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Machine learning and data mining frameworks for predicting drug response in cancer: An overview and a novel in silico screening process based on association rule mining
K. Vougas, T. Sakellaropoulos, A. Kotsinas, GP. Foukas, A. Ntargaras, F. Koinis, A. Polyzos, V. Myrianthopoulos, H. Zhou, S. Narang, V. Georgoulias, L. Alexopoulos, I. Aifantis, PA. Townsend, P. Sfikakis, R. Fitzgerald, D. Thanos, J. Bartek, R....
Jazyk angličtina Země Velká Británie
Typ dokumentu časopisecké články, Research Support, N.I.H., Extramural, práce podpořená grantem, přehledy
Grantová podpora
P30 CA016087
NCI NIH HHS - United States
- MeSH
- data mining * MeSH
- lidé MeSH
- nádory farmakoterapie MeSH
- počítačová simulace MeSH
- strojové učení * MeSH
- výsledek terapie MeSH
- zvířata MeSH
- Check Tag
- lidé MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- přehledy MeSH
- Research Support, N.I.H., Extramural MeSH
A major challenge in cancer treatment is predicting the clinical response to anti-cancer drugs on a personalized basis. The success of such a task largely depends on the ability to develop computational resources that integrate big "omic" data into effective drug-response models. Machine learning is both an expanding and an evolving computational field that holds promise to cover such needs. Here we provide a focused overview of: 1) the various supervised and unsupervised algorithms used specifically in drug response prediction applications, 2) the strategies employed to develop these algorithms into applicable models, 3) data resources that are fed into these frameworks and 4) pitfalls and challenges to maximize model performance. In this context we also describe a novel in silico screening process, based on Association Rule Mining, for identifying genes as candidate drivers of drug response and compare it with relevant data mining frameworks, for which we generated a web application freely available at: https://compbio.nyumc.org/drugs/. This pipeline explores with high efficiency large sample-spaces, while is able to detect low frequency events and evaluate statistical significance even in the multidimensional space, presenting the results in the form of easily interpretable rules. We conclude with future prospects and challenges of applying machine learning based drug response prediction in precision medicine.
Applied Bioinformatics Laboratories NYU School of Medicine New York NY 10016 USA
Department of Pathology NYU School of Medicine New York NY 10016 USA
Laboratory of Tumour Cell Biology School of Medicine University of Crete Heraklion Crete Greece
Laura and Isaac Perlmutter Cancer Center NYU School of Medicine New York NY 10016 USA
School of Mechanical Engineering National Technical University of Athens Zografou 15780 Greece
Citace poskytuje Crossref.org
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- $a Vougas, Konstantinos $u Biomedical Research Foundation of the Academy of Athens, 4 Soranou Ephessiou Str., Athens GR-11527, Greece; Molecular Carcinogenesis Group, Department of Histology and Embryology, School of Medicine, National and Kapodistrian University of Athens, 75 Mikras Asias Str, Athens GR-11527, Greece.
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- $a Machine learning and data mining frameworks for predicting drug response in cancer: An overview and a novel in silico screening process based on association rule mining / $c K. Vougas, T. Sakellaropoulos, A. Kotsinas, GP. Foukas, A. Ntargaras, F. Koinis, A. Polyzos, V. Myrianthopoulos, H. Zhou, S. Narang, V. Georgoulias, L. Alexopoulos, I. Aifantis, PA. Townsend, P. Sfikakis, R. Fitzgerald, D. Thanos, J. Bartek, R. Petty, A. Tsirigos, VG. Gorgoulis,
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- $a A major challenge in cancer treatment is predicting the clinical response to anti-cancer drugs on a personalized basis. The success of such a task largely depends on the ability to develop computational resources that integrate big "omic" data into effective drug-response models. Machine learning is both an expanding and an evolving computational field that holds promise to cover such needs. Here we provide a focused overview of: 1) the various supervised and unsupervised algorithms used specifically in drug response prediction applications, 2) the strategies employed to develop these algorithms into applicable models, 3) data resources that are fed into these frameworks and 4) pitfalls and challenges to maximize model performance. In this context we also describe a novel in silico screening process, based on Association Rule Mining, for identifying genes as candidate drivers of drug response and compare it with relevant data mining frameworks, for which we generated a web application freely available at: https://compbio.nyumc.org/drugs/. This pipeline explores with high efficiency large sample-spaces, while is able to detect low frequency events and evaluate statistical significance even in the multidimensional space, presenting the results in the form of easily interpretable rules. We conclude with future prospects and challenges of applying machine learning based drug response prediction in precision medicine.
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- $a Sakellaropoulos, Theodore $u Department of Pathology, NYU School of Medicine, New York, NY 10016, USA; Laura and Isaac Perlmutter Cancer Center, NYU School of Medicine, New York, NY 10016, USA.
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- $a Kotsinas, Athanassios $u Molecular Carcinogenesis Group, Department of Histology and Embryology, School of Medicine, National and Kapodistrian University of Athens, 75 Mikras Asias Str, Athens GR-11527, Greece.
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- $a Myrianthopoulos, Vassilios $u Molecular Carcinogenesis Group, Department of Histology and Embryology, School of Medicine, National and Kapodistrian University of Athens, 75 Mikras Asias Str, Athens GR-11527, Greece; Division of Pharmaceutical Chemistry, School of Pharmacy, National and Kapodistrian University of Athens, Athens, Greece.
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- $a Sfikakis, Petros $u 1st Department of Propaedeutic Internal Medicine, Medical School, Laikon Hospital, National and Kapodistrian University of Athens, 75 Mikras Asias Str, Athens GR-11527, Greece; Center for New Biotechnologies and Precision Medicine, Medical School, National and Kapodistrian University of Athens, 75 Mikras Asias Str, Athens GR-11527, Greece.
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- $a Bartek, Jiri $u Genome Integrity Unit, Danish Cancer Society Research Centre, Strandboulevarden 49, Copenhagen DK-2100, Denmark; Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacky University, Hněvotínská, Olomouc 1333/5 779 00, Czech Republic; Science for Life Laboratory, Division of Translational Medicine and Chemical Biology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm SE-171 77, Sweden.
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- $a Gorgoulis, Vassilis G $u Biomedical Research Foundation of the Academy of Athens, 4 Soranou Ephessiou Str., Athens GR-11527, Greece; Molecular Carcinogenesis Group, Department of Histology and Embryology, School of Medicine, National and Kapodistrian University of Athens, 75 Mikras Asias Str, Athens GR-11527, Greece; Division of Cancer Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, Manchester Cancer Research Centre, NIHR Manchester Biomedical Research Centre, University of Manchester, Manchester M20 4GJ, UK; Center for New Biotechnologies and Precision Medicine, Medical School, National and Kapodistrian University of Athens, 75 Mikras Asias Str, Athens GR-11527, Greece. Electronic address: vgorg@med.uoa.gr.
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