Machine Learning and Deep Learning for the Pharmacogenomics of Antidepressant Treatments
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A growing body of evidence now proposes that machine learning and deep learning techniques can serve as a vital foundation for the pharmacogenomics of antidepressant treatments in patients with major depressive disorder (MDD). In this review, we focus on the latest developments for pharmacogenomics research using machine learning and deep learning approaches together with neuroimaging and multi-omics data. First, we review relevant pharmacogenomics studies that leverage numerous machine learning and deep learning techniques to determine treatment prediction and potential biomarkers for antidepressant treatments in MDD. In addition, we depict some neuroimaging pharmacogenomics studies that utilize various machine learning approaches to predict antidepressant treatment outcomes in MDD based on the integration of research on pharmacogenomics and neuroimaging. Moreover, we summarize the limitations in regard to the past pharmacogenomics studies of antidepressant treatments in MDD. Finally, we outline a discussion of challenges and directions for future research. In light of latest advancements in neuroimaging and multi-omics, various genomic variants and biomarkers associated with antidepressant treatments in MDD are being identified in pharmacogenomics research by employing machine learning and deep learning algorithms.
Abstract
A growing body of evidence now proposes that machine learning and deep learning techniques can serve as a vital foundation for the pharmacogenomics of antidepressant treatments in patients with major depressive disorder (MDD). In this review, we focus on the latest developments for pharmacogenomics research using machine learning and deep learning approaches together with neuroimaging and multi-omics data. First, we review relevant pharmacogenomics studies that leverage numerous machine learning and deep learning techniques to determine treatment prediction and potential biomarkers for antidepressant treatments in MDD. In addition, we depict some neuroimaging pharmacogenomics studies that utilize various machine learning approaches to predict antidepressant treatment outcomes in MDD based on the integration of research on pharmacogenomics and neuroimaging. Moreover, we summarize the limitations in regard to the past pharmacogenomics studies of antidepressant treatments in MDD. Finally, we outline a discussion of challenges and directions for future research. In light of latest advancements in neuroimaging and multi-omics, various genomic variants and biomarkers associated with antidepressant treatments in MDD are being identified in pharmacogenomics research by employing machine learning and deep learning algorithms.
참고문헌 (79)
- Lin E, "Precision psychiatry applications with pharmacogenomics : artificial intelligence and machine learning approaches", Int J Mol Sci, 21, pp.969~ (2020)
- Klein ME, "Clinical implementation of pharmacogenomics for personalized precision medicine : barriers and solutions", J Pharm Sci, 106, pp.2368~2379 (2017)
- Torres EB, "Toward precision psychiatry : statistical platform for the personalized characterization of natural behaviors", Front Neurol, 7, pp.8~ (2016)
- Amare AT, "Pharmacogenomics in the treatment of mood disorders : strategies and opportunities for personalized psychiatry", EPMA J, 8, pp.211~227 (2017)
- Lin E, "Genome-wide microarray analysis of gene expression profiling in major depression and antidepressant therapy", Prog Neuropsychopharmacol Biol Psychiatry, 64, pp.334~340 (2016)
- Lin E, "Genome-wide association studies in pharmacogenomics of antidepressants", Pharmacogenomics, 16, pp.555~566 (2015)
- Lin E, "Multi-omics and machine learning applications in precision medicine", Curr Pharmacogen Personal Med, 15, pp.97~104 (2017)
- Bzdok D, "Machine learning for precision psychiatry : opportunities and challenges", Biol Psychiatry Cogn Neurosci Neuroimaging, 3, pp.223~230 (2018)
- Lin E, "Machine learning and systems genomics approaches for multi-omics data", Biomark Res, 5, pp.2~ (2017)
- Athreya AP, "Integration of machine learning and pharmacogenomic biomarkers for predicting response to antidepressant treatment:can computational intelligence be used to augment clinical assessments?", Pharmacogenomics, 20, pp.983~988 (2019)
- Litjens G, "A survey on deep learning in medical image analysis", Med Image Anal, 42, pp.60~88 (2017)
- Ching T, "Opportunities and obstacles for deep learning in biology and medicine", J R Soc Interface, 15, pp.20170387~ (2018)
- Dana D, "Deep learning in drug discovery and medicine; scratching the surface", Molecules, 23, pp.2384~ (2018)
- Grapov D, "Rise of deep learning for genomic, proteomic, and metabolomic data integration in precision medicine", OMICS, 22, pp.630~636 (2018)
- Zhang S, "Learning for personalized medicine : a comprehensive review from a deep learning perspective", IEEE Rev Biomed Eng, 12, pp.194~208 (2019)
- LeCun Y, "Deep learning", Nature, 521, pp.436~444 (2015)
- Hinton G, "Deep learning-a technology with the potential to transform health care", JAMA, 320, pp.1101~1102 (2018)
- Hulshoff Pol H, "Neural networks in psychiatry", Eur Neuropsychopharmacol, 23, pp.1~6 (2013)
- Ekins S, "The next era : deep learning in pharmaceutical research", Pharm Res, 33, pp.2594~2603 (2016)
- Mamoshina P, "Applications of deep learning in biomedicine", Mol Pharm, 13, pp.1445~1454 (2016)
- Falcone M, "Neuroimaging in psychiatric pharmacogenetics research : the promise and pitfalls", Neuropsychopharmacology, 38, pp.2327~2337 (2013)
- Mufford MS, "Neuroimaging genomics in psychiatry-a translational approach", Genome Med, 9, pp.102~ (2017)
- Davatzikos C, "Machine learning in neuroimaging : progress and challenges", Neuroimage, 197, pp.652~656 (2019)
- Godlewska BR, "Neuroimaging as a tool for individualized treatment choice in depression : the past, the present and the future", Curr Behav Neurosci Rep, 7, pp.32~39 (2020)
- Patel MJ, "Studying depression using imaging and machine learning methods", Neuroimage Clin, 10, pp.115~123 (2015)
- Iniesta R, "Machine learning, statistical learning and the future of biological research in psychiatry", Psychol Med, 46, pp.2455~2465 (2016)
- Lane HY, "Assessing gene-gene interactions in pharmacogenomics", Mol Diagn Ther, 16, pp.15~27 (2012)
- Lin E, "Pattern-recognition tech-niques with haplotype analysis in pharmacogenomics", Pharmacogenomics, 8, pp.75~83 (2007)
- Dwyer DB, "Machine learning approaches for clinical psychology and psychiatry", Annu Rev Clin Psychol, 14, pp.91~118 (2018)
- Perlman K, "A systematic meta-review of predictors of antidepressant treatment outcome in major depressive disorder", J Affect Disord, 243, pp.503~515 (2019)
- Huang LC, "A comparison of classification methods for predicting Chronic Fatigue Syndrome based on genetic data", J Transl Med, 7, pp.81~ (2009)
- Maciukiewicz M, "GWAS-based machine learning approach to predict duloxetine response in major depressive disorder", J Psychiatr Res, 99, pp.62~68 (2018)
- Tibshirani R, "The lasso method for variable selection in the Cox model", Stat Med, 16, pp.385~395 (1997)
- Vapnik V, "The nature of statistical learning theory" (2013), Springer Science & Business Media
- Lin E, "A support vector machine approach to assess drug efficacy of interferon-alpha and ribavirin combination therapy", Mol Diagn Ther, 12, pp.219~223 (2008)
- Breiman L, "Random forests", Mach Learn, 45, pp.5~32 (2001)
- Kautzky A, "The combined effect of genetic polymorphisms and clinical parameters on treatment outcome in treatment-resistant depression", Eur Neuropsychopharmacol, 25, pp.441~453 (2015)
- Athreya AP, "Pharmacogenomics-driven prediction of antidepressant treatment outcomes : a machine-learning approach with multi-trial replication", Clin Pharmacol Ther, 106, pp.855~865 (2019)
- Galar M, "A review on ensembles for the class imbalance problem : bagging-, boosting-, and hybrid-based approaches", IEEE Trans Syst Man Cybern Part C, 42, pp.463~484 (2011)
- Yang P, "A review of ensemble methods in bioinformatics", Curr Bioinform, 5, pp.296~308 (2010)
- Lin E, "An ensemble approach to predict schizophrenia using protein data in the N-methyl-D-aspartate receptor(NMDAR)and tryptophan catabolic pathways", Front Bioeng Biotechnol, 8, pp.569~ (2020)
- Lin E, "Prediction of antidepressant treatment response and remission using an ensemble machine learning framework", Pharmaceuticals(Basel), 13, pp.305~ (2020)
- Kohavi R, "Wrappers for feature subset selection", Artif Intell, 97, pp.273~324 (1997)
- Rumelhart DE, "Parallel distributed processing: explorations in the microstructure of cognition. Vol. 1: foundations" (1996), MIT Press
- Lin E, "A deep learning approach for predicting antidepressant response in major depression using clinical and genetic biomarkers", Front Psychiatry, 9, pp.290~ (2018)
- Fonseka TM, "Neuroimaging biomarkers as predictors of treatment outcome in Major Depressive Disorder", J Affect Disord, 233, pp.21~35 (2018)
- Kang SG, "Neuroimaging biomarkers for predicting treatment response and recurrence of major depressive disorder", Int J Mol Sci, 21, pp.2148~ (2020)
- Janssen RJ, "Making individual prognoses in psychiatry using neuroimaging and machine learning", Biol Psychiatry Cogn Neurosci Neuroimaging, 3, pp.798~808 (2018)
- Patel MJ, "Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction", Int J Geriatr Psychiatry, 30, pp.1056~1067 (2015)
- Hariri AR, "Imaging genetics : perspectives from studies of genetically driven variation in serotonin function and corticolimbic affective processing", Biol Psychiatry, 59, pp.888~897 (2006)
- Rabl U, "Imaging genetics : implications for research on variable antidepressant drug response", Expert Rev Clin Pharmacol, 3, pp.471~489 (2010)
- Thompson PM, "The ENIGMA Consortium : large-scale collaborative analyses of neuroimaging and genetic data", Brain Imaging Behav, 8, pp.153~182 (2014)
- Lam RW, "Discovering biomarkers for antidepressant response : protocol from the Canadian biomarker integration network in depression(CAN-BIND)and clinical characteristics of the first patient cohort", BMC Psychiatry, 16, pp.105~ (2016)
- Gottesman II, "The endophenotype concept in psychiatry : etymology and strategic intentions", Am J Psychiatry, 160, pp.636~645 (2003)
- Meyer-Lindenberg A, "Intermediate phenotypes and genetic mechanisms of psychiatric disorders", Nat Rev Neurosci, 7, pp.818~827 (2006)
- Viviani R, "Use of magnetic resonance imaging in pharmacogenomics", Br J Clin Pharmacol, 77, pp.684~694 (2014)
- Buch AM, "Dissecting diagnostic heterogeneity in depression by integrating neuroimaging and genetics", Neuropsychopharmacology, 46, pp.156~175 (2021)
- Carter CS, "Enhancing the informativeness and replicability of imaging genomics studies", Biol Psychiatry, 82, pp.157~164 (2017)
- Pereira LP, "Imaging genetics paradigms in depression research : systematic review and meta-analysis", Prog Neuropsychopharmacol Biol Psychiatry, 86, pp.102~113 (2018)
- Chang B, "ARPNet:antidepressant response prediction network for major depressive disorder", Genes(Basel), 10, pp.907~ (2019)
- Pei C, "Ensemble learning for early-response prediction of antidepressant treatment in major depressive disorder", J Magn Reson Imaging, 52, pp.161~171 (2020)
- Rutledge RB, "Machine learning and big data in psychiatry : toward clinical applications", Curr Opin Neurobiol, 55, pp.152~159 (2019)
- Lin E, "Relevant applications of generative adversarial networks in drug design and discovery : molecular de novo design, dimensionality reduction, and de novo peptide and protein design", Molecules, 25, pp.3250~ (2020)
- Kalinin AA, "Deep learning in pharmacogenomics : from gene regulation to patient stratification", Pharmacogenomics, 19, pp.629~650 (2018)
- Jiao Y, "Performance measures in evaluating machine learning based bioinformatics predictors for classifications", Quant Biol, 4, pp.320~330 (2016)
- Wong TT, "Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation", Pattern Recognit, 48, pp.2839~2846 (2015)
- Zou J, "A primer on deep learning in genomics", Nat Genet, 51, pp.12~18 (2019)
- Russell LE, "Variant discovery using next-generation sequencing and its future role in pharmacogenetics", Pharmacogenomics, 21, pp.471~486 (2020)
- Azodi CB, "Benchmarking parametric and machine learning models for genomic prediction of complex traits", G3(Bethesda), 9, pp.3691~3702 (2019)
- Azodi CB, "Opening the black box : interpretable machine learning for geneticists", Trends Genet, 36, pp.442~455 (2020)
- Eraslan G, "Deep learning : new computational modelling techniques for genomics", Nat Rev Genet, 20, pp.389~403 (2019)
- Kalamara A, "How to find the right drug for each patient? Advances and challenges in pharmacogenomics", Curr Opin Syst Biol, 10, pp.53~62 (2018)
- Najafabadi MM, "Deep learning applications and challenges in big data analytics", J Big Data, 2, pp.1~ (2015)
- Goodfellow I, "Generative adversarial nets" (2014), University of Montreal
- Lin E, "A deep adversarial variational autoencoder model for dimensionality reduction in single-cell RNA sequencing analysis", BMC Bioinformatics, 21, pp.64~ (2020)
- Lin E, "Combination of G72 genetic variation and G72 protein level to detect schizophrenia : machine learning approaches", Front Psychiatry, 9, pp.566~ (2018)
- Angermueller C, "Deep learning for computational biology", Mol Syst Biol, 12, pp.878~ (2016)
- Lin E, "Soft computing for biological systems", pp.13~22 (2018), Springer
- Litjens G, "Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis", Sci Rep, 6, pp.26286~ (2016)
