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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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Elastic-net regularization approaches for genome-wide association studies of rheumatoid arthritis

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BMC proceedings 2009年第7期3 Suppl 7卷 S25页

作者： Seoae Cho Haseong Kim Sohee Oh Kyunga Kim Taesung Park Interdisciplinary Program in Bioinformatics Seoul National University Kwan-ak St 599 Kwan-ak Gu Seoul South Korea 151-741 Republic of Korea. sacho71@snu.ac.kr.

The current trend in genome-wide association studies is to identify regions where the true disease-causing genes may lie by evaluating thousands of single-nucleotide polymorphisms (SNPs) across the whole genome. However, many challenges exist in detecting disease-causing genes among the thousands of SNPs. Examples include multicollinearity and multiple testing issues, especially when a large number of correlated SNPs are simultaneously tested. Multicollinearity can often occur when predictor variables in a multiple regression model are highly correlated, and can cause imprecise estimation of association. In this study, we propose a simple stepwise procedure that identifies disease-causing SNPs simultaneously by employing elastic-net regularization, a variable selection method that allows one to address multicollinearity. At Step 1, the single-marker association analysis was conducted to screen SNPs. At Step 2, the multiple-marker association was scanned based on the elastic-net regularization. The proposed approach was applied to the rheumatoid arthritis (RA) case-control data set of Genetic Analysis Workshop 16. While the selected SNPs at the screening step are located mostly on chromosome 6, the elastic-net approach identified putative RA-related SNPs on other chromosomes in an increased proportion. For some of those putative RA-related SNPs, we identified the interactions with sex, a well known factor affecting RA susceptibility.

关键词： Genetic Analysis Workshop Screen Step Ridge Penalty Autocrine Motility Factor Receptor Automatic Variable Selection

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Integrated synchrotron radiation-based fourier transform infrared (SR-FTIR) microscopy and tandem-mass spectrometry (LC-MS/MS) used to elucidate the apoptotic effect of chamuangone in A549 cells

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Biochemical and Biophysical Research Communications 2025年 764卷 151826页

作者： Wanaragthai, Panatda Yingchutrakul, Yodying Panichayupakaranant, Pharkphoom Vongsvivut, Jitraporn Aonbangkhen, Chanat Yang, Meng Chieh Maiuthed, Arnatchai Chanvorachote, Pithi Wood, Bayden R. Choowongkomon, Kiattawee Krobthong, Sucheewin Interdisciplinary Program of Genetic Engineering and Bioinformatics Kasetsart University Bangkok Thailand National Center for Genetic Engineering and Biotechnology NSTDA Pathum Thani 12120 Thailand Phytomedicine and Pharmaceutical Biotechnology Excellence Center Faculty of Pharmaceutical Sciences Prince of Songkla University Hat-Yai Thailand ANSTO-Australian Synchrotron 800 Blackburn Road Clayton Victoria Australia Center of Excellence in Natural Products Chemistry (CENP) Department of Chemistry Faculty of Science Chulalongkorn University Bangkok 10330 Thailand Department of Anatomy Faculty of Medicine Srinakharinwirot University Bangkok 10110 Thailand Centre of Biopharmaceutical Science for Healthy Ageing Faculty of Pharmacy Mahidol University Bangkok 10400 Thailand Department of Pharmacology Faculty of Pharmacy Mahidol University Bangkok 10400 Thailand Center of Excellence in Cancer Cell and Molecular Biology Faculty of Pharmaceutical Sciences Chulalongkorn University Bangkok 10330 Thailand Department of Pharmacology and Physiology Faculty of Pharmaceutical Sciences Chulalongkorn University Bangkok 10330 Thailand Bio-spectroscopy Group School of Chemistry Monash University Clayton Victoria 3800 Australia Department of Biochemistry Faculty of Science Kasetsart University Bangkok Thailand

Chamuangone, a natural compound extracted from Garcinia cowa leaves, has demonstrated potential in cancer therapeutics, but its effects on lung cancer cells remain unclear. This study investigates the apoptotic effects of Chamuangone on human lung adenocarcinoma cells (A549). The A549 cells were treated with Chamuangone, and the cytotoxic effects were evaluated using an MTT assay, revealing a dose-dependent inhibition of cell proliferation with an IC₅₀ value of 19.43 μM. Annexin V assays further confirmed that Chamuangone induces apoptosis in A549 cells, showing increased levels of late apoptosis with higher concentrations. Synchrotron radiation-based Fourier transform infrared (SR-FTIR) microscopy provided insights into macromolecular changes, highlighting significant alterations in proteins, lipids, and nucleic acids. These structural changes in key cellular macromolecules were supported by proteomic analysis, which identified the upregulation of apoptosis-related proteins, including Peroxiredoxin-2 and Na⁺/H⁺ exchange regulatory cofactor NHE-RF1. Canonical pathway analysis indicated that Chamuangone affects oxidative phosphorylation and mitochondrial dysfunction, both crucial pathways for apoptosis. Additionally, upstream regulator analysis demonstrated significant inhibition of the epidermal growth factor receptor (EGFR), a key player in lung cancer progression. These findings suggest that Chamuangone triggers apoptosis through mitochondrial pathways and EGFR inhibition, positioning it as a promising therapeutic candidate for lung cancer treatment. © 2025 Elsevier Inc.

关键词： Epidermal growth factor receptor Garcinia cowa Mitochondrial dysfunction Oxidative phosphorylation Proteomics

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