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Analyzing COVID-19 progression with Markov multistage models: insights from a Korean cohort

Genomics and Informatics 2025年第1期23卷 1-11页

作者： Ndagijimana, Frank Aimee Rodrigue Park, Taesung Interdisciplinary Program in Bioinformatics Seoul National University Seoul South Korea Department of Statistics Seoul National University Seoul South Korea

Background: Understanding the progression and recovery process of COVID-19 is crucial for guiding public health strategies and developing targeted interventions. This longitudinal cohort study aims to elucidate the dynamics of COVID-19 severity progression and evaluate the impact of underlying health conditions on these transitions, providing critical insights for more effective disease management. Methods: Data from 4549 COVID-19 patients admitted to Seoul National University Boramae Medical Center between February 5th, 2020, and October 30th, 2021, were analyzed using a 5-state continuous-time Markov multistate model. The model estimated instantaneous transition rates between different levels of COVID-19 severity, predicted probabilities of state transitions, and determined hazard ratios associated with underlying comorbidities. Results: The analysis revealed that most patients stabilized in their initial state, with 72.2% of patients with moderate symptoms remaining moderate. Patients with hypertension had a 67.6% higher risk of progressing from moderate to severe, while those with diabetes had an 89.9% higher risk of deteriorating from severe to critical. Although transition rates to death were low early in hospitalization, these comorbidities significantly increased the likelihood of worsening conditions. Conclusion: This study highlights the utility of continuous-time Markov multistate models in assessing COVID-19 severity progression among hospitalized patients. The findings indicate that patients are more likely to recover than to experience worsening conditions. However, hypertension and diabetes significantly increase the risk of severe outcomes, underscoring the importance of managing these conditions in COVID-19 patients. © The Author(s) 2025.

关键词： Comorbidity COVID-19 progression Markov multistate model Severity state Transition intensity

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Deep learning health space model for ordered responses

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BMC Medical Informatics and Decision Making 2025年第1期25卷 1-10页

作者： Lee, Chanhee Park, Taesung Interdisciplinary Program in Bioinformatics Seoul National University Seoul Korea Department of Statistics Seoul National University Seoul Korea

As personalized medicine becomes more prevalent, the objective measurement and visualization of an individual’s health status are becoming increasingly crucial. However, as the dimensions of data collected from each individual increase, this task becomes more challenging. The Health Space (HS) model provides a statistical framework for visualizing an individual’s health status on biologically meaningful axes. In our previous study, we developed HS models using statistical models such as logistic regression model (LRM) and the proportional odds model (POM). However, these statistical HS models are limited in their ability to accommodate complex non-linear biological relationships. In order to model complex non-linear biological relationship, we developed deep learning HS models. Specifically, we formulated five distinct deep learning HS models: four standard binary deep neural networks (DNNs) for binary outcomes and one deep ordinal neural network (DONN) that accounts for the ordinality of the dependent variable. We trained these models using 32,140 samples from the Korea National Health and Nutrition Examination Survey (KNHANES) and validated them with data from the Ewha-Boramae cohort (862 samples) and the Korea Association Resource (KARE) project (3,199 samples). The proposed deep learning HS models were compared with the existing statistical HS model based on the POM. Deep learning HS model using DONN demonstrated the best performance in discriminating health status in both the training and external datasets. We developed deep learning HS models to capture complex non-linear biological relationships in HS and compared their performance with our previously best-performing statistical HS model. The deep learning HS models show promise as effective tools for objectively and meaningfully visualizing an individual’s health status. Not applicable.

关键词： Biologically interpretable visualization Deep ordinal neural network Health space model

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Modeling the causal regulatory network by integrating chromatin accessibility and transcriptome data

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National Science Review 2016年第2期3卷 240-251页

作者： Yong Wang Rui Jiang Wing Hung Wong Department of Statistics Department of Biomedical Data ScienceBio-X ProgramStanford University Academy of Mathematics and Systems Science National Center for Mathematics and Interdisciplinary SciencesChinese Academy of Sciences MOE Key Laboratory of Bioinformatics Bioinformatics Division and Center for Synthetic and Systems BiologyTNLISTDepartment of AutomationTsinghua University

Cell packs a lot of genetic and regulatory information through a structure known as chromatin,*** is wrapped around histone proteins and is tightly packed in a remarkable *** express a gene in a specific coding region,the chromatin would open up and DNA loop may be formed by interacting enhancers and ***,the mediator and cohesion complexes,sequence-specific transcription factors,and RNA polymerase Ⅱ are recruited and work together to elaborately regulate the expression *** is in pressing need to understand how the information,about when,where,and to what degree genes should be expressed,is embedded into chromatin structure and gene regulatory *** to large consortia such as Encyclopedia of DNA Elements(ENCODE) and Roadmap Epigenomic projects,extensive data on chromatin accessibility and transcript abundance are available across many tissues and cell *** rich data offer an exciting opportunity to model the causal regulatory ***,we will review the current experimental approaches,foundational data,computational problems,interpretive frameworks,and integrative models that will enable the accurate interpretation of regulatory ***,we will discuss the efforts to organize,analyze,model,and integrate the DNA accessibility data,transcriptional data,and functional genomic regions *** believe that these efforts will eventually help us understand the information flow within the cell and will influence research directions across many fields.

关键词： gene regulatory network open chromatin DNA accessibility transcription factor colocalization statistical model data integration

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Uncertainty Quantification and Statistical Inference for Biologically Informed Neural Networks

Uncertainty Quantification and Statistical Inference for Bio...

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2024 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2024

作者： Goo, Taewan Lee, Chanhee Shin, Sangyeon Kim, Haeyoung Park, Taesung Seoul National University Interdisciplinary Program in Bioinformatics Seoul Korea Republic of Seoul National University Department of Statistics Seoul Korea Republic of

ISBN: (纸本)9798350386226

In precision medicine, deep learning models play an important role in identifying relevant biomarkers and making predictions for the early diagnosis and prognosis of diseases. While successful in developing accurate prediction models for prognosis and diagnosis, the 'black box' nature of deep learning poses significant challenges, which makes it difficult to have a meaningful biological interpretation. Recently, several biologically informed neural networks (BINNs), such as the Visible Neural Network and P-NET, have been proposed to incorporate biological knowledge for improved interpretation. These models assess node importance using quantitative measures such as SHAP and DeepLIFT. However, these approaches have limitations because they typically provide a single estimate of node importance without any information on the uncertainty of these estimates. This study aims to assess uncertainty of node importance through a novel statistical framework. Bootstrapping is used to calculate confidence interval of node importance. Our findings validate that the proposed statistical framework quantifies the uncertainty of nodes in BINNs. By providing robust confidence intervals of pathways and genes, this framework enhances the interpretability and reliability of BINNs. © 2024 IEEE.

关键词： Neural network models

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Confidence Interval Estimation for Machine Learning Models in Forecasting Infectious Diseases

Confidence Interval Estimation for Machine Learning Models i...

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2024 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2024

作者： Goo, Taewan Han, Kyulhee Song, Hanbyul Park, Jiwon Liu, Zhe Oh, Jooha Jose, Sayooj Aby Park, Taesung Seoul National University Interdisciplinary Program in Bioinformatics Seoul Korea Republic of Seoul National University Department of Statistics Seoul Korea Republic of

ISBN: (纸本)9798350386226

Forecasting models have been instrumental in managing the COVID-19 pandemic by facilitating efficient resource distribution and proper interventions. Although various forecasting models - such as mathematical models, statistical models, and machine learning models - are available, a few models account for the uncertainty in their predictions. The lack of uncertainty information constrains the reliability of forecasting results, making them less applicable for use in decision-making. Confidence Intervals (CIs) are widely used in statistical inference to provide uncertainty information. In this study, we introduce a framework that provides bootstrap-based CIs readily applicable to various forecasting models. The key concept of this framework is repeated training on bootstrap data based on resampled residuals from the forecasting model. After fitting the forecasting model, bootstrap datasets are generated considering time series characteristics to retrain the forecasting models. Then, CIs are calculated based on the distribution of prediction values. This framework was applied to forecasting COVID-19 outcomes - such as daily confirmed COVID-19 cases, deaths, and ICU patients - in South Korea. Our results demonstrate that bootstrap-based CIs can be successfully applied to provide additional information on the reliability of prediction. In conclusion, our framework can provide uncertainty information without requiring complex assumptions, regardless of the forecasting model or response variable type. Therefore, this approach can support public health policymakers in gaining a deeper understanding of epidemic trends, thereby offering valuable insights for decision-making. © 2024 IEEE.

关键词： COVID-19

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Genome-wide association analysis with matched samples discloses additional novel risk loci

Genome-wide association analysis with matched samples disclo...

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IEEE International Conference on bioinformatics and Biomedicine

作者： Jungsoo Gim Sungkyoung Choi Jongho Im Jae-Kwang Kim Taesung Park Interdisciplinary program for Bioinformatics and Department of Statistics Seoul National University Interdisciplinary program for Bioinformatics Seoul National University Department of Statistics Iowa State University

ISBN: (纸本)9781479956708

Genome-wide association studies have identified many candidate causal variants associated with common complex diseases and traits, but most of them have been drawn from nonrandomized case/control designs. In nonrandomized experiments, the results drawn from two different groups can be misleading because the units exposed to one group generally differ systematically from the units exposed to the other group. Propensity score is widely used to group case and control units for a more direct and significant comparison even with nonrandomized experiments. This propensity score matching can help with prioritizing additional uncovered variants on disease risk via sub-group analysis in genome-wide association studies. The aim of this work is to propose a post-hoc association test based on the subsets of samples. For that purpose, this paper presents a new paradigm for a post-hoc genome-wide association test when the sample size of controls are larger than that of cases: selecting control samples by equating the distribution of covariates in the case and control groups and re-performing association analysis upon these matched samples. We demonstrated the feasibility of this approach by applying it to 2752 type II diabetes patients in 8842 Korean population. Genome-wide association approach with matched samples is able to disclose 9 additional novel variants and 7 out of 9 have not identified from the association test of whole control samples. The process described here can successfully be combined with other types of case/control studies with large covariate information. This indicates that there a possibility of obtaining additional candidate causal variants responsible for common diseases through genome-wide association analysis with matched samples.

关键词： Propensity score Matching Genome-wide association study

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A normalization method for multiple reaction monitoring (MRM) data

A normalization method for multiple reaction monitoring (MRM...

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IEEE International Conference on bioinformatics and Biomedicine

作者： Jungsoo Gim Taesung Park Interdisciplinary Program for Bioinformatics and Department of Statistics Seoul National University

ISBN: (纸本)9781479956708

High accuracy and reproducibility provided by selected or multiple reaction monitoring (SRM/MRM) experiments suggest that it is likely to become the platform of choice for identifying and verifying candidate biomarkers. Although several methods have been developed to quantify and discover the significant changes in SRM/MRM expression, we show normalization continues to be an essential step in the differential expression analysis. Here, we first investigate what makes normalization across the samples difficult in SRM assay. Then, we suggest a simple but effective normalization method to overcome the difficulty. Both real and simulated data sets are examined and improved results, compared to an existing method, are shown for inferring differential expression.

关键词： Proteomics Selected reaction monitoring (SRM) Normalization Differential expression test

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Elastic restricted Boltzmann machines for cancer data analysis

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Frontiers of Electrical and Electronic Engineering in China 2017年第2期5卷 159-172页

作者： Sai Zhang Muxuan Liang Zhongjun Zhou Chen Zhang Ning Chen Ting Chen Jianyang Zeng Institute for Interdisciplinary Information Sciences Tsinghua University Beijing 100084 China Department of Statistics University of Wisconsin-Madison Madison WI 53706-1685 USA Bioinformatics Division TNLIST Department of Computer Science and Technology Tsinghua University Beijing 100084 China Program in Computational Biology and Bioinformatics Univesity of Southern California Los Angeles CA 90089 USA

Background： Restricted Boltzmann machines （RBMs） are endowed with the universal power of modeling （binary） joint distributions. Meanwhile, as a result of their confining network structure, training RBMs confronts less difficulties when dealing with approximation and inference issues. But little work has been developed to fully exploit the capacity of these models to analyze cancer data, e.g., cancer genomic, transcriptomic, proteomic and epigenomic data. On the other hand, in the cancer data analysis task, the number of features/predictors is usually much larger than the sample size, which is known as the ＇~ 〉〉 N＂ problem and is also ubiquitous in other bioinformatics and computational biology fields. The ＂p 〉〉 N＂ problem puts the bias-variance trade-off in a more crucial place when designing statistical learning methods. However, to date, few RBM models have been particularly designed to address this issue. Methods： We propose a novel RBMs model, called elastic restricted Boltzmann machines （eRBMs）, which incorporates the elastic regularization term into the likelihood function, to balance the model complexity and sensitivity. Facilitated by the classic contrastive divergence （CD） algorithm, we develop the elastic contrastive divergence （eCD） algorithm which can train eRBMs efficiently. Results： We obtain several theoretical results on the rationality and properties of our model. We further evaluate the power of our model based on a challenging task -- predicting dichotomized survival time using the molecular profiling of tumors. The test results show that the prediction performance of eRBMs is much superior to that of the state-of-the-art methods. Conclusions： The proposed eRBMs are capable of dealing with the ＂p 〉〉 N＂ problems and have superior modeling performance over traditional methods. Our novel model is a promising method for future cancer data analysis.

关键词： RBMs regularization cancer data analysis survival time prediction

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VizEpis: A visualization and mapping tool for interpreting epistasis

VizEpis: A visualization and mapping tool for interpreting e...

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IEEE International Conference on bioinformatics and Biomedicine

作者： Min-Seok Kwon Sungyoung Lee Yongkang Kim Taesung Park Interdisciplinary program in Bioinformatics Seoul National University Department of Statistics Seoul National University

ISBN: (纸本)9781467368001

An important issue in genetic association studies is gene-gene interaction (epistasis) underlying common complex diseases that are affected by multiple genetic factors. Although many methods have been proposed to analyze epistasis, the interpretation of the identified gene-gene interactions is not straightforward. In order to efficiently provide the statistical interpretation and biological evidences of gene-gene interactions, we developed the VizEpis, a tool for visualizing of gene-gene interactions in genetic association analysis and mapping of epistatic interaction to the biological evidence from public interaction databases. Using interaction network and circular plot, the VizEpis provides to explore the interaction network integrated with biological evidences in epigenetic regulation, splicing, transcription, translation and post-translation level. To aid statistical interaction in genotype level, the VizEpis provides pairwise checkerboard, forest, and funnel. VizEpis provides for the user-specified variants with statistical interaction to find biological evidences.

关键词： Vizualization Gene-gene interaction Genome-wide association study

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Quality control plot for high dimensional omics data

Quality control plot for high dimensional omics data

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2016 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2016

作者： Kim, Gyu-Tae Kim, Yongkang Kwon, Min-Seok Park, Taesung Department of Statistics Seoul National University Daehak-Dong Seoul Gwanak-gu08826 Korea Republic of Interdisciplinary Program in Bioinformatics Seoul National University Daehak-Dong Gwanak-gu Seoul08826 Korea Republic of

ISBN: (纸本)9781509016105

Quality control (QC) becomes more important in pre-processing analysis of high dimensional omics data. Several routine QC processes became a standard process in omics data analysis. The standard QC analysis includes calculating quality-related measures, checking the consistency among samples, detecting outlying observations and so forth. QC analysis tends to be more important in the era of high dimensional omics data. Although several QC analysis tools providing simple graphical display have been developed by many researchers, they usually require a subjective decision on QC. Here, we propose high-dimensional data quality control (HidQC) plot which is a simple and efficient QC tool for handling high dimensional omics data. HidQC plot primarily focuses on identifying samples of poor quality by conducting a contrast analysis for the between/within group distances and the summary distances. HidQC plot checks the quality by investigating the consistency of samples for each group. Unlike other QC plots, HidQC plot provides the p-value of each sample based on the permutation test, which can be used as a more objective criterion to determine whether to use the sample or not. We applied HidQC plot to MicroArray Quality Control (MAQC) project 1 data to demonstrate its usefulness. © 2016 IEEE.

关键词： Quality control

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