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Fong WJ, Tan HM, Garg R, Teh AL, Pan H, Gupta V, Krishna B, Chen ZH, Purwanto NY, Yap F, Tan KH, Chan KYJ, Chan SY, Goh N, Rane N, Tan ESE, Jiang Y, Han M, Meaney M, Wang D, Keppo J, Tan GCY. Comparing feature selection and machine learning approaches for predicting CYP2D6 methylation from genetic variation. Front Neuroinform 2024; 17:1244336. [PMID: 38449836 PMCID: PMC10915285 DOI: 10.3389/fninf.2023.1244336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 10/18/2023] [Indexed: 03/08/2024] Open
Abstract
Introduction Pharmacogenetics currently supports clinical decision-making on the basis of a limited number of variants in a few genes and may benefit paediatric prescribing where there is a need for more precise dosing. Integrating genomic information such as methylation into pharmacogenetic models holds the potential to improve their accuracy and consequently prescribing decisions. Cytochrome P450 2D6 (CYP2D6) is a highly polymorphic gene conventionally associated with the metabolism of commonly used drugs and endogenous substrates. We thus sought to predict epigenetic loci from single nucleotide polymorphisms (SNPs) related to CYP2D6 in children from the GUSTO cohort. Methods Buffy coat DNA methylation was quantified using the Illumina Infinium Methylation EPIC beadchip. CpG sites associated with CYP2D6 were used as outcome variables in Linear Regression, Elastic Net and XGBoost models. We compared feature selection of SNPs from GWAS mQTLs, GTEx eQTLs and SNPs within 2 MB of the CYP2D6 gene and the impact of adding demographic data. The samples were split into training (75%) sets and test (25%) sets for validation. In Elastic Net model and XGBoost models, optimal hyperparameter search was done using 10-fold cross validation. Root Mean Square Error and R-squared values were obtained to investigate each models' performance. When GWAS was performed to determine SNPs associated with CpG sites, a total of 15 SNPs were identified where several SNPs appeared to influence multiple CpG sites. Results Overall, Elastic Net models of genetic features appeared to perform marginally better than heritability estimates and substantially better than Linear Regression and XGBoost models. The addition of nongenetic features appeared to improve performance for some but not all feature sets and probes. The best feature set and Machine Learning (ML) approach differed substantially between CpG sites and a number of top variables were identified for each model. Discussion The development of SNP-based prediction models for CYP2D6 CpG methylation in Singaporean children of varying ethnicities in this study has clinical application. With further validation, they may add to the set of tools available to improve precision medicine and pharmacogenetics-based dosing.
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Affiliation(s)
- Wei Jing Fong
- Computational Biology, National University of Singapore, Singapore, Singapore
| | - Hong Ming Tan
- Computational Biology, National University of Singapore, Singapore, Singapore
| | - Rishabh Garg
- Computational Biology, National University of Singapore, Singapore, Singapore
| | - Ai Ling Teh
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Hong Pan
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Varsha Gupta
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Bernadus Krishna
- Computational Biology, National University of Singapore, Singapore, Singapore
| | - Zou Hui Chen
- Computational Biology, National University of Singapore, Singapore, Singapore
| | | | - Fabian Yap
- KK Women's and Children's Hospital, Singapore, Singapore
| | - Kok Hian Tan
- KK Women's and Children's Hospital, Singapore, Singapore
- Duke NUS Medical School, Singapore, Singapore
| | - Kok Yen Jerry Chan
- KK Women's and Children's Hospital, Singapore, Singapore
- Duke NUS Medical School, Singapore, Singapore
| | - Shiao-Yng Chan
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- National University Hospital, Singapore, Singapore
| | | | - Nikita Rane
- Institute of Mental Health,Singapore, Singapore
| | | | | | - Mei Han
- Computational Biology, National University of Singapore, Singapore, Singapore
| | - Michael Meaney
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Dennis Wang
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Jussi Keppo
- Computational Biology, National University of Singapore, Singapore, Singapore
| | - Geoffrey Chern-Yee Tan
- Computational Biology, National University of Singapore, Singapore, Singapore
- Institute of Mental Health,Singapore, Singapore
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Tan GCY, Wang Z, Tan ESE, Ong RJM, Ooi PE, Lee D, Rane N, Tey SYX, Chua SY, Goh N, Lam GW, Chakraborty A, Yew AKL, Ong SK, Kee JL, Lim XY, Hashim N, Lu SH, Meany M, Tolomeo S, Lee CA, Tan HM, Keppo J. Transdiagnostic clustering of self-schema from self-referential judgements identifies subtypes of healthy personality and depression. Front Neuroinform 2024; 17:1244347. [PMID: 38274390 PMCID: PMC10808829 DOI: 10.3389/fninf.2023.1244347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 11/06/2023] [Indexed: 01/27/2024] Open
Abstract
Introduction The heterogeneity of depressive and anxiety disorders complicates clinical management as it may account for differences in trajectory and treatment response. Self-schemas, which can be determined by Self-Referential Judgements (SRJs), are heterogeneous yet stable. SRJs have been used to characterize personality in the general population and shown to be prognostic in depressive and anxiety disorders. Methods In this study, we used SRJs from a Self-Referential Encoding Task (SRET) to identify clusters from a clinical sample of 119 patients recruited from the Institute of Mental Health presenting with depressive or anxiety symptoms and a non-clinical sample of 115 healthy adults. The generated clusters were examined in terms of most endorsed words, cross-sample correspondence, association with depressive symptoms and the Depressive Experiences Questionnaire and diagnostic category. Results We identify a 5-cluster solution in each sample and a 7-cluster solution in the combined sample. When perturbed, metrics such as optimum cluster number, criterion value, likelihood, DBI and CHI remained stable and cluster centers appeared stable when using BIC or ICL as criteria. Top endorsed words in clusters were meaningful across theoretical frameworks from personality, psychodynamic concepts of relatedness and self-definition, and valence in self-referential processing. The clinical clusters were labeled "Neurotic" (C1), "Extraverted" (C2), "Anxious to please" (C3), "Self-critical" (C4), "Conscientious" (C5). The non-clinical clusters were labeled "Self-confident" (N1), "Low endorsement" (N2), "Non-neurotic" (N3), "Neurotic" (N4), "High endorsement" (N5). The combined clusters were labeled "Self-confident" (NC1), "Externalising" (NC2), "Neurotic" (NC3), "Secure" (NC4), "Low endorsement" (NC5), "High endorsement" (NC6), "Self-critical" (NC7). Cluster differences were observed in endorsement of positive and negative words, latency biases, recall biases, depressive symptoms, frequency of depressive disorders and self-criticism. Discussion Overall, clusters endorsing more negative words tended to endorse fewer positive words, showed more negative biases in reaction time and negative recall bias, reported more severe depressive symptoms and a higher frequency of depressive disorders and more self-criticism in the clinical population. SRJ-based clustering represents a novel transdiagnostic framework for subgrouping patients with depressive and anxiety symptoms that may support the future translation of the science of self-referential processing, personality and psychodynamic concepts of self-definition to clinical applications.
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Affiliation(s)
| | | | | | - Rachel Jing Min Ong
- Faculty of Social Sciences, National University of Singapore, Singapore, Singapore
| | - Pei En Ooi
- School of Biological Sciences, National Technological University, Singapore, Singapore
| | - Danan Lee
- Yale-NUS College, Singapore, Singapore
| | - Nikita Rane
- Institute of Mental Health, Singapore, Singapore
| | | | - Si Ying Chua
- Institute of Mental Health, Singapore, Singapore
| | | | | | - Atlanta Chakraborty
- Institute of Operations Research and Analytics, National University of Singapore, Singapore, Singapore
| | - Anthony Khye Loong Yew
- Institute of Operations Research and Analytics, National University of Singapore, Singapore, Singapore
| | | | | | - Xin Ying Lim
- Faculty of Social Sciences, National University of Singapore, Singapore, Singapore
| | - Nawal Hashim
- Institute of Mental Health, Singapore, Singapore
| | | | - Michael Meany
- Singapore Institute for Clinical Sciences, A*STAR, Singapore, Singapore
| | - Serenella Tolomeo
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | | | - Hong Ming Tan
- Institute of Operations Research and Analytics, National University of Singapore, Singapore, Singapore
| | - Jussi Keppo
- Institute of Operations Research and Analytics, National University of Singapore, Singapore, Singapore
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Tan HM, Lall AC, Keppo J, Chen SL. Evaluation of a new antiresistic strategy to manage antibiotic resistance. J Glob Antimicrob Resist 2023:S2213-7165(23)00049-8. [PMID: 37019210 DOI: 10.1016/j.jgar.2023.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 03/01/2023] [Accepted: 03/07/2023] [Indexed: 04/05/2023] Open
Abstract
OBJECTIVES Systemic strategies for combating antimicrobial resistance (AMR) currently focus on limiting antibiotic use and have been generally insufficient in preventing the rise of AMR. Additionally, they often generate other adverse incentives, such as discouraging pharmaceutical companies from investing in research and development (R&D) of new antibiotics, further exacerbating the problem. This paper proposes a novel systemic strategy for tackling AMR, which we term "antiresistics": any intervention (whether a small molecule, genetic element, phage, or whole organism) that reduces resistance rates in pathogen populations. A prime example of an antiresistic would be a small molecule that specifically disrupts the maintenance of antibiotic resistance plasmids. Of note, an antiresistic would be expected to have a population-level effect and not necessarily be useful on a time scale relevant to individual patients. METHODS We developed a mathematical model to assess the impact of antiresistics on population resistance levels and calibrated it to longitudinal data was available at the country level. We also estimated potential impacts on idealized rates for introduction of new antibiotics. RESULTS The model shows that greater use of antiresistics allows for greater usage of existing antibiotics. This leads to an ability to maintain a constant overall rate of antibiotic efficacy with a slower rate of developing new antibiotics; alternatively, antiresistics has a positive benefit on the effective lifetime and thus profitability of antibiotics. CONCLUSIONS By directly reducing resistance rates, antiresistics can provide clear qualitative benefits (which may be quantitatively large) in terms of existing antibiotic efficacy, longevity,and alignment of incentives.
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Keppo J, Shumway T, Weagley D. Are Monthly Market Returns Predictable? Rev Asset Pricing Stud 2021; 11:806-836. [PMID: 34812276 PMCID: PMC8601754 DOI: 10.1093/rapstu/raab010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Indexed: 06/13/2023]
Abstract
We document significant persistence in the market timing performance of active individual investors, suggesting that some investors are skilled at timing. Using data on all trades by active Finnish individual investors over almost 15 years, we also show that the net purchases of skilled versus unskilled investors predict monthly market returns. Our results lend credibility to the view that market returns are predictable, without having to specify which variables active investors use to successfully time the market. (JEL G10, G11, G12, G14, G15).
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Abdulla A, Wang B, Qian F, Kee T, Blasiak A, Ong YH, Hooi L, Parekh F, Soriano R, Olinger GG, Keppo J, Hardesty CL, Chow EK, Ho D, Ding X. Project IDentif.AI: Harnessing Artificial Intelligence to Rapidly Optimize Combination Therapy Development for Infectious Disease Intervention. Adv Ther (Weinh) 2020; 3:2000034. [PMID: 32838027 PMCID: PMC7235487 DOI: 10.1002/adtp.202000034] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2020] [Indexed: 12/24/2022]
Abstract
In 2019/2020, the emergence of coronavirus disease 2019 (COVID‐19) resulted in rapid increases in infection rates as well as patient mortality. Treatment options addressing COVID‐19 included drug repurposing, investigational therapies such as remdesivir, and vaccine development. Combination therapy based on drug repurposing is among the most widely pursued of these efforts. Multi‐drug regimens are traditionally designed by selecting drugs based on their mechanism of action. This is followed by dose‐finding to achieve drug synergy. This approach is widely‐used for drug development and repurposing. Realizing synergistic combinations, however, is a substantially different outcome compared to globally optimizing combination therapy, which realizes the best possible treatment outcome by a set of candidate therapies and doses toward a disease indication. To address this challenge, the results of Project IDentif.AI (Identifying Infectious Disease Combination Therapy with Artificial Intelligence) are reported. An AI‐based platform is used to interrogate a massive 12 drug/dose parameter space, rapidly identifying actionable combination therapies that optimally inhibit A549 lung cell infection by vesicular stomatitis virus within three days of project start. Importantly, a sevenfold difference in efficacy is observed between the top‐ranked combination being optimally and sub‐optimally dosed, demonstrating the critical importance of ideal drug and dose identification. This platform is disease indication and disease mechanism‐agnostic, and potentially applicable to the systematic N‐of‐1 and population‐wide design of highly efficacious and tolerable clinical regimens. This work also discusses key factors ranging from healthcare economics to global health policy that may serve to drive the broader deployment of this platform to address COVID‐19 and future pandemics.
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Affiliation(s)
- Aynur Abdulla
- Institute for Personalized Medicine School of Biomedical Engineering Shanghai Jiao Tong University Shanghai 200030 China
| | - Boqian Wang
- Institute for Personalized Medicine School of Biomedical Engineering Shanghai Jiao Tong University Shanghai 200030 China
| | - Feng Qian
- Ministry of Education Key Laboratory of Contemporary Anthropology Human Phenome Institute School of Life Sciences Fudan University Shanghai 200438 China
| | - Theodore Kee
- The N.1 Institute for Health (N.1) National University of Singapore Singapore 117456 Singapore.,The Institute for Digital Medicine (WisDM) Yong Loo Lin School of Medicine National University of Singapore Singapore 11756 Singapore.,Department of Biomedical Engineering NUS Engineering National University of Singapore Singapore 117583 Singapore
| | - Agata Blasiak
- The N.1 Institute for Health (N.1) National University of Singapore Singapore 117456 Singapore.,The Institute for Digital Medicine (WisDM) Yong Loo Lin School of Medicine National University of Singapore Singapore 11756 Singapore.,Department of Biomedical Engineering NUS Engineering National University of Singapore Singapore 117583 Singapore
| | - Yoong Hun Ong
- The N.1 Institute for Health (N.1) National University of Singapore Singapore 117456 Singapore
| | - Lissa Hooi
- Cancer Science Institute of Singapore National University of Singapore Singapore 117599 Singapore
| | | | | | - Gene G Olinger
- Global Health Surveillance and Diagnostic Division MRIGlobal Gaithersburg MD 20878 USA.,Boston University School of Medicine Division of Infectious Diseases Boston MA 02118 USA
| | - Jussi Keppo
- NUS Business School and Institute of Operations Research and Analytics National University of Singapore Singapore 119245 Singapore
| | - Chris L Hardesty
- KPMG Global Health and Life Sciences Centre of Excellence Singapore 048581 Singapore
| | - Edward K Chow
- The N.1 Institute for Health (N.1) National University of Singapore Singapore 117456 Singapore.,Cancer Science Institute of Singapore National University of Singapore Singapore 117599 Singapore.,Department of Pharmacology Yong Loo Lin School of Medicine National University of Singapore Singapore 117600 Singapore
| | - Dean Ho
- The N.1 Institute for Health (N.1) National University of Singapore Singapore 117456 Singapore.,The Institute for Digital Medicine (WisDM) Yong Loo Lin School of Medicine National University of Singapore Singapore 11756 Singapore.,Department of Biomedical Engineering NUS Engineering National University of Singapore Singapore 117583 Singapore.,Department of Pharmacology Yong Loo Lin School of Medicine National University of Singapore Singapore 117600 Singapore
| | - Xianting Ding
- Institute for Personalized Medicine School of Biomedical Engineering Shanghai Jiao Tong University Shanghai 200030 China
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