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Article ; Online: Overlapping group screening for binary cancer classification with TCGA high-dimensional genomic data.

Wang, Jie-Huei / Chen, Yi-Hau

Journal of bioinformatics and computational biology

2023 Volume 21, Issue 3, Page(s) 2350013

Abstract: Precision medicine has been a global trend of medical development, wherein cancer diagnosis plays an important role. With accurate diagnosis of cancer, we can provide patients with appropriate medical treatments for improving patients' survival. Since ... ...

Abstract	Precision medicine has been a global trend of medical development, wherein cancer diagnosis plays an important role. With accurate diagnosis of cancer, we can provide patients with appropriate medical treatments for improving patients' survival. Since disease developments involve complex interplay among multiple factors such as gene-gene interactions, cancer classifications based on microarray gene expression profiling data are expected to be effective, and hence, have attracted extensive attention in computational biology and medicine. However, when using genomic data to build a diagnostic model, there exist several problems to be overcome, including the high-dimensional feature space and feature contamination. In this paper, we propose using the overlapping group screening (OGS) approach to build an accurate cancer diagnosis model and predict the probability of a patient falling into some disease classification category in the logistic regression framework. This new proposal integrates gene pathway information into the procedure for identifying genes and gene-gene interactions associated with the classification of cancer outcome groups. We conduct a series of simulation studies to compare the predictive accuracy of our proposed method for cancer diagnosis with some existing machine learning methods, and find the better performances of the former method. We apply the proposed method to the genomic data of The Cancer Genome Atlas related to lung adenocarcinoma (LUAD), liver hepatocellular carcinoma (LHC), and thyroid carcinoma (THCA), to establish accurate cancer diagnosis models.
MeSH term(s)	Humans ; Early Detection of Cancer ; Gene Expression Profiling/methods ; Genomics ; Computer Simulation ; Neoplasms/genetics
Language	English
Publishing date	2023-06-22
Publishing country	Singapore
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2115015-1
ISSN	1757-6334 ; 0219-7200
ISSN (online)	1757-6334
ISSN	0219-7200
DOI	10.1142/S0219720023500130
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Analyzing recurrent and nonrecurrent terminal events data in discrete time.

Wen, Chi-Chung / Chen, Yi-Hau

Biometrical journal. Biometrische Zeitschrift

2022 Volume 65, Issue 3, Page(s) e2100361

Abstract: Joint analysis of recurrent and nonrecurrent terminal events has attracted substantial attention in literature. However, there lacks formal methodology for such analysis when the event time data are on discrete scales, even though some modeling and ... ...

Abstract	Joint analysis of recurrent and nonrecurrent terminal events has attracted substantial attention in literature. However, there lacks formal methodology for such analysis when the event time data are on discrete scales, even though some modeling and inference strategies have been developed for discrete-time survival analysis. We propose a discrete-time joint modeling approach for the analysis of recurrent and terminal events where the two types of events may be correlated with each other. The proposed joint modeling assumes a shared frailty to account for the dependence among recurrent events and between the recurrent and the terminal terminal events. Also, the joint modeling allows for time-dependent covariates and rich families of transformation models for the recurrent and terminal events. A major advantage of our approach is that it does not assume a distribution for the frailty, nor does it assume a Poisson process for the analysis of the recurrent event. The utility of the proposed analysis is illustrated by simulation studies and two real applications, where the application to the biochemists' rank promotion data jointly analyzes the biochemists' citation numbers and times to rank promotion, and the application to the scleroderma lung study data jointly analyzes the adverse events and off-drug time among patients with the symptomatic scleroderma-related interstitial lung disease.
MeSH term(s)	Humans ; Models, Statistical ; Frailty ; Recurrence ; Computer Simulation ; Survival Analysis
Language	English
Publishing date	2022-10-26
Publishing country	Germany
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	131640-0
ISSN	1521-4036 ; 0323-3847 ; 0006-3452
ISSN (online)	1521-4036
ISSN	0323-3847 ; 0006-3452
DOI	10.1002/bimj.202100361
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Article ; Online: microRNA Biomarkers in Clinical Study.

Wang, Hsiuying / Chen, Yi-Hau

Biomolecules

2021 Volume 11, Issue 12

Abstract: MicroRNAs (miRNAs), short non-coding RNAs, are involved in the initiation and progression of many human diseases that also play a key role in immune response and drug metabolism modulation [ ... ]. ...

Abstract	MicroRNAs (miRNAs), short non-coding RNAs, are involved in the initiation and progression of many human diseases that also play a key role in immune response and drug metabolism modulation [...].
MeSH term(s)	Biomarkers ; Cognition ; Gene Expression Regulation, Neoplastic
Chemical Substances	Biomarkers
Language	English
Publishing date	2021-12-02
Publishing country	Switzerland
Document type	Editorial
ZDB-ID	2701262-1
ISSN	2218-273X ; 2218-273X
ISSN (online)	2218-273X
ISSN	2218-273X
DOI	10.3390/biom11121810
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Article ; Online: Network-adjusted Kendall's Tau Measure for Feature Screening with Application to High-dimensional Survival Genomic Data.

Wang, Jie-Huei / Chen, Yi-Hau

Bioinformatics (Oxford, England)

2021 Volume 37, Issue 15, Page(s) 2150–2156

Abstract: Motivation: In high-dimensional genetic/genomic data, the identification of genes related to clinical survival trait is a challenging and important issue. In particular, right-censored survival outcomes and contaminated biomarker data make the relevant ... ...

Abstract	Motivation: In high-dimensional genetic/genomic data, the identification of genes related to clinical survival trait is a challenging and important issue. In particular, right-censored survival outcomes and contaminated biomarker data make the relevant feature screening difficult. Several independence screening methods have been developed, but they fail to account for gene-gene dependency information, and may be sensitive to outlying feature data. Results: We improve the inverse probability-of-censoring weighted (IPCW) Kendall's tau statistic by using Google's PageRank Markov matrix to incorporate feature dependency network information. Also, to tackle outlying feature data, the nonparanormal approach transforming the feature data to multivariate normal variates are utilized in the graphical lasso procedure to estimate the network structure in feature data. Simulation studies under various scenarios show that the proposed network-adjusted weighted Kendall's tau approach leads to more accurate feature selection and survival prediction than the methods without accounting for feature dependency network information and outlying feature data. The applications on the clinical survival outcome data of diffuse large B-cell lymphoma and of The Cancer Genome Atlas lung adenocarcinoma patients demonstrate clearly the advantages of the new proposal over the alternative methods. Supplementary information: Supplementary data are available at Bioinformatics online.
Language	English
Publishing date	2021-02-01
Publishing country	England
Document type	Journal Article
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btab064
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Article ; Online: Overlapping group screening for detection of gene-environment interactions with application to TCGA high-dimensional survival genomic data.

Wang, Jie-Huei / Wang, Kang-Hsin / Chen, Yi-Hau

BMC bioinformatics

2022 Volume 23, Issue 1, Page(s) 202

Abstract: Background: In the context of biomedical and epidemiological research, gene-environment (G-E) interaction is of great significance to the etiology and progression of many complex diseases. In high-dimensional genetic data, two general models, marginal ... ...

Abstract	Background: In the context of biomedical and epidemiological research, gene-environment (G-E) interaction is of great significance to the etiology and progression of many complex diseases. In high-dimensional genetic data, two general models, marginal and joint models, are proposed to identify important interaction factors. Most existing approaches for identifying G-E interactions are limited owing to the lack of robustness to outliers/contamination in response and predictor data. In particular, right-censored survival outcomes make the associated feature screening even challenging. In this article, we utilize the overlapping group screening (OGS) approach to select important G-E interactions related to clinical survival outcomes by incorporating the gene pathway information under a joint modeling framework. Results: Simulation studies under various scenarios are carried out to compare the performances of our proposed method with some commonly used methods. In the real data applications, we use our proposed method to identify G-E interactions related to the clinical survival outcomes of patients with head and neck squamous cell carcinoma, and esophageal carcinoma in The Cancer Genome Atlas clinical survival genetic data, and further establish corresponding survival prediction models. Both simulation and real data studies show that our method performs well and outperforms existing methods in the G-E interaction selection, effect estimation, and survival prediction accuracy. Conclusions: The OGS approach is useful for selecting important environmental factors, genes and G-E interactions in the ultra-high dimensional feature space. The prediction ability of OGS with the Lasso penalty is better than existing methods. The same idea of the OGS approach can apply to other outcome models, such as the proportional odds survival time model, the logistic regression model for binary outcomes, and the multinomial logistic regression model for multi-class outcomes.
MeSH term(s)	Computer Simulation ; Gene-Environment Interaction ; Genomics ; Humans ; Neoplasms/genetics ; Research
Language	English
Publishing date	2022-05-30
Publishing country	England
Document type	Journal Article
ZDB-ID	2041484-5
ISSN	1471-2105 ; 1471-2105
ISSN (online)	1471-2105
ISSN	1471-2105
DOI	10.1186/s12859-022-04750-7
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Article ; Online: The Association between Depression and Gastroesophageal Reflux based on Phylogenetic Analysis of miRNA Biomarkers.

Chen, Yi-Hau / Wang, Hsiuying

Current medicinal chemistry

2020 Volume 27, Issue 38, Page(s) 6536–6547

Abstract: A number of clinical studies have revealed that there is an association between major depression (MD) and gastroesophageal reflux disease (GERD). Both the diseases are shown to affect a large proportion of the global population. More advanced studies for ...

Abstract	A number of clinical studies have revealed that there is an association between major depression (MD) and gastroesophageal reflux disease (GERD). Both the diseases are shown to affect a large proportion of the global population. More advanced studies for understanding the comorbidity mechanism of these two diseases can shed light on developing new therapies of both diseases. To the best of our knowledge, there has not been any research work in the literature investigating the relationship between MD and GERD using their miRNA biomarkers. We adopt a phylogenetic analysis to analyze their miRNA biomarkers. From our analyzed results, the association between these two diseases can be explored through miRNA phylogeny. In addition to evidence from the phylogenetic analysis, we also demonstrate epidemiological evidence for the relationship between MD and GERD based on Taiwan biobank data.
MeSH term(s)	Biomarkers ; Depression ; Gastroesophageal Reflux/epidemiology ; Humans ; MicroRNAs/genetics ; Phylogeny
Chemical Substances	Biomarkers ; MicroRNAs
Language	English
Publishing date	2020-04-22
Publishing country	United Arab Emirates
Document type	Journal Article
ZDB-ID	1319315-6
ISSN	1875-533X ; 0929-8673
ISSN (online)	1875-533X
ISSN	0929-8673
DOI	10.2174/0929867327666200425214906
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Article: Exploring Diversity of COVID‑19 Based on Substitution Distance.

Chen, Yi-Hau / Wang, Hsiuying

Infection and drug resistance

2020 Volume 13, Page(s) 3887–3894

Abstract: Background: The number of COVID-19 infections worldwide has reached 10 million. COVID‑19 caused by SARS-CoV-2 is more contagious than SARS-CoV-1. There is a dispute about the origin of COVID-19. Study results showed that all SARS-CoV-2 sequences around ... ...

Abstract	Background: The number of COVID-19 infections worldwide has reached 10 million. COVID‑19 caused by SARS-CoV-2 is more contagious than SARS-CoV-1. There is a dispute about the origin of COVID-19. Study results showed that all SARS-CoV-2 sequences around the world share a common ancestor towards the end of 2019. Methods: Virus sequences from COVID-19 samples at the early time should be less diversifiable than those from samples at the later time because there might be more mutations when the virus evolutes over time. The diversity of virus nucleotide sequences can be measured by the nucleotide substitution distance. To explore the diversity of SARS-CoV-2, we use different nucleotide substitution models to calculate the distances of SARS-CoV-2 samples from 3 different areas, China, Europe, and the USA. Then, we use these distances to infer the origin of COVID-19. Results: It is known that COVID-19 originated in Wuhan China and then spread to Europe and the USA. By using different substitution models, the distances of SARS-CoV-2 samples from these areas are significantly different. By ANOVA testing, the p-value is less than 2.2e-16. The analyzed results in most substitution models show that China has the lowest diversity, followed by Europe and lastly by the USA. This outcome coincides with the virus transmission time order that SARS-CoV-2 starts in China, then outbreaks in Europe and finally in the USA. Conclusion: The magnitude of nucleotide substitution distance of SARS-CoV-2 is closely related to the transmission time order of SARS-CoV-2. This outcome reveals that the nucleotide substitution distance of SARS-CoV-2 may be used to infer the origin of COVID-19.
Keywords	covid19
Language	English
Publishing date	2020-10-29
Publishing country	New Zealand
Document type	Journal Article
ZDB-ID	2494856-1
ISSN	1178-6973
ISSN	1178-6973
DOI	10.2147/IDR.S277620
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Article ; Online: Discrete-time survival data with longitudinal covariates.

Wen, Chi-Chung / Chen, Yi-Hau

Statistics in medicine

2020 Volume 39, Issue 29, Page(s) 4372–4385

Abstract: Survival analysis has been conventionally performed on a continuous time scale. In practice, the survival time is often recorded or handled on a discrete scale; when this is the case, the discrete-time survival analysis would provide analysis results ... ...

Abstract	Survival analysis has been conventionally performed on a continuous time scale. In practice, the survival time is often recorded or handled on a discrete scale; when this is the case, the discrete-time survival analysis would provide analysis results more relevant to the actual data scale. Besides, data on time-dependent covariates in the survival analysis are usually collected through intermittent follow-ups, resulting in the missing and mismeasured covariate data. In this work, we propose the sufficient discrete hazard (SDH) approach to discrete-time survival analysis with longitudinal covariates that are subject to missingness and mismeasurement. The SDH method employs the conditional score idea available for dealing with mismeasured covariates, and the penalized least squares for estimating the missing covariate value using the regression spline basis. The SDH method is developed for the single event analysis with the logistic discrete hazard model, and for the competing risks analysis with the multinomial logit model. Simulation results revel good finite-sample performances of the proposed estimator and the associated asymptotic theory. The proposed SDH method is applied to the scleroderma lung study data, where the time to medication withdrawal and time to death were recorded discretely in months, for illustration.
MeSH term(s)	Computer Simulation ; Humans ; Proportional Hazards Models ; Research Design ; Risk Assessment ; Survival Analysis
Language	English
Publishing date	2020-09-01
Publishing country	England
Document type	Journal Article
ZDB-ID	843037-8
ISSN	1097-0258 ; 0277-6715
ISSN (online)	1097-0258
ISSN	0277-6715
DOI	10.1002/sim.8729
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Article ; Online: The Association between Migraine and Depression based on miRNA Biomarkers and Cohort Studies.

Chen, Yi-Hau / Wang, Hsiuying

Current medicinal chemistry

2020 Volume 28, Issue 27, Page(s) 5648–5656

Abstract: Background: An association between migraine and Major Depression (MD) has been revealed in a number of clinical studies. Both diseases have affected a large global population. More understanding of the comorbidity mechanism of these two diseases can ... ...

Abstract	Background: An association between migraine and Major Depression (MD) has been revealed in a number of clinical studies. Both diseases have affected a large global population. More understanding of the comorbidity mechanism of these two diseases can shed light on developing new therapies for their treatment. Methods: To the best of our knowledge, there have not been any researches in the literature based on microRNA (miRNA) biomarkers to investigate the relationship between MD and migraine. In this study, we have discussed the association between these two diseases based on their miRNA biomarkers. In addition to miRNA biomarkers, we have also demonstrated epidemiological evidence for their association based on Taiwan Biobank (TWB) data. Results: Among the 12 migraine miRNA biomarkers, 11 are related to MD. Only miR-181a has no direct evidence to be involved in the mechanism of MD. In addition to the biological biomarker evidence, the statistical analysis using the large-scale epidemiologic data collected from TWB provides strong evidence on the relationship between MD and migraine. Conclusion: The evidence based on both molecular and epidemiological data reveals the significant association between MD and migraine. This result can help investigate the correlated underlying mechanism of these two diseases.
MeSH term(s)	Biomarkers ; Cohort Studies ; Comorbidity ; Depression ; Depressive Disorder, Major/epidemiology ; Depressive Disorder, Major/genetics ; Humans ; MicroRNAs/genetics ; Migraine Disorders/epidemiology ; Migraine Disorders/genetics
Chemical Substances	Biomarkers ; MicroRNAs
Language	English
Publishing date	2020-11-18
Publishing country	United Arab Emirates
Document type	Journal Article
ZDB-ID	1319315-6
ISSN	1875-533X ; 0929-8673
ISSN (online)	1875-533X
ISSN	0929-8673
DOI	10.2174/0929867327666201117100026
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Article ; Online: Interaction screening by Kendall's partial correlation for ultrahigh-dimensional data with survival trait.

Wang, Jie-Huei / Chen, Yi-Hau

Bioinformatics (Oxford, England)

2020 Volume 36, Issue 9, Page(s) 2763–2769

Abstract: Motivation: In gene expression and genome-wide association studies, the identification of interaction effects is an important and challenging issue owing to its ultrahigh-dimensional nature. In particular, contaminated data and right-censored survival ... ...

Abstract	Motivation: In gene expression and genome-wide association studies, the identification of interaction effects is an important and challenging issue owing to its ultrahigh-dimensional nature. In particular, contaminated data and right-censored survival outcome make the associated feature screening even challenging. Results: In this article, we propose an inverse probability-of-censoring weighted Kendall's tau statistic to measure association of a survival trait with biomarkers, as well as a Kendall's partial correlation statistic to measure the relationship of a survival trait with an interaction variable conditional on the main effects. The Kendall's partial correlation is then used to conduct interaction screening. Simulation studies under various scenarios are performed to compare the performance of our proposal with some commonly available methods. In the real data application, we utilize our proposed method to identify epistasis associated with the clinical survival outcomes of non-small-cell lung cancer, diffuse large B-cell lymphoma and lung adenocarcinoma patients. Both simulation and real data studies demonstrate that our method performs well and outperforms existing methods in identifying main and interaction biomarkers. Availability and implementation: R-package 'IPCWK' is available to implement this method, together with a reference manual describing how to perform the 'IPCWK' package. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Carcinoma, Non-Small-Cell Lung ; Genome-Wide Association Study ; Humans ; Lung Neoplasms/genetics ; Phenotype
Language	English
Publishing date	2020-01-11
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btaa017
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