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arXiv 2019年

作者： Martin, Evan A. Fu, Audrey Qiuyan The Bioinformatics and Computational Biology Program University of Idaho United States The Bioinformatics and Computational Biology Program Department of Mathematics and Statistical Science Institute for Modeling Collaboration and Innovation Institute for Interdisciplinary Data Sciences University of Idaho United States

Graphical models or networks describe the statistical dependence among multiple variables and are widely used in biology (e.g., gene regulatory networks). Under appropriate assumptions, directed edges may represent causal relationships. A key feature of a biological network is sparsity, defined by how likely an edge is present, of which we often have some knowledge. However, most existing Bayesian methods use priors for the entire graph, making it difficult to specify the level of sparsity. The few methods that use priors on edges estimate the two directions independently;the sum of the two probabilities can exceed 1. Here, we present baycn (BAYesian Causal Network), a novel approximate Bayesian method that represents a graph in terms of three states of edges: the two directions and edge absence, and specifies priors on these edge states. We design a pseudo Bayesian sampling algorithm for efficient inference. We apply baycn to two genomic problems: i) distinguishing direct and indirect target genes of genetic variants, using these variants as instrumental variables, and ii) inferring combinatorial binding of highly-correlated transcription factors in Drosophila. In both cases and in extensive simulations, our method demonstrates much improved accuracy over existing methods for the whole graph and for individual edges. Copyright © 2019, The Authors. All rights reserved.

关键词： Inference engines

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Gene-gene interaction analysis for the survival phenotype based on the standardized residuals from parametric regression models

Gene-gene interaction analysis for the survival phenotype ba...

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IEEE International Conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： Seungyeoun Lee Jinseok Oh Min-Seok Kwon Taesung Park Department of Mathematics and Statistics Sejong University South Korea Interdisciplinary Program in Bioinformatics Seoul National University Seoul South Korea

Most of statistical methods in genome-wide association studies (GWAS) have been developed to identify SNP-SNP interactions using a binary phenotype for case-control studies. However, there is an interest in identifying SNPs and combinations of SNPs that relate to the survival phenotype in many cancer studies. Recently, Gui et al. (2011) proposed a novel method, called Surv-MDR, for detecting gene-gene interactions associated with survival time by modifying the multifactor dimensionality reduction (MDR) method based on the log-rank test. However, the Surv-MDR method needs more-intensive computations and does not allow for a covariate adjustment. Lee et al. (2011) proposed an alternative approach, called Cox-MDR, for detecting gene-gene interactions based on a Cox model by extending generalized multifactor dimensionality reduction (GMDR) to the survival time phenotype. The main idea of Cox-MDR is to use a martingale residual as a score to classify multi-level genotypes as high and low risk groups. Cox-MDR also allows the effects of discrete and quantitative covariates to be easily adjusted in the Cox model and requires much less computation than Surv-MDR. In this paper, we propose to extend the idea of a Cox-MDR to the parametric regression model, called PRM-MDR for identifying gene-gene interactions associated with the survival phenotype. The main idea of PRM-MDR is to use a standardized residual from a parametric regression model as a score to classify multi-level genotypes as high and low risk groups while keeping the rest of the MDR procedure unchanged. We performed a simulation study to compare PRM-MDR with Cox-MDR as well as the Surv-MDR method in detecting two-way interactions. The simulation setting is constructed as similar as that of Gui et al. (2011), so that three methods are comparable. In addition, we compared the power of PRM-MDR with adjusting the covariates versus without adjusting the covariates in two different scenarios. In the first scenario, we a

关键词： Accuracy Computational modeling Cancer Testing Analytical models Mathematical model Training

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Towards High Generalization Performance on Electrocardiogram Classification

Towards High Generalization Performance on Electrocardiogram...

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2021 Computing in Cardiology, CinC 2021

作者： Han, Hyeongrok Park, Seongjae Min, Seonwoo Choi, Hyun-Soo Kim, Eunji Kim, Hyunki Park, Sangha Kim, Jinkook Park, Junsang An, Junho Lee, Kwanglo Jeong, Wonsun Chon, Sangil Ha, Kwonwoo Han, Myungkyu Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul Korea Republic of HUINNO Co. Ltd Seoul Korea Republic of LG AI Research Seoul Korea Republic of Department of Computer Science and Engineering Kangwon National University Chuncheon Korea Republic of Department of Biological Sciences Interdisciplinary Program in Bioinformatics Interdisciplinary Program in Artificial Intelligence ASRI INMC Institute of Engineering Research Seoul National University Seoul Korea Republic of

ISBN: (纸本)9781665479165

Recently, many electrocardiogram (ECG) classification algorithms using deep learning have been proposed. The characteristics of ECG vary from dataset to dataset for various reasons (i.e., hospital, race, etc.). Therefore, it is important for a model to have high generalization performance consistently over all datasets. In this paper, as part of the PhysioNet / Computing in Cardiology Challenge 2021, we present a model developed to classify cardiac abnormalities from 12 lead and reduced-lead ECGs. In particular, to upgrade our previous model for improving generalization performance, we newly adopt constant-weighted cross-entropy loss, additional features, Mixup augmentation, and squeeze/excitation block, OneCycle learning rate scheduler, which are selected via evaluation of generalization performance using leave-one-dataset-out cross-validation setting. With the present model, our DSAILSNU team has received challenge scores of 0.55, 0.58, 0.58, 0.57 and 0.57 (ranked 2nd, 1st, 1st, 2nd, 2nd out of 39 teams) for the 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead versions of the hidden test set, respectively. The present model achieves higher generalization performance over all versions of the hidden test set than the model submitted last year. © 2021 Creative Commons.

关键词： Deep learning

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GxG-Viztool: A program for visualizing gene-gene interactions in genetic association analysis

GxG-Viztool: A program for visualizing gene-gene interaction...

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IEEE International Conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： Kyunga Kim Min-Seok Kwon Sung Young Lee Junghyun Namkung Ming D. Li Taesung Park Department of Statistics Sookmyung Womens University Seoul South Korea Interdisciplinary Program in Bioinformatics Seoul National University Seoul South Korea Department of Psychiatry and Neurobehavioral Sciences University of Virginia Charlottesville VA USA Interdisciplinary Program in Bioinformatics Department of Statistics Seoul National University Seoul South Korea

ISBN: (纸本)9781467327466

Gene-gene interactions are important factors underlying a common complex trait that is mostly polygenic. While many methods have been proposed to analyze gene-gene interactions in genetic association studies, the interpretation of the identified gene-gene interactions is not straightforward. In order to aid the interpretation of gene-gene interactions, we developed the GxG-Viztool, an executable program for visualizing gene-gene interactions in genetic association analysis. The GxG-Viztool provides an effective way to recognize genotype combinations that enhance/repress a trait and to display polygenic structure of interactions. The GxG-Viztool implements six graphical tools: checkerboard, pairwise checkerboard, forest, funnel, 3D lattice and parallel coordinate plots, which make it effective to recognize certain patterns in gene-gene interactions. It is freely available at http ://bibs. snu. ac. kr/GxG-Viz tool.

关键词： Data visualization Genetics Lattices Visualization Educational institutions Pattern recognition Heating

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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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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

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

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

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.

关键词： COVID-19 Training Uncertainty Biological system modeling Decision making Machine learning Predictive models Mathematical models Reliability Forecasting

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CUDA-LR: CUDA-accelerated logistic regression analysis tool for gene-gene interaction for genome-wide association study

CUDA-LR: CUDA-accelerated logistic regression analysis tool ...

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IEEE International Conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： Sungyoung Lee Min-Seok Kwon Ik-Soo Huh Taesung Park Interdisciplinary Program in Bioinformatics Seoul National University Seoul South Korea Department of Statistics Seoul National University Seoul South Korea

In genome-wide association studies (GWAS), logistic regression (LR) has been most commonly used for finding an association between a disease phenotype and genetic variants such as single nucleotide polymorphism (SNP). Since logistic regression model requires iterative algorithms to get the parameter estimates, its application to GWAS has been limited to the identification of the individual SNPs. Thus, there have been limited applications of LR to multiple SNP analysis including gene-gene interaction analysis in large scale GWAS data. To overcome this computational burden, we developed a logistic regression analysis tool named CUDA-LR, based on the new programming architecture using Graphics Processing Unit (GPU). CUDA-LR supports not only the simple model with single SNP but also more complex model with two SNPs including the interaction. In addition, CUDA-LR provides various parameters to gain more acceleration and perform specified analysis. In the comparison between our analysis and the other methods, CUDA-LR showed almost 700-folds of acceleration and highly reliable results by our GPU specified optimization techniques. We believe that the CUDA-LR now is a useful logistic regression analysis tool for interaction analysis of large scale GWAS datasets.

关键词： Graphics processing unit bioinformatics Acceleration Genomics Logistics Mathematical model programming

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Using a Recurrent Neural Network Model for Classification of Tweets Conveyed Influenza-related Information 1

Using a Recurrent Neural Network Model for Classification of...

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1st International Workshop on Digital Disease Detection using Social Media, DDDSM 2017, co-located with the 8th International Joint Conference on Natural Language Processing, IJCNLP 2017

作者： Wang, Chen-Kai Singh, Onkar Tang, Zhao-Li Dai, Hong-Jie Graduate Institute of Biomedical Informatics Taipei Medical University Taipei Taiwan Bioinformatics Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Institute of Biomedical Informatics National Yang-Ming University Taipei Taiwan Department of Computer Science and Information Engineering National Taitung University Taitung Taiwan Interdisciplinary Program of Green and Information Technology National Taitung University Taitung Taiwan

ISBN: (纸本)9781948087070

Traditional disease surveillance systems depend on outpatient reporting and virological test results released by hospitals. These data have valid and accurate information about emerging outbreaks but it's often not timely. In recent years the exponential growth of users getting connected to social media provides immense knowledge about epidemics by sharing related information. Social media can now flag more immediate concerns related to outbreaks in real time. In this paper we apply the long short-term memory recurrent neural network (RNN) architecture to classify tweets conveyed influenza-related information and compare its performance with baseline algorithms including support vector machine (SVM), decision tree, naive Bayes, simple logistics, and naive Bayes multinomial. The developed RNN model achieved an F-score of 0.845 on the MedWeb task test set, which outperforms the F-score of SVM without applying the synthetic minority oversampling technique by 0.08. The F-score of the RNN model is within 1% of the highest score achieved by SVM with oversampling technique. © 2017 AFNLP

关键词： Support vector machines

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Identification of hyperparameters with high effects on performance of deep neural networks: application to clinicopathological data of ovarian cancer

Identification of hyperparameters with high effects on perfo...

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IEEE International Conference on bioinformatics and Biomedicine

作者： Suhyun Hwangbo Yong-Sang Song Se Ik Kim Taesung Park Untack Cho Interdisciplinary Program in Bioinformatics Seoul National University Department of Obstetrics and Gynecology Seoul National University College of Medicine Department of Statistics Seoul National University Interdisciplinary Program in Cancer Biology Seoul National University College of Medicine

ISBN: (纸本)9781728118680

Recent advances in deep learning have emerged as an effective approach for precision medicine. The applications of deep learning to medicine have been applied mainly to medical image data but not clinicopathological data. One of challenges of deep learning model to clinicopathological data is to optimize hyperparameters to get high predictive power. In this study, we identified hyperparameters of deep learning model that have large effects on power. Specifically, we focused on predicting platinum-based chemotherapy response for ovarian cancer patients. As a performance metric, we used the area under the curve. We optimized six hyperparameters: the number of hidden layers, number of hidden units, optimization algorithm, weight initialization, activation function, and dropout rate. We also identified significant interaction effects between hyperparameters. We successfully found the combination of hyperparameters having large effects on prediction. These optimal combinations are expected to increase the prediction accuracy for the response to chemotherapy for a variety of cancer patients.

关键词： deep neural network hyperparameter predictive power Hyperparameter Ovary Cancer Area Under Curve image data interaction effect Performance metrics Machine Learning network applications Drug Therapy weight initialization Neural network

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Pathway based identification of rare mutation effect in cancer

Pathway based identification of rare mutation effect in canc...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： TaeJin Ahn Taesung Park Interdisciplinary Program in Bioinformatics Seoul National University Seoul Republic of Korea Department of statistics Seoul National University Seoul Republic of Korea

ISBN: (纸本)9781479913114

Identifying driver mutation is important in understanding disease mechanism and future application of custom tailored therapeutic decision. Functional analysis of mutational impact usually focuses on the gene expression level of the mutated gene. However, complex regulatory network may cause differential gene expression among functional neighbors of the mutated gene. We suggest a new approach for discovering rare mutations that have real impact in the context of pathway; philosophy of our method is iteratively combining rare mutations until no more mutations can be added, under the condition that the combined mutational event can statistically discriminate pathway level mRNA expression between groups with and without mutation events. Our approach is shown to sensitively capture mutations that change pathway level gene expression at breast cancer data.

关键词： Gene expression Breast cancer Drugs Tumors Proteins

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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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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

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

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

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.

关键词： Deep learning Uncertainty Biological system modeling Precision medicine Predictive models Metastasis Reliability Prognostics and health management Prostate cancer Biological neural networks

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