GWAS-based machine learning approach to predict duloxetine response in major depressive disorder

Maciukiewicz, Małgorzata; Marshe, Victoria S.; Hauschild, Anne-Christin; Foster, Jane A.; Rotzinger, Susan; Kennedy, James L.; Kennedy, Sidney H.; Müller, Daniel J.; Geraci, Joseph

doi:10.1016/j.jpsychires.2017.12.009

Cited by 68 publications

(55 citation statements)

References 40 publications

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“…Lin et al [21] Deep learning architecture AUC = 0.82, sensitivity = 0.75, specificity = 0.69 for antidepressant treatment response; AUC = 0.81, sensitivity = 0.77, specificity = 0.66 for remission Kautzky et al [22] Random forest An accuracy of 25% for antidepressant treatment outcome Furthermore, a recent study by Maciukiewicz et al [27] implicated that a support vector machine (SVM)-based and decision trees-based structure, a conventional artificial intelligence and machine learning method, can estimate antidepressant treatment response with an accuracy of 52% based on SNPs. The multi-omics data in their study included the SNPs datasets.…”

Section: Study Model Resultsmentioning

confidence: 99%

“…Firstly, Maciukiewicz et al [27] carried out a GWAS study to search for genetic susceptibility loci of antidepressant treatment response in a hypothesis-free manner. Then, least absolute shrinkage and selection operator (LASSO) regression was performed to find potentially significant predictors including the rs2036270 SNP in the retinoic acid receptor beta (RARB) gene and the rs7037011 SNP near the LOC105375971 gene [27]. To strengthen the accuracy for treatment prediction, LASSO implements both regularization and variable selection [28].…”

Section: Study Model Resultsmentioning

confidence: 99%

“…To predict antidepressant treatment response, various research studies [22][23][24][25][26][27][28][29][30] used conventional artificial intelligence and machine learning methods. For example, Kautzky et al [22] suggested that the random forest structure, a conventional artificial intelligence and machine learning method, correctly predicted 25% of responders for antidepressant treatment outcome based on genetic and clinical data.…”

Section: Applications In Treatment Predictionmentioning

confidence: 99%

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Precision Psychiatry Applications with Pharmacogenomics: Artificial Intelligence and Machine Learning Approaches

Lin

Lane

2020

IJMS

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A growing body of evidence now suggests that precision psychiatry, an interdisciplinary field of psychiatry, precision medicine, and pharmacogenomics, serves as an indispensable foundation of medical practices by offering the accurate medication with the accurate dose at the accurate time to patients with psychiatric disorders. In light of the latest advancements in artificial intelligence and machine learning techniques, numerous biomarkers and genetic loci associated with psychiatric diseases and relevant treatments are being discovered in precision psychiatry research by employing neuroimaging and multi-omics. In this review, we focus on the latest developments for precision psychiatry research using artificial intelligence and machine learning approaches, such as deep learning and neural network algorithms, together with multi-omics and neuroimaging data. Firstly, we review precision psychiatry and pharmacogenomics studies that leverage various artificial intelligence and machine learning techniques to assess treatment prediction, prognosis prediction, diagnosis prediction, and the detection of potential biomarkers. In addition, we describe potential biomarkers and genetic loci that have been discovered to be associated with psychiatric diseases and relevant treatments. Moreover, we outline the limitations in regard to the previous precision psychiatry and pharmacogenomics studies. Finally, we present a discussion of directions and challenges for future research.

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Section: Study Model Resultsmentioning

confidence: 99%

Section: Study Model Resultsmentioning

confidence: 99%

Section: Applications In Treatment Predictionmentioning

confidence: 99%

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Precision Psychiatry Applications with Pharmacogenomics: Artificial Intelligence and Machine Learning Approaches

Lin

Lane

2020

IJMS

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“…Some studies have already tested this approach, despite convincing independent replication is lacking. [79][80][81] The improvement in our statistical analysis methods and in genotyping technologies will both contribute to the evolution of pharmacogenomics in the next years. For example, the cost of genotyping has shown more than an exponential decrease after 2007 and the cost for sequencing a human genome dropped from $95.263.072 in 2001 to $1.121 in 2017.…”

Section: Discussionmentioning

confidence: 99%

Pharmacogenetics and Depression: A Critical Perspective

Corponi

Fabbri

Serretti

2019

Psychiatry Investig

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Depression leads the higher personal and socio-economical burden within psychiatric disorders. Despite the fact that over 40 antidepressants (ADs) are available, suboptimal response still poses a major challenge and is thought to be partially a result of genetic variation. Pharmacogenetics studies the effects of genetic variants on treatment outcomes with the aim of providing tailored treatments, thereby maximizing efficacy and tolerability. After two decades of pharmacogenetic research, variants in genes coding for the cytochromes involved in ADs metabolism (CYP2D6 and CYP2C19) are now considered biomarkers with sufficient scientific support for clinical application, despite the lack of conclusive cost/effectiveness evidence. The effect of variants in genes modulating ADs mechanisms of action (pharmacodynamics) is still controversial, because of the much higher complexity of ADs pharmacodynamics compared to ADs metabolism. Considerable progress has been made since the era of candidate gene studies: the genomic revolution has made possible to assess genetic variance on an unprecedented scale, throughout the whole genome, and to analyze the cumulative effect of different variants. The results have revealed key information on the biological mechanisms mediating ADs effect and identified hypothetical new pharmacological targets. They also paved the way for future availability of polygenic pharmacogenetic panels to predict treatment outcome, which are expected to explain much higher variance in ADs response compared to CYP2D6 and CYP2C19 only. As the demand and availability of AD pharmacogenetic testing is projected to increase, it is important for clinicians to keep abreast of this evolving area to facilitate informed discussions with their patients. Psychiatry Investig 2019;16(9):645-653cc This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/bync/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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“…This issue can be prevented by applying nested cross-validation or by excluding certain datasets before the validation process [85,86].…”

Section: Methodsmentioning

confidence: 99%

Disease networks and their contribution to disease understanding and drug repurposing: Evolution of the concept, techniques and data sources

et al. 2018

Preprint

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Over a decade ago, a new discipline called network medicine emerged as an approach to understand human diseases from a network theory point-of-view. Disease networks proved to be an intuitive and powerful way to reveal hidden connections among apparently unconnected biomedical entities such as diseases, physiological processes, signaling pathways, and genes.One of the fields that has benefited most from this improvement is the identification of new opportunities for the use of old drugs, known as drug repurposing. The importance of drug repurposing lies in the high costs and the prolonged time from target selection to regulatory approval of traditional drug development. In this document we analyze the evolution of disease network concept during the last decade and apply a data science pipeline approach to evaluate their functional units. As a result of this analysis, we obtain a list of the most commonly used functional units and the challenges that remain to be solved. This information can be very valuable for the generation of new prediction models based on disease networks. Data Science Pipelineimprove the classification of diseases [2]. However, the use of these sources raised new problems such as their fragmentation, heterogeneity, availability and different conceptualization of their data [3,4].Recent developments in network theory provide a way to address this challenge by representing these complex relationships as a collection of linked nodes [5]. Complex networks theory is a statistical physics interpretation of the old graph theory, aimed at describing and understanding the structures created by the relationships between the elements of a complex system [6][7][8][9]. Those elements are represented by nodes, pairwise connected by links whenever a relationship is observed between the corresponding elements. The resulting structure can then be described by means of a plethora of topological metrics [10], or be used as a base for modelling the system. The application of this field to biological problems has been named "network biology", while its use in biomedical problems is known as "network medicine" [11]. Some applications of biological networks are protein-protein interaction networks, gene regulatory networks (DNA-protein interaction networks), metabolic networks, signaling networks, neuronal network or phylogenetic trees [12].Following this approach, disease networks express the relationship between diseases as nodes and edges in a graph in, where D represents the set of diseases (nodes) and W the set of their relationships (edges) based upon their similarity. The meaning of similarity varies depending on the data used to build the network, which may be biological (genes or common proteins) or phenotypic (comorbidity, similar symptoms) [13], among other approaches. As will be explained throughout this article, the concept of disease network is not limited to diseasedisease connections (homogeneous networks), but also to relations between the disease and other factors such as its symptoms, its associ...

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GWAS-based machine learning approach to predict duloxetine response in major depressive disorder

Cited by 68 publications

References 40 publications

Precision Psychiatry Applications with Pharmacogenomics: Artificial Intelligence and Machine Learning Approaches

Precision Psychiatry Applications with Pharmacogenomics: Artificial Intelligence and Machine Learning Approaches

Pharmacogenetics and Depression: A Critical Perspective

Disease networks and their contribution to disease understanding and drug repurposing: Evolution of the concept, techniques and data sources

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