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IEEE International Conference on bioinformatics and Biomedicine

作者： Gyu-tae Kim Yongkang Kim Min-Seok Kwon Taesung Park Department of Statistics Seoul National University Daehak-Dong Gwanak-gu 08826 Republic of Korea Interdisciplinary Program in Bioinformatics Seoul National University Daehak-Dong Gwanak-gu 08826 Republic of Korea

ISBN: (纸本)9781509016129

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.

关键词： High-dimensional data quality control (HidQC) plot Quality Control (QC) Microarray Omics data MicroArray Quality Control(MAQC) project

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Protein function classification based on gene ontology

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2nd Asia Information Retrieval Symposium, AIRS 2005

作者： Park, Dae-Won Heo, Hyoung-Sam Kwon, Hyuk-Chul Chung, Hea-Young Department of Computer Science and Engineering Pusan National University 30 Jangjeon-dong Geumjung-gu Busan 609-735 Korea Republic of Interdisciplinary Program of Bioinformatics Pusan National University 30 Jangjeon-dong Geumjung-gu Busan 609-735 Korea Republic of Department of Pharmacy Pusan National University 030 Jangjeon-dong Geumjung-gu Busan 609-735 Korea Republic of

ISBN: (纸本)3540291865

Most proteins interact with other proteins, cells, tissues or diseases. They have biological functions and can be classified according to their functions. With the functions and the functional relations of proteins, we can explain many biological phenomena and obtain answers in solving biological problems. Therefore, it is important to determine the functions of proteins. In this paper we present a protein function classification method for the function prediction of proteins. With human proteins assigned to GO molecular function terms, we measure the similarity of proteins to function classes using the functional distribution. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Proteins

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Flexible dual-branched message passing neural network for quantum mechanical property prediction with molecular conformation

arXiv

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arXiv 2021年

作者： Jo, Jeonghee Kwak, Bumju Lee, Byunghan Yoon, Sungroh Interdisciplinary Program in Bioinformatics Seoul National University Recommendation Team Kakao Corporation Department of Electronic and IT Media Engineering Seoul National University of Science and Technology Department of Electrical and Computer Engineering Seoul National University

A molecule is a complex of heterogeneous components, and the spatial arrangements of these components determine the whole molecular properties and characteristics. With the advent of deep learning in computational chemistry, several studies have focused on how to predict molecular properties based on molecular configurations. Message passing neural network provides an effective framework for capturing molecular geometric features with the perspective of a molecule as a graph. However, most of these studies assumed that all heterogeneous molecular features, such as atomic charge, bond length, or other geometric features always contribute equivalently to the target prediction, regardless of the task type. In this study, we propose a dual-branched neural network for molecular property prediction based on message-passing framework. Our model learns heterogeneous molecular features with different scales, which are trained flexibly according to each prediction target. In addition, we introduce a discrete branch to learn single atom features without local aggregation, apart from message-passing steps. We verify that this novel structure can improve the model performance with faster convergence in most targets. The proposed model outperforms other recent models with sparser representations. Our experimental results indicate that in the chemical property prediction tasks, the diverse chemical nature of targets should be carefully considered for both model performance and generalizability. © 2021, CC BY.

关键词： Deep learning

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Diversity and Evolution of RNA Recognition Motifs: A Case Study on the Utility of RdRp in the phylum Duplornaviricota

Diversity and Evolution of RNA Recognition Motifs: A Case St...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Mikyung Je Myeongji Cho Hyeon S. Son Interdisciplinary Graduate Program in Bioinformatics College of Natural Science Seoul National University Seoul Republic of Korea Lab. of Computational Biology & Bioinformatics Graduate School of Public Health Seoul National University Seoul Republic of Korea Institute of Health and Environment Seoul National University Seoul Republic of Korea

Viruses possess specific conserved regions known as RNA recognition motifs, which are shared within taxonomic groups. Applying these to the Baltimore virus classification system, there are genes/proteins that serve as biomarkers for each of the seven groups. Mutations in conserved sequence regions can have a significant impact on the function of a virus, especially if these conserved regions are part of a major protein. In this study, we analyzed the RdRp gene, known to be a representative RRM of RNA viruses, from viruses of five families (Alternaviridae, Chrysoviridae, Totiviridae, Sedoreoviridae, and Spinareoviridae) within the phylum Duplornaviricota. Data were retrieved from the NCBI non-redundant (nr) database (https://***/protein) applying a cut-off of 300 amino acids in length. RdRp protein sequences were aligned using PROMALS3D, and PhyML (v3.0) was used for phylogenetic analysis. In the next step, we utilized the officially provided ICTV taxonomy and bioinformatics tools to assign the phylogenetic position of RdRp. Although this analysis focused solely on RdRp, it can be concluded that using this gene as a marker may be sufficient for taxonomic assignment. Subsequent analyses will target DNA viruses and RT viruses to perform a more comprehensive assessment. This extensive analysis aims to validate the usefulness of marker genes in the overall viral taxonomy.

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Towards High Generalization Performance on Electrocardiogram Classification

Towards High Generalization Performance on Electrocardiogram...

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Computers in Cardiology (CinC)

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

ISBN: (纸本)9781665467216

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 DSAIL_SNU 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.

关键词： Deep learning Hospitals Computational modeling Electrocardiography Lead Classification algorithms Cardiology

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TargetNet: Functional microRNA target prediction with deep neural networks

arXiv

引用

arXiv 2021年

作者： Min, Seonwoo Lee, Byunghan Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of LG AI Research Seoul07796 Korea Republic of Department of Electronic and IT Media Engineering Seoul National University of Science and Technology Seoul01811 Korea Republic of Interdisciplinary Program in Artificial Intelligence and Interdisciplinary Program in Bioinformatics Seoul National University Seoul08826 Korea Republic of

Motivation: MicroRNAs (miRNAs) play pivotal roles in gene expression regulation by binding to target sites of messenger RNAs (mRNAs). While identifying functional targets of miRNAs is of utmost importance, their prediction remains a great challenge. Previous computational algorithms have major limitations. They use conservative candidate target site (CTS) selection criteria mainly focusing on canonical site types, rely on laborious and time-consuming manual feature extraction, and do not fully capitalize on the information underlying miRNA-CTS interactions. Results: In this paper, we introduce TargetNet, a novel deep learning-based algorithm for functional miRNA target prediction. To address the limitations of previous approaches, TargetNet has three key components: (1) relaxed CTS selection criteria accommodating irregularities in the seed region, (2) a novel miRNA-CTS sequence encoding scheme incorporating extended seed region alignments, and (3) a deep residual network-based prediction model. The proposed model was trained with miRNA-CTS pair datasets and evaluated with miRNA-mRNA pair datasets. TargetNet advances the previous state-of-theart algorithms used in functional miRNA target classification. Furthermore, it demonstrates great potential for distinguishing high-functional miRNA targets. © 2021, CC BY-NC-ND.

关键词： RNA

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Single-cell RNA-sequencing and subcellular spatial transcriptomics facilitate the translation of liver microphysiological systems for regulatory application

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Journal of Pharmaceutical Analysis 2023年第7期13卷 691-693页

作者： Dan Li Zhou Fang Qiang Shi Nicholas Zhang Binsheng Gong Weida Tong Ahmet F.Coskun Joshua Xu Division of Bioinformatics and Biostatistics National Center for Toxicological ResearchU.S.Food and Drug AdministrationJeffersonAR72079USA Wallace H.Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory UniversityAtlantaGA30332USA Machine Learning Graduate Program Georgia Institute of TechnologyAtlantaGA30332USA Division of Systems Biology National Center for Toxicological ResearchU.S.Food and Drug AdministrationJeffersonAR72079USA Interdisciplinary Bioengineering Graduate Program Georgia Institute of TechnologyAtlantaGA30332USA Parker H.Petit Institute for Bioengineering and Bioscience Georgia Institute of TechnologyAtlantaGA30332USA

Reducing the use of animal models in drug development and safety assessment has long been supported by the *** and Drug Administration(FDA).The report by Royal Society for the Prevention of Cruelty to Animals indicates that in 2020,experiments involved the use of over 100 million animals,with the United States leading the list by utilizing 20 million *** ethical considerations associated with animal testing and the costs in terms of time and money,animal models are not always effective in predicting human reactions to drug *** animal testing has been the traditional method for assessing the safety and efficacy of drugs.

关键词： money utilizing testing

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Biomarker development for pancreatic ductal adenocarcinoma using integrated analysis of mRNA and miRNA expression

Biomarker development for pancreatic ductal adenocarcinoma u...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Min-Seok Kwon Yongkang Kim Seungyeoun Lee Junghyun Namkung Taegyun Yun Sung Gon Yi Sangjo Han Meejoo Kang Sun Whe Kim Jin-Young Jang Taesung Park Interdisciplinary program in Bioinformatics Seoul National University Seoul Korea Department of Statistics Seoul National University Seoul Korea Department of Mathematics and Statistics Sejong University Seoul Korea Healthcare group SK telecom Bioinformatics Tech. Lab. Seoul Korea Department of Surgery Seoul National University Hospital Seoul Korea

ISBN: (纸本)9781479956708

Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer, which has dismal prognosis because of its silent early symptoms, high metastatic potential, and resistance to conventional therapies. Although a PDAC patient who is diagnosed at an early stage would have a substantial increase in chance of survival, the survival rate is poor because there is no efficient non-invasive diagnostic test in the early stage. In this study, we developed an efficient prediction models to detect PDAC in its early stages. Our prediction models use both mRNA and miRNA expression data from 104 PDAC tissues and 17 normal pancreatic tissues using microarray technology. After quality control, we built prediction models based on support vector machine (SVM) from mRNA and miRNA expressions for detecting early PDAC. To prevent over-fitting effect, we conducted leave-one-out cross validation (LOOCV) and 5-fold cross validation (CV). For independent validation of prediction models, we performed evaluation on independent datasets from Gene Expression Omnibus (GEO). After the validation, we identified 28 single markers and 231 combinations of markers with powerful prediction performance. In addition, the marker candidates are annotated with cancer pathways using gene ontology analysis. Our prediction models for PDAC may have potential for early diagnosis of PDAC.

关键词： Cancer Barium Blood Lungs Predictive models Ontologies Reliability

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In vitro and in vivo toxicity assessment of the senotherapeutic Peptide 14

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Toxicology Reports 2022年 9卷 1632-1638页

作者： Zonari, Alessandra Brace, Lear E. Alencar-Silva, Thuany Porto, William F. Foyt, Daniel Guiang, Mylieneth Cruz, Edgar Andres Ochoa Franco, Octavio L. Oliveira, Carolina R. Boroni, Mariana Carvalho, Juliana L. OneSkin Inc San Francisco 94107 CA United States Genomic Sciences and Biotechnology Program Catholic University of Brasilia DF Brasília 70790-160 Brazil DF Brasília 72236–011 Brazil Center of Proteomic Analyses and Biochemistry Genomic Sciences and Biotechnology Program Catholic University of Brasilia DF Brasilia 70790-160 Brazil S-Inova Biotech Biotechnology Program Catholic University Dom Bosco MS Campo Grande 79117-010 Brazil Molecular Pathology Program University of Brasilia DF Brasilia 70.910-900 Brazil Bioinformatics and Computational Biology Lab Brazilian National Cancer Institute (INCA) RJ Rio de Janeiro 20231-050 Brazil Interdisciplinary Biosciences Laboratory Faculty of Medicine University of Brasília DF Brasília 70.910–900 Brazil

Senotherapeutic molecules decrease cellular senescence burden, constituting promising approaches to combat the accumulation of senescent cells observed in chronological aging and age-related diseases. Numerous molecules have displayed senotherapeutic potential, but toxicity has been frequently observed. Recently, a new senotherapeutic compound, Peptide 14, was developed to modulate cellular senescence in the skin. In order to assess the potential toxic and genotoxic effects of the peptide, we observed the viability of human primary dermal fibroblasts and epidermal keratinocytes with Peptide 14 treatment, and show that it is mostly non-toxic in concentrations up to 100 μM. Cancer lines were also used to investigate its potential of modulating proliferation. Different concentrations of the peptide promoted a discrete reduction in the proliferation of cancerous cells of the MeWo and HeLa lineages. In full-thickness human skin equivalents, topically formulated Peptide 14 also failed to exert any significant irritation, nor cellular toxicity when added to the culture media. Genotoxic assays including the Ames, micronucleus, and karyotyping tests also indicate the safety of the peptide. Finally, the irritative potential of the peptide was assessed in human subjects in a repeated insult patch test executed using 1 mM peptide. No visible skin reactions were observed in any of the 54 participants. Taken together, the present data support that Peptide 14 is a senotherapeutic molecule with a positive safety profile as tested with cruelty-free models, justifying further studies involving the peptide. © 2022 The Authors

关键词： 3D skin equivalents Aging Cellular senescence In-vitro toxicology Peptide 14 RIPT

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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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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Sunghoon Choi Soo-yeon Park Hoejin Kim Oran Kwon Taesung Park Department of Statistics Seoul National University Seoul Republic of Korea Department of Nutritional Science and Food Management Ewha Woman's University Seoul Republic of Korea Interdisciplinary Program in Bioinformatics Seoul National University Seoul Republic of Korea

ISBN: (纸本)9781509016129

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.

关键词： Hardware design languages Time measurement Genomics bioinformatics

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