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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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A Drug-Induced Liver Injury Prediction Model using Transcriptional Response Data with Graph Neural Network

A Drug-Induced Liver Injury Prediction Model using Transcrip...

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International Conference on Big Data and Smart Computing (BIGCOMP)

作者： Doyeong Hwang Minji Jeon Jaewoo Kang Department of Computer Science and Engineering Korea University Seoul Republic of Korea Interdisciplinary Graduate Program in Bioinformatics Korea University Seoul Republic of Korea

ISBN: (数字)9781728160344

ISBN: (纸本)9781728160351

Drug-Induced Liver Injury (DILI) is a major cause of failed drug candidates in clinical trials and withdrawal of approved drugs from the market. Therefore, machine learning-based DILI prediction can be key in increasing the success rate of drug discovery because drug candidates that are predicted to potentially induce liver injury can be rejected before clinical trials. However, existing DILI prediction models mainly focus on the chemical structures of drugs. Since we cannot determine whether a drug will cause liver injury based solely on its structure, DILI prediction based on the transcriptional effect of a drug on a cell is necessary. In this paper, we propose GLIT which is a model that uses transcriptional response data and chemical structures and can be used for drug-induced liver injury prediction. GLIT learns the embedding vectors of drug structures and drug-induced gene expression profiles using graph attention networks in a biological knowledge graph for predicting DILI. GLIT outperformed a baseline model that uses only drug structure information by 7% and 19.2% in terms of correct classification rate (CCR) and Matthews correlation coefficient (MCC), respectively. In addition, we conducted a literature survey to confirm whether the class labels of drugs, in the unknown DILI class, predicted by GLIT are correct.

关键词： Drugs Gene expression Neural networks Injuries Liver Predictive models Biological system modeling

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Comparing family-based rare variant association tests for dichotomous phenotypes

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BMC proceedings 2016年第SUPPL 7期10卷 181-186页

作者： Longfei Wang Sungkyoung Choi Sungyoung Lee Taesung Park Sungho Won Interdisciplinary Program in bioinformatics Seoul National University Seoul 151-742 Korea. Interdisciplinary Program in bioinformatics Seoul National University Seoul 151-742 Korea Department of Statistics Seoul National University Seoul 151-742 Korea. Department of Public Health Science Seoul National University Seoul 151-742 Korea Institute of Health Environment Seoul National University Seoul 151-742 Korea.

BACKGROUND:It has been repeatedly stressed that family-based samples suffer less from genetic heterogeneity and that association analyses with family-based samples are expected to be powerful for detecting susceptibility loci for rare disease. Various approaches for rare-variant analysis with family-based samples have been proposed. METHODS:In this report, performances of the existing methods were compared with the simulated data set provided as part of Genetic Analysis Workshop 19 (GAW19). We considered the rare variant transmission disequilibrium test (RV-TDT), generalized estimating equations-based kernel association (GEE-KM) test, an extended combined multivariate and collapsing test for pedigree data (known as Pedigree Combined Multivariate and Collapsing [PedCMC]), gene-level kernel and burden association tests with disease status for pedigree data (PedGene), and the family-based rare variant association test (FARVAT). RESULTS:The results show that PedGene and FARVAT are usually the most efficient, and the optimal test statistic provided by FARVAT is robust under different disease models. Furthermore, FARVAT was implemented with C++, which is more computationally faster than other methods. CONCLUSIONS:Considering both statistical and computational efficiency, we conclude that FARVAT is a good choice for rare-variant analysis with extended families.

关键词： Rare Variant Pedigree Data Genetic Analysis Workshop Empirical Size Rare Variant Association

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bioinformatics analysis of hereditary disease gene set to identify key modulators of myocardial remodeling during heart regeneration in Zebrafish 12

Bioinformatics analysis of hereditary disease gene set to id...

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12th International Conference on bioinformatics and Computational Biology, BICOB 2020

作者： Liu, Lawrence Yu-Min Lai, Zih-Yin Lin, Min-Hsuan Shih, Yu Chuang, Yung-Jen Department of Medical Science and Institute of Bioinformatics and Structural Biology National Tsing Hua University Hsinchu30013 Taiwan Division of Cardiology Department of Internal Medicine Hsinchu Mackay Memorial Hospital Hsinchu30071 Taiwan Interdisciplinary Program of Life Science National Tsing Hua University Hsinchu30013 Taiwan

Unlike mammals, adult zebrafish hearts retain a remarkable capacity to regenerate after injury. Since regeneration shares many common molecular pathways with embryonic development, we investigated myocardial remodeling genes and pathways by performing a comparative transcriptomic analysis of zebrafish heart regeneration using a set of known human hereditary heart disease genes related to myocardial hypertrophy during development. We cross-matched human hypertrophic cardiomyopathy-associated genes with a time-course microarray dataset of adult zebrafish heart regeneration. Genes in the expression profiles that were highly elevated in the early phases of myocardial repair and remodeling after injury in zebrafish were identified. These genes were further analyzed with web-based bioinformatics tools to construct a regulatory network revealing potential transcription factors and their upstream receptors. In silico functional analysis of these genes showed that they are involved in cardiomyocyte proliferation and differentiation, angiogenesis, and inflammation-related pathways. The regulatory network indicated that β-2- microglobulin-mediated signaling may play an important role in myocardial remodeling after injury. This novel cross-species bioinformatics approach to uncover key modulators of zebrafish heart regeneration through human hereditary disease genomic analysis could greatly facilitate the understanding of the evolutionarily conserved cardiac remodeling process. © 2020, EasyChair. All rights reserved.

关键词： Mammals

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FISH: Finding of identical spectra set for Homogenous peptide using two-stage clustering algorithm

FISH: Finding of identical spectra set for Homogenous peptid...

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IEEE International Conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： Seungmook Lee Min-Seok Kwon Hyoung-Joo Lee Young-Ki Paik Jae K. Lee Taesung Park Department of Statistics and Interdisciplinary program in Bioinformatics Seoul National University South Korea Interdisciplinary program in Bioinformatics Seoul National University South Korea Department of Biochemistry Yonsei Proteome Research Center and Biomedical Proteome Research Center Yonsei University South Korea Department of Public Health Sciences Division of Biostatistics University of Virginia School of Medicine USA

MS/MS experiments generate hundreds to tens of thousands of fragment ion spectra during the experiment. In the peptide identification, MS/MS spectra are often identified by database searching algorithms such as SEQUEST and Mascot. Most database searching algorithms calculate score functions to compare the experimental MS/MS spectra with theoretical MS/MS spectra of certain peptides derived from protein sequence databases. However, these indirect methods are vulnerable to potential errors. Thus, some spectra may not be assigned to peptides. To overcome these limitations, we propose a novel algorithm called Finding Identical Spectra for Homogenous peptide (FISH) by using two-stage clustering algorithm. Our proposed FISH algorithm can cluster spectra from the same peptide as a group based on the direct comparison. That is, through all possible pair-wise comparisons, our FISH method provides a set of spectra from the same peptide. To investigate the efficiency of our proposed method, we performed Nano-LC-MS/MS experiment for human tissue samples. Also, we conducted the simulation study to compare the performance of our proposed method with other database searching methods. Our simulation study showed that FISH yielded higher sensitivity than the other methods.

关键词： Peptides Databases Marine animals Proteomics Ions Proteins Correlation

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LOCOS: A cosine based local gene expression pattern finding algorithm on time-series data

LOCOS: A cosine based local gene expression pattern finding ...

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IEEE International Conference on Big Data

作者： Youjeong Suk Jaemin Jeon Inuk Jung School of Computer Science and Engineering Kyungpook National University Daegu Republic of Korea Interdisciplinary Program in Bioinformatics Seoul National University Seoul Republic of Korea

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

In gene expression analysis, understanding a biological event that is observed at some time instance often requires capturing genes whose expression levels modulate before and after the event. Such genes are expected to be the responders to the event and will exhibit similar expression patterns during the event. However, after the effect of the event fades, their expression levels may loose their correlation. Hence, it is a non-trivial task to identify genes that share highly similar local expression patterns nearby the event and also allowing some level of divergent expression patterns further away from the event’s time point. Here, we propose LOCOS (LOcal COSine), a novel time-course clustering algorithm tailored to cluster genes exhibiting similar expression patterns within a specified time interval. LOCOS is an extension of the traditional Non-negative Matrix Factorization (NMF), which incorporates the cosine similarity and Euclidean distances in its update procedure. LOCOS maximizes the cosine similarity within an interval of interest, while allowing a relaxed minimization of Euclidean distance outside it. Using synthetic and non-biological time-course data, we showed that LOCOS was able to correctly detect the known local patterns within a specified interval. Furthermore, we used longitudinal single-cell RNA-seq samples from four patients showing deteriorating and recovering health conditions to identify genes related to each of the phenotype. As a result, LOCOS was able to capture gene clusters with distinct expression patterns that aligned with the intervals embedding the clinical deterioration and recovery events.

关键词： Phenotypes Correlation Clustering algorithms Euclidean distance Big Data Minimization Biology Gene expression

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CollaboNet: Collaboration of deep neural networks for biomedical named entity recognition

arXiv

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arXiv 2018年

作者： Yoon, Wonjin So, Chan Ho Lee, Jinhyuk Kang, Jaewoo Department of Computer Science and Engineering Korea University Seoul02841 Korea Republic of Interdisciplinary Graduate Program in Bioinformatics Korea University Seoul02841 Korea Republic of

Background: Finding biomedical named entities is one of the most essential tasks in biomedical text mining. Recently, deep learning-based approaches have been applied to biomedical named entity recognition (BioNER) and showed promising results. However, as deep learning approaches need an abundant amount of training data, a lack of data can hinder performance. BioNER datasets are scarce resources and each dataset covers only a small subset of entity types. Furthermore, many bio entities are polysemous, which is one of the major obstacles in named entity recognition. Results: To address the lack of data and the entity type misclassification problem, we propose CollaboNet which utilizes a combination of multiple NER models. In CollaboNet, models trained on a different dataset are connected to each other so that a target model obtains information from other collaborator models to reduce false positives. Every model is an expert on their target entity type and takes turns serving as a target and a collaborator model during training time. The experimental results show that CollaboNet can be used to greatly reduce the number of false positives and misclassified entities including polysemous words. CollaboNet achieved state-of-the-art performance in terms of precision, recall and F1 score. Conclusions: We demonstrated the benefits of combining multiple models for BioNER. Our model has successfully reduced the number of misclassified entities and improved the performance by leveraging multiple datasets annotated for different entity types. Given the state-of-the-art performance of our model, we believe that CollaboNet can improve the accuracy of downstream biomedical text mining applications such as bio-entity relation extraction. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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Analysis for doubly repeated omics data from crossover design

Analysis for doubly repeated omics data from crossover desig...

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2016 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2016

作者： Choi, Sunghoon Park, Soo-Yeon Kim, Hoejin Kwon, Oran Park, Taesung Department of Statistics Seoul National University Daehak-Dong Seoul Gwanak-gu Korea Republic of Department of Nutritional Science and Food Management Ewha Woman's University Daehyeon-dong Seodaemun-gu Seoul Korea Republic of Interdisciplinary Program in Bioinformatics Seoul National University Daehak-Dong Gwanak-gu Seoul Korea Republic of

ISBN: (纸本)9781509016105

Some crossover clinical trials produce doubly repeated omics data with two repeated factors. Linear mixed effect models (LMMs) are commonly applied to the data from the crossover design focusing on the analysis of repeatedly observed omics data themselves. Alternatively, the univariate analyses using the single summary measurements such as differences between time points and incremental area under curve (iAUC) are also widely used. In this study, we compare the performance of both methods for real doubly repeated omics data from a crossover study. © 2016 IEEE.

关键词： Crossover design Linear mixed effect model Repeated measurements

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Quantized memory-augmented neural networks

arXiv

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arXiv 2017年

作者： Park, Seongsik Kim, Seijoon Lee, Seil Bae, Ho Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Interdisciplinary Program in Bioinformatics Seoul National University Seoul08826 Korea Republic of

Memory-augmented neural networks (MANNs) refer to a class of neural network models equipped with external memory (such as neural Turing machines and memory networks). These neural networks outperform conventional recurrent neural networks (RNNs) in terms of learning long-term dependency, allowing them to solve intriguing AI tasks that would otherwise be hard to address. This paper concerns the problem of quantizing MANNs. Quantization is known to be effective when we deploy deep models on embedded systems with limited resources. Furthermore, quantization can substantially reduce the energy consumption of the inference procedure. These benefits justify recent developments of quantized multilayer perceptrons, convolutional networks, and RNNs. However, no prior work has reported the successful quantization of MANNs. The in-depth analysis presented here reveals various challenges that do not appear in the quantization of the other networks. Without addressing them properly, quantized MANNs would normally suffer from excessive quantization error which leads to degraded performance. In this paper, we identify memory addressing (specifically, content-based addressing) as the main reason for the performance degradation and propose a robust quantization method for MANNs to address the challenge. In our experiments, we achieved a computation-energy gain of 22× with 8-bit fixed-point and binary quantization compared to the floating-point implementation. Measured on the bAbI dataset, the resulting model, named the quantized MANN (Q-MANN), improved the error rate by 46% and 30% with 8-bit fixed-point and binary quantization, respectively, compared to the MANN quantized using conventional techniques. Copyright © 2017, The Authors. All rights reserved.

关键词： Recurrent neural networks

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