methods.
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