| scholarly article | Q13442814 |
| P2093 | author name string | Po-Hsiu Kuo | |
| Yu-Li Liu | |||
| Albert C Yang | |||
| Shih-Jen Tsai | |||
| Younger W-Y Yu | |||
| Eugene Lin | |||
| P2860 | cites work | A flexible and accurate genotype imputation method for the next generation of genome-wide association studies | Q21129496 |
| Genome-wide association study of bipolar disorder in Canadian and UK populations corroborates disease loci including SYNE1 and CSMD1 | Q21261478 | ||
| Synaptic P-Rex1 signaling regulates hippocampal long-term depression and autism-like social behavior | Q27320047 | ||
| Genome-wide association scan of trait depression | Q28943415 | ||
| The Path to Personalized Medicine | Q29541502 | ||
| Gene expression inference with deep learning. | Q30354138 | ||
| A generalized combinatorial approach for detecting gene-by-gene and gene-by-environment interactions with application to nicotine dependence | Q30443642 | ||
| Suicidal behavior in the context of disrupted rhythmicity in bipolar disorder--data from an association study of suicide attempts with clock genes | Q30901577 | ||
| Machine learning and systems genomics approaches for multi-omics data | Q31157242 | ||
| A comparison of classification methods for predicting Chronic Fatigue Syndrome based on genetic data | Q33505815 | ||
| Duplication of the SLIT3 locus on 5q35.1 predisposes to major depressive disorder | Q33769815 | ||
| Bipolar disorder with comorbid binge eating history: a genome-wide association study implicates APOB. | Q34471534 | ||
| The International SSRI Pharmacogenomics Consortium (ISPC): a genome-wide association study of antidepressant treatment response. | Q35715254 | ||
| Genome-Wide Association Study for Autism Spectrum Disorder in Taiwanese Han Population | Q35784947 | ||
| Exploring the associations between genetic variants in genes encoding for subunits of calcium channel and subtypes of bipolar disorder. | Q50657439 | ||
| Identification of 11p14.1-p15.3 deletion probably associated with short stature, relative macrocephaly, and delayed closure of the fontanelles. | Q52971052 | ||
| Stimulated gene expression profiles as a blood marker of major depressive disorder. | Q54428030 | ||
| Ban I Polymorphism of the Cytosolic Phospholipase A 2 Gene and Mood Disorders in the Korean Population | Q60628018 | ||
| Whole-Genome Sequencing Suggests Schizophrenia Risk Mechanisms in Humans with 22q11.2 Deletion Syndrome | Q36245866 | ||
| Pharmacogenetics/genomics and personalized medicine | Q36294685 | ||
| Effects of circadian clock genes and health-related behavior on metabolic syndrome in a Taiwanese population: Evidence from association and interaction analysis | Q36309116 | ||
| Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction | Q36383001 | ||
| Measuring diagnostic and predictive accuracy in disease management: an introduction to receiver operating characteristic (ROC) analysis | Q36437188 | ||
| A clinical risk stratification tool for predicting treatment resistance in major depressive disorder | Q36948219 | ||
| Pharmacogenomics with antidepressants in the STAR*D study | Q37205961 | ||
| Association and interaction of APOA5, BUD13, CETP, LIPA and health-related behavior with metabolic syndrome in a Taiwanese population | Q37403028 | ||
| Personalized health care: from theory to practice | Q37969713 | ||
| Assessing gene-gene interactions in pharmacogenomics | Q37986775 | ||
| Representation learning: a review and new perspectives | Q38115908 | ||
| Genome-wide microarray analysis of gene expression profiling in major depression and antidepressant therapy | Q38364592 | ||
| Genome-wide association studies in pharmacogenomics of antidepressants | Q38444447 | ||
| Predicting healthcare trajectories from medical records: A deep learning approach | Q38730247 | ||
| Genetic predictors of antipsychotic response to lurasidone identified in a genome wide association study and by schizophrenia risk genes | Q38826029 | ||
| The rs1277306 Variant of the REST Gene Confers Susceptibility to Cognitive Aging in an Elderly Taiwanese Population | Q38988668 | ||
| Cross-trial prediction of treatment outcome in depression: a machine learning approach | Q40069166 | ||
| An artificial neural network approach to the drug efficacy of interferon treatments | Q40277321 | ||
| Combining clinical variables to optimize prediction of antidepressant treatment outcomes | Q40794290 | ||
| The combined effect of genetic polymorphisms and clinical parameters on treatment outcome in treatment-resistant depression | Q41225938 | ||
| New suggestive genetic loci and biological pathways for attention function in adult attention-deficit/hyperactivity disorder | Q41586761 | ||
| An Essential Role for the Tetraspanin LHFPL4 in the Cell-Type-Specific Targeting and Clustering of Synaptic GABAA Receptors. | Q42377342 | ||
| Transforming growth factor-β signaling pathway-associated genes SMAD2 and TGFBR2 are implicated in metabolic syndrome in a Taiwanese population | Q42670499 | ||
| CYP1A2 genetic polymorphisms are associated with treatment response to the antidepressant paroxetine. | Q42788954 | ||
| A support vector machine approach to assess drug efficacy of interferon-alpha and ribavirin combination therapy | Q42980284 | ||
| Clock gene variants differentiate mood disorders. | Q45956185 | ||
| BNIP-3: a novel candidate for an intrinsic depression-related factor found in NG108-15 cells treated with Hochu-ekki-to, a traditional oriental medicine, or typical antidepressants | Q46497680 | ||
| Public health. A case study of personalized medicine. | Q46664702 | ||
| A Bayesian approach to gene-gene and gene-environment interactions in chronic fatigue syndrome | Q46683578 | ||
| Clock genes: influencing and being influenced by psychoactive drugs | Q46972542 | ||
| Association between the variability of the ABCA13 gene and the risk of major depressive disorder and schizophrenia in the Han Chinese population | Q47920386 | ||
| A study of single nucleotide polymorphisms of GRIN2B in schizophrenia from Chinese Han population | Q47975059 | ||
| Investigating association of four gene regions (GABRB3, MAOB, PAH, and SLC6A4) with five symptoms in schizophrenia | Q48025093 | ||
| Genome-wide investigation of schizophrenia associated plasma Ndel1 enzyme activity. | Q48690683 | ||
| GWAS-based machine learning approach to predict duloxetine response in major depressive disorder. | Q50177770 | ||
| P275 | copyright license | Creative Commons Attribution 4.0 International | Q20007257 |
| P6216 | copyright status | copyrighted | Q50423863 |
| P921 | main subject | deep learning | Q197536 |
| biomarker | Q864574 | ||
| P304 | page(s) | 290 | |
| P577 | publication date | 2018-07-06 | |
| P1433 | published in | Frontiers in Psychiatry | Q27723495 |
| P1476 | title | A Deep Learning Approach for Predicting Antidepressant Response in Major Depression Using Clinical and Genetic Biomarkers | |
| P478 | volume | 9 |
| Q89747545 | A polygenic predictor of treatment-resistant depression using whole exome sequencing and genome-wide genotyping |
| Q89964981 | C2H2-Type Zinc Finger Proteins in Brain Development, Neurodevelopmental, and Other Neuropsychiatric Disorders: Systematic Literature-Based Analysis |
| Q59136420 | Combination of Genetic Variation and G72 Protein Level to Detect Schizophrenia: Machine Learning Approaches |
| Q96348280 | Deep learning in mental health outcome research: a scoping review |
| Q111517441 | Designing a Deep Learning Hybrid Using CNN and Inception V3 Transfer Learning to Detect the Aggression Level of Deep Obsessive Compulsive Disorder in Children |
| Q92907265 | Epigenetics and Depression: An Update |
| Q92083774 | Genetic Associations between Voltage-Gated Calcium Channels and Psychiatric Disorders |
| Q90698512 | Nell-1 Is a Key Functional Modulator in Osteochondrogenesis and Beyond |
| Q90240601 | Personalized Psychiatry and Depression: The Role of Sociodemographic and Clinical Variables |
| Q92907278 | Pharmacogenetics and Depression: A Critical Perspective |
| Q90224430 | Pharmacogenomics-Driven Prediction of Antidepressant Treatment Outcomes: A Machine-Learning Approach With Multi-trial Replication |
| Q89551935 | Precision Psychiatry Applications with Pharmacogenomics: Artificial Intelligence and Machine Learning Approaches |
| Q59796376 | Precision pharmacotherapy: psychiatry's future direction in preventing, diagnosing, and treating mental disorders |
| Q89579432 | Predicting patient outcomes in psychiatric hospitals with routine data: a machine learning approach |
| Q91883465 | Sensitivity and specificity evaluation of multiple neurodegenerative proteins for Creutzfeldt-Jakob disease diagnosis using a deep-learning approach |
| Q62491494 | Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives |
Search more.