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3rd International Conference on Computer Science and Computational Intelligence, ICCSCI 2018

作者： Pardamean, Bens Cenggoro, Tjeng Wawan Rahutomo, Reza Budiarto, Arif Karuppiah, Ettikan Kandasamy Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia NVIDIA Corporation United States

Breast cancer is one of the deadliest cancer for female nowadays. Despite of the rapid advancement in medical image analysis with the rise of deep learning, development of breast cancer detection system is limited due to relatively small size of the publicly available mammogram dataset. In this paper, we discover an effective configuration for transfer learning from Chest X-Ray pre-trained Convolutional Neural Network to overcome the small-size mammogram dataset problem. We found that the best configuration achieve 90.38% validation accuracy for modified. © 2018 The Authors. Published by Elsevier Ltd.

关键词： Deep learning

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Crowdsourcing annotation system of object counting dataset for deep learning algorithm 2

Crowdsourcing annotation system of object counting dataset f...

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2nd International Conference on Eco Engineering Development 2018, ICEED 2018

作者： Cenggoro, Tjeng Wawan Tanzil, Fidelson Aslamiah, Ayu Hidayah Karuppiah, Ettikan Kandasamy Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia NVIDIA Corporation Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

Deep Learning is currently the state-of-the-art technique for various Computer Vision tasks, including object counting. Despite of its high performance, Deep Learning requires a gigantic amount of training data to show its best result. Getting this massive data in reasonable time requires a proper strategy such as crowdsourcing. However, in case of object counting, we found no crowdsourcing system able to effectively collect necessary data. To tackle this problem, we develop a crowdsourcing system to annotate image for object counting dataset. This system is also equipped with validation system to ensure the quality of collected dataset. © 2018 Institute of Physics Publishing. All rights reserved.

关键词： Crowdsourcing

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Model of metabolism and gene expression predicts proteome allocation in Pseudomonas putida

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NPJ systems biology and applications 2025年第1期11卷 55页

作者： Juan D Tibocha-Bonilla Vishant Gandhi Chloe Lieng Oriane Moyne Rodrigo Santibáñez-Palominos Karsten Zengler Bioinformatics and Systems Biology Graduate Program University of California San Diego 9500 Gilman Drive La Jolla CA 92093-0760 USA. Department of Bioengineering University of California San Diego La Jolla CA 92093-0412 USA. Department of Pediatrics University of California San Diego 9500 Gilman Drive La Jolla CA 92093-0760 USA. Department of Bioengineering University of California San Diego La Jolla CA 92093-0412 USA. kzengler@ucsd.edu. Department of Pediatrics University of California San Diego 9500 Gilman Drive La Jolla CA 92093-0760 USA. kzengler@ucsd.edu. Center for Microbiome Innovation University of California San Diego 9500 Gilman Drive La Jolla CA 92093-0403 USA. kzengler@ucsd.edu. Program in Materials Science and Engineering University of California San Diego 9500 Gilman Drive La Jolla CA 92093-0418 USA. kzengler@ucsd.edu.

The genome-scale model of metabolism and gene expression (ME-model) for Pseudomonas putida KT2440, iPpu1676-ME, provides a comprehensive representation of biosynthetic costs and proteome allocation. Compared to a metabolic-only model, iPpu1676-ME significantly expands on gene expression, macromolecular assembly, and cofactor utilization, enabling accurate growth predictions without additional constraints. Multi-omics analysis using RNA sequencing and ribosomal profiling data revealed translational prioritization in P. putida, with core pathways, such as nicotinamide biosynthesis and queuosine metabolism, exhibiting higher translational efficiency, while secondary pathways displayed lower priority. Notably, the ME-model significantly outperformed the M-model in alignment with multi-omics data, thereby validating its predictive capacity. Thus, iPpu1676-ME offers valuable insights into P. putida's proteome allocation and presents a powerful tool for understanding resource allocation in this industrially relevant microorganism.

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Author Correction: Comprehensive molecular characterization of mitochondrial genomes in human cancers

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Nature genetics 2023年第6期55卷 1078页

作者： Yuan Yuan Young Seok Ju Youngwook Kim Jun Li Yumeng Wang Christopher J Yoon Yang Yang Inigo Martincorena Chad J Creighton John N Weinstein Yanxun Xu Leng Han Hyung-Lae Kim Hidewaki Nakagawa Keunchil Park Peter J Campbell Han Liang Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center Houston TX USA. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK. Graduate School of Medical Science and Engineering Korea Advanced Institute of Science and Technology Daejeon Korea. Department of Health Science and Technology Samsung Advanced Institute for Health Science and Technology Sungkyunkwan University School of Medicine Seoul Korea. Samsung Genome Institute Samsung Medical Center Seoul Korea. Quantitative and Computational Biosciences Graduate Program Baylor College of Medicine Houston TX USA. Division of Biostatistics The University of Texas Health Science Center at Houston School of Public Health Houston TX USA. Department of Medicine and Dan L. Duncan Cancer Center Division of Biostatistics Baylor College of Medicine Houston TX USA. Department of Systems Biology The University of Texas MD Anderson Cancer Center Houston TX USA. Department of Applied Mathematics and Statistics Johns Hopkins University Baltimore MD USA. Department of Biochemistry and Molecular Biology The University of Texas Health Science Center at Houston McGovern Medical School Houston TX USA. Department of Biochemistry Ewha Womans University School of Medicine Seoul Korea. Laboratory for Cancer Genomics RIKEN Center for Integrative Medical Sciences Yokohama Japan. Division of Hematology/Oncology Samsung Medical Center Sungkyunkwan University School of Medicine Seoul Korea. kpark@skku.edu. Samsung Advanced Institute for Health Sciences and Technology Sungkyunkwan University School of Medicine Seoul South Korea. kpark@skku.edu. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK. pc8@sanger.ac.uk. Department of Haematology University of Cambridge Cambridge UK. pc8@sanger.ac.uk. Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center Houston TX USA. hliang1@mdanderson.org. Quantitative and Comp

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An Evaluation of Deep Neural Network Performance on Limited Protein Phosphorylation Site Prediction Data

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Procedia Computer Science 2019年 157卷 25-30页

作者： Favorisen Rosyking Lumbanraja Bharuno Mahesworo Tjeng Wawan Cenggoro Arif Budiarto Bens Pardamean Department of Computer Science Faculty of Mathematics and Natural Science University of Lampung Jalan Prof. Dr. Sumantri Brojonegoro No.17 35145 Bandar Lampung Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

One of the common and important post-translational modification (PTM) types is phosphorylation. Protein phosphorylation is used to regulate various enzyme and receptor activations which include signal pathways. There have been many significant studies conducted to predict phosphorylation sites using various machine learning methods. Recently, several researchers claimed deep learning based methods as the best methods for phosphorylation sited prediction. However, the performance of these methods were backed up with the massive training data used in the researches. In this paper, we study the performance of simple deep neural network on the limited data generally used prior to deep learning employment. The result shows that a deep neural network can still achieve comparable performance in the limited data settings.

关键词： Phosphorylation Site Prediction Protein Phosphorylation Deep Learning Deep Neural Network

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An FGFR-p53 developmental signaling axis drives salivary cancer progression

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Oncogene 2025年 2025 Jun 2页

作者： Adele M Musicant Julia M R Billington Jeffrey S Damrauer Jennifer L Modliszewski Luane J B Landuau Yi-Hsuan Tsai Jay H Mehta John Powers Renee Betancourt Radhika Sekhri Ricardo J Padilla Juan C Hernandez-Prera D Neil Hayes Trevor G Hackman Omer Gokcumen Sarah M Knox Antonio L Amelio Graduate Curriculum in Genetics and Molecular Biology Biological and Biomedical Sciences Program School of Medicine The University of North Carolina at Chapel Hill Chapel Hill NC USA. Cancer Biology PhD Program University of South Florida Tampa FL USA. Department of Tumor Microenvironment and Metastasis H. Lee Moffitt Cancer Center & Research Institute Tampa FL USA. Department of Medicine Division of Oncology School of Medicine The University of North Carolina at Chapel Hill Chapel Hill NC USA. Bioinformatics Core Lineberger Comprehensive Cancer Center School of Medicine The University of North Carolina at Chapel Hill Chapel Hill NC USA. Department of Biological Sciences University at Buffalo Buffalo NY USA. Department of Pathology and Laboratory Medicine School of Medicine The University of North Carolina at Chapel Hill Chapel Hill NC USA. Center for Cancer Research University of Tennessee Health Sciences Center Memphis TN USA. Division of Diagnostic Sciences Adams School of Dentistry University of North Carolina at Chapel Hill Chapel Hill NC USA. Department of Pathology H. Lee Moffitt Cancer Center & Research Institute Tampa FL USA. Department of Otolaryngology/Head and Neck Surgery School of Medicine The University of North Carolina at Chapel Hill Chapel Hill NC USA. Department of Cell and Tissue Biology University of California San Francisco San Francisco CA USA. Program in Craniofacial Biology University of California San Francisco San Francisco CA USA. Department of Tumor Microenvironment and Metastasis H. Lee Moffitt Cancer Center & Research Institute Tampa FL USA. antonio.amelio@***. Department of Head and Neck-Endocrine Oncology H. Lee Moffitt Cancer Center & Research Institute Tampa FL USA. antonio.amelio@***.

Mucoepidermoid carcinoma (MEC) is the most frequently occurring salivary gland malignancy. Here, we investigated transcriptomic profiles of human fetal and adult salivary glands and MEC tumors to assess programs involved in MEC progression. Molecular and genetic analyses revealed that MEC tumors and fetal salivary glands share proliferative and developmental gene expression profiles that implicate an FGFR-p53 signaling axis in salivary MEC progression. Based on these findings, we developed a genetically engineered mouse model of advanced MEC via targeted expression of the CRTC1-MAML2 oncogene in salivary ductal cells. Specifically, CRTC1-MAML2 expression combined with p53 dysregulation in salivary ducts rewires FGF signaling to drive formation of tumors with histological and molecular features of high-grade MEC. The combined bioinformatics and mouse modeling of this study demonstrate that salivary MEC progression is underpinned by reactivation of developmental signaling programs and suggests a role for FGFR targeted therapies in the treatment of high-grade MEC.

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Glycyrrhizic acid conjugates with amino acid methyl esters target the main protease, exhibiting antiviral activity against wild-type and nirmatrelvir-resistant SARS-CoV-2 variants

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Antiviral research 2024年 227卷 105920页

作者： Uyen Nguyen Phuong Le Yu-Jen Chang Chih-Hao Lu Yeh Chen Wen-Chi Su Shao-Ting Chao Lia A Baltina Svetlana F Petrova Sin-Rong Li Mien-Chie Hung Michael M C Lai Lidia A Baltina Cheng-Wen Lin Graduate Institute of Biological Science and Technology China Medical University Taichung Taiwan Department of Medical Laboratory Science and Biotechnology China Medical University Taichung Taiwan. The Ph.D. Program of Biotechnology and Biomedical Industry China Medical University Taichung Taiwan. The Ph.D. Program of Biotechnology and Biomedical Industry China Medical University Taichung Taiwan Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan. Department of Food Science and Biotechnology National Chung Hsing University Taichung 402 Taiwan. Graduate Institute of Biomedical Sciences China Medical University Taichung Taiwan. Ufa Institute of Chemistry Ufa Federal Research Center of the Russian Academy of Sciences Russia. Department of Laboratory Medicine China Medical University Hospital Taichung 404327 Taiwan. Research Center for Cancer Biology China Medical University Taichung 404327 Taiwan Center for Molecular Medicine China Medical University Hospital Taichung 404327 Taiwan. Institute of Molecular Biology Academia Sinica Taipei 115201 Taiwan. Ufa Institute of Chemistry Ufa Federal Research Center of the Russian Academy of Sciences Russia. Electronic address: baltina@anrb.ru. Department of Medical Laboratory Science and Biotechnology China Medical University Taichung Taiwan Graduate Institute of Biomedical Sciences China Medical University Taichung Taiwan Department of Medical Laboratory Science and Biotechnology Asia University Taichung Taiwan. Electronic address: cwlin@mail.cmu.edu.tw.

COVID-19 pandemic is predominantly caused by SARS-CoV-2, with its main protease, Mpro, playing a pivotal role in viral replication and serving as a potential target for inhibiting different variants. In this study, potent Mpro inhibitors were identified from glycyrrhizic acid (GL) derivatives with amino acid methyl/ethyl esters. Out of the 17 derivatives semisynthesized, Compounds 2, 6, 9, and 15, with methionine methyl esters, D-tyrosine methyl esters, glutamic acid methyl esters, and methionines in the carbohydrate moiety, respectively, significantly inhibited wild-type SARS-CoV-2 Mpro-mediated proteolysis, with IC50 values ranging from 0.06 μM to 0.84 μM. They also demonstrated efficacy in inhibiting trans-cleavage by mutant Mpro variants (Mpro_P132H, Mpro_E166V, Mpro_P168A, Mpro_Q189I), with IC50 values ranging from 0.05 to 0.92 μM, surpassing nirmatrelvir (IC50: 1.17-152.9 μM). Molecular modeling revealed stronger interactions with Valine166 in the structural complex of Mpro_E166V with the compounds compared to nirmatrelvir. Moreover, these compounds efficiently inhibited the post-entry viral processes of wild-type SARS-CoV-2 single-round infectious particles (SRIPs), mitigating viral cytopathic effects and reducing replicon-driven GFP reporter signals, as well as in vitro infectivity of wild-type, Mpro_E166V, and Mpro_Q189I SRIPs, with EC50 values ranging from 0.02 to 0.53 μM. However, nirmatrelvir showed a significant decrease in inhibiting the replication of mutant SARS-CoV-2 SRIPs carrying Mpro_E166V (EC50: >20 μM) and Mpro_Q189I (EC50: 13.2 μM) compared to wild-type SRIPs (EC50: 0.06 μM). Overall, this study identifies four GL derivatives as promising lead compounds for developing treatments against various SARS-CoV-2 strains, including Omicron, and nirmatrelvir-resistant variants.

关键词： Glycyrrhizic acid derivative Inhibitor Mpro Nirmatrelvir-resistant variant Omicron SARS-CoV-2

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The evolutionary history of 2,658 cancers (vol 578, pg 122, 2020)

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NATURE 2023年第7948期614卷 E42-E42页

作者： Gerstung, Moritz Jolly, Clemency Leshchiner, Ignaty Dentro, Stefan C. Gonzalez, Santiago Rosebrock, Daniel Mitchell, Thomas J. Rubanova, Yulia Anur, Pavana Yu, Kaixian Tarabichi, Maxime Deshwar, Amit Wintersinger, Jeff Kleinheinz, Kortine Vazquez-Garcia, Ignacio Haase, Kerstin Jerman, Lara Sengupta, Subhajit Macintyre, Geoff Malikic, Salem Donmez, Nilgun Livitz, Dimitri G. Cmero, Marek Demeulemeester, Jonas Schumacher, Steven Fan, Yu Yao, Xiaotong Lee, Juhee Schlesner, Matthias Boutros, Paul C. Bowtell, David D. Zhu, Hongtu Getz, Gad Imielinski, Marcin Beroukhim, Rameen Sahinalp, S. Cenk Ji, Yuan Peifer, Martin Markowetz, Florian Mustonen, Ville Yuan, Ke Wang, Wenyi Morris, Quaid D. Spellman, Paul T. Wedge, David C. Van Loo, Peter European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Wellcome Sanger Institute Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany University of Ljubljana Ljubljana Slovenia The Francis Crick Institute London UK Big Data Institute University of Oxford Oxford UK Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Big Data Institute Li Ka Shing Centre University of Oxford Oxford UK University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Department of Applied Mathematics and Theoretical Physics Centre for Mathematical Sciences University of Cambridge Cambridge UK Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY USA Department of Statistics Columbia University New York NY USA Department of Haematology University of Cambridge Cambridge UK University of Leuven Leuven Belgium Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Charlestown MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Dana-Farber Cancer Institute Boston MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Oxford NIHR Biomedical Research Centre Oxford UK Oxford NIHR Biomedical Research Centre University of Oxford Oxford UK University of Toronto Toronto Ontario Canada Vector Institute Toronto Ontario Canada Vector Institute Toronto ON Canada Department of Computer Science University of Toronto Toronto ON Canada Ontario Institute for Cancer Research Toronto Ontario Canada University of California Los Angeles

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Author Correction: Genomic footprints of activated telomere maintenance mechanisms in cancer (Dec, 10.1038/s41467-019-13824-9, 2022)

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NATURE COMMUNICATIONS 2022年第1期13卷 1-1页

作者： Sieverling, Lina Hong, Chen Koser, Sandra D. Ginsbach, Philip Kleinheinz, Kortine Hutter, Barbara Braun, Delia M. Cortes-Ciriano, Isidro Xi, Ruibin Kabbe, Rolf Park, Peter J. Eils, Roland Schlesner, Matthias Brors, Benedikt Rippe, Karsten Jones, David T. W. Feuerbach, Lars Division of Applied Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Faculty of Biosciences Heidelberg University 69120 Heidelberg Germany Division of Applied Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Biosciences Heidelberg University Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Department for Bioinformatics and Functional Genomics Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant 69120 Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Institute of Pharmacy and Molecular Biotechnology and BioQuant Heidelberg University Heidelberg Germany German Cancer Consortium (DKTK) Heidelberg Germany National Center for Tumor Diseases (NCT) Heidelberg Heidelberg Germany Heidelberg Center for Personalized Oncology (DKFZ-HIPO) German Cancer Research Center (DKFZ) Heidelberg Germany German Cancer Consortium (DKTK) German Cancer Research Center (DKFZ) Heidelberg Germany Division of Chromatin Networks German Cancer Research Center (DKFZ) and BioQuant 69120 Heidelberg Germany Department of Biomedical Informatics Harvard Medical School Boston Massachusetts 02115 USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge CB2 1EW UK Ludwig Center at Harvard Medical School Boston Massachusetts 02115 USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge UK Ludwig Center at Harvard Medical School Boston MA USA School of Mathematical Sciences and Center for Statistical Science Peking University Beijing 100871 China Heidelberg University Heidelberg Germany New BIH Digital Health Center Berlin Institute of Health (BIH) and Charité - Universitätsmedizin Berlin Berlin Germany Bioi

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Hypoxia-responsive circular RNA circAAGAB reduces breast cancer malignancy by activating p38 MAPK and sponging miR-378 h

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Cancer cell international 2023年第1期23卷 45页

作者： Kuan-Yi Lee Chia-Ming Liu Li-Han Chen Chien-Yueh Lee Tzu-Pin Lu Li-Ling Chuang Liang-Chuan Lai Graduate Institute of Physiology College of Medicine National Taiwan University Taipei Taiwan. Institute of Fisheries Science College of Life Science National Taiwan University Taipei Taiwan. Department of Life Science College of Life Science National Taiwan University Taipei Taiwan. Master Program for Biomedical Engineering College of Biomedical Engineering China Medical University Taichung Taiwan. Institute of Epidemiology and Preventive Medicine Department of Public Health National Taiwan University Taipei Taiwan. School of Physical Therapy and Graduate Institute of Rehabilitation Science College of Medicine Chang Gung University Taoyuan Taiwan. lchuang@gap.cgu.edu.tw. Department of Physical Medicine and Rehabilitation Chang Gung Memorial Hospital at Linkou Taoyuan Taiwan. lchuang@gap.cgu.edu.tw. Graduate Institute of Physiology College of Medicine National Taiwan University Taipei Taiwan. llai@ntu.edu.tw. Bioinformatics and Biostatistics Core Center of Genomic and Precision Medicine National Taiwan University Taipei Taiwan. llai@ntu.edu.tw.

BACKGROUND:Breast cancer is a prevalent disease in women, with high prevalence worldwide. The hypoxic microenvironment of solid tumors develops during the progress of carcinogenesis and leads to greater malignancy and treatment resistance. Recently, accumulating evidence indicates that non-coding RNAs, such as circular RNAs (circRNAs), play a pivotal role in altering cellular functions. However, the underlying mechanisms of circRNAs in breast cancer are still unclear. Therefore, the purpose of this study was to investigate the role of a tumor-suppressive circRNA, circAAGAB, in breast cancer by assuming down-regulation of circAAGAB under hypoxia and the properties of a tumor suppressor. METHODS:Firstly, circAAGAB was identified from expression profiling by next generation sequencing. Next, the stability of circAAGAB increased by interacting with the RNA binding protein FUS. Moreover, cellular and nuclear fractionation showed that most circAAGAB resided in the cytoplasm and that it up-regulated KIAA1522, NKX3-1, and JADE3 by sponging miR-378 h. Lastly, the functions of circAAGAB were explored by identifying its down-stream genes using Affymetrix microarrays and validated by in vitro assays. RESULTS:The results showed that circAAGAB reduced cell colony formation, cell migration, and signaling through p38 MAPK pathway, as well as increased radiosensitivity. CONCLUSION:These findings suggest that the oxygen-responsive circAAGAB acts as a tumor suppressor in breast cancer, and may contribute to the development of a more specific therapeutic regimen for breast cancer.

关键词： Breast cancer Hypoxia Radiosensitivity Tumor suppressor circAAGAB miRNA sponge p38 MAPK

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