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作者： Asadi, Behrang Maurya, Mano Ram Tartakovsky, Daniel M. Subramaniam, Shankar Departments of Bioengineering and Mechanical and Aerospace Engineering University of California San Diego Danaher Labs la Jolla Santa ClaraCA United States San Diego Supercomputer Center Department of Bioengineering University of California San Diego San DiegoCA United States Department of Mechanical Aerospace Engineering University of California San Diego San DiegoCA United States Department of Bioengineering Departments of Computer Science and Engineering Cellular and Molecular Medicine and the Graduate Program in Bioinformatics University of California San Diego San DiegoCA United States

Reconstruction of biological and biochemical networks is a crucial step in extracting knowledge and causal information from large biological data sets. This task is particularly challenging when dealing with time-series data from dynamic networks. We have developed a new method, called doubly penalized least absolute shrinkage and selection operator (DPLASSO), for the reconstruction of dynamic biological networks. Our method consists of two components: statistical significance testing of model coefficients and penalized/constrained optimization. A partial least squares (PLS) with statistical significance testing acts as a supervisory-level filter to extract the most informative components of the network from a data set. Then, LASSO with extra weights on the smaller parameters identified in the first layer is employed to retain the main predictors and to set the smallest coefficients to zero. We present two case studies to compare the relative performance of DPLASSO and LASSO in terms of several metrics, such as sensitivity, specificity, and accuracy. The first case study employs a synthetic data set;it demonstrates that DPLASSO substantially improves network reconstruction in terms of accuracy and specificity. The second case study relies on simulated data sets for cell division cycle of fission yeast and shows that DPLASSO overall outperforms LASSO and PLS in terms of sensitivity, specificity, and accuracy. Combining PLS with statistical significance testing and LASSO provides good performance with respect to several metrics. DPLASSO is well suited for reconstruction of networks with low and moderate density. For high-density networks, high specificity is desired, making PLS a more suitable approach. © 1963-2012 IEEE.

关键词： Least squares approximations

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Author Correction: Wiskott-Aldrich syndrome protein forms nuclear condensates and regulates alternative splicing

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Nature communications 2022年第1期13卷 5291页

作者： Baolei Yuan Xuan Zhou Keiichiro Suzuki Gerardo Ramos-Mandujano Mengge Wang Muhammad Tehseen Lorena V Cortés-Medina James J Moresco Sarah Dunn Reyna Hernandez-Benitez Tomoaki Hishida Na Young Kim Manal M Andijani Chongwei Bi Manching Ku Yuta Takahashi Jinna Xu Jinsong Qiu Ling Huang Christopher Benner Emi Aizawa Jing Qu Guang-Hui Liu Zhongwei Li Fei Yi Yanal Ghosheh Changwei Shao Maxim Shokhirev Patrizia Comoli Francesco Frassoni John R Yates Xiang-Dong Fu Concepcion Rodriguez Esteban Samir Hamdan Juan Carlos Izpisua Belmonte Mo Li Bioscience Program Biological and Environmental Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Kingdom of Saudi Arabia. Gene Expression Laboratory Salk Institute for Biological Studies 10010 North Torrey Pines Road La Jolla CA 92037 USA. Institute for Advanced Co-Creation Studies Graduate School of Engineering Science Osaka University Osaka Japan. Department of Cell Biology Scripps Research Institute La Jolla CA 92037 USA. The Waitt Advanced Biophotonics Core Facility Salk Institute for Biological Studies 10010 North Torrey Pines Road La Jolla CA 92037 USA. Altos Labs Inc. 5510 Morehouse Drive Suite 300 San Diego CA 92121 USA. Laboratory of Biological Chemistry School of Pharmaceutical Sciences Wakayama Medical University 25-1 Shitibancho Wakayama Wakayama 640-8156 Japan. Next-generation sequencing core Salk Institute for Biological Studies 10010 North Torrey Pines Road La Jolla CA 92037 USA. Life Science Center Tsukuba Advanced Research Alliance University of Tsukuba 1-1-1 Tennoudai Tsukuba Ibaraki 305-8577 Japan. Department of Cellular & Molecular Medicine University of California at San Diego La Jolla CA 92093 USA. Integrative Genomics and Bioinformatics Core Salk Institute for Biological Studies 10010 North Torrey Pines Road La Jolla CA 92037 USA. State Key Laboratory of Membrane Biology Institute of Zoology Chinese Academy of Sciences Beijing 100101 China. University of Southern California 1333 San Pablo Street MMR 618 Los Angeles CA 90033 USA. Ambys Medicines 131 Oyster Point Blvd. Suite 200 South San Francisco CA 94080 USA. Pediatric Hematology/Oncology and Cell Factory Fondazione IRCCS Policlinico San Matteo Pavia Italy. Department of Research Laboratories and Director of Center for Stem Cell and Cell Therapy Instituto G. Gaslini Children Hospital Scientific Institute 16147 Genova Italy. Bioscience Program Biological and Environmental Science and Engineering

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Functional annotation of chemical libraries across diverse biological processes (vol 13, pg 982, 2017)

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NATURE CHEMICAL BIOLOGY 2017年第12期13卷 1286-1286页

作者： Piotrowski, Jeff S. Li, Sheena C. Deshpande, Raamesh Simpkins, Scott W. Nelson, Justin Yashiroda, Yoko Barber, Jacqueline M. Safizadeh, Hamid Wilson, Erin Okada, Hiroki Gebre, Abraham A. Kubo, Karen Torres, Nikko P. LeBlanc, Marissa A. Andrusiak, Kerry Okamoto, Reika Yoshimura, Mami DeRango-Adem, Eva van Leeuwen, Jolanda Shirahige, Katsuhiko Baryshnikova, Anastasia Brown, Grant W. Hirano, Hiroyuki Costanzo, Michael Andrews, Brenda Ohya, Yoshikazu Osada, Hiroyuki Yoshida, Minoru Myers, Chad L. Boone, Charles RIKEN Center for Sustainable Resource Science Wako Saitama Japan Department of Computer Science and Engineering University of Minnesota-Twin Cities Minneapolis Minnesota USA Bioinformatics and Computational Biology Program University of Minnesota-Twin Cities Minneapolis Minnesota USA Department of Electrical and Computer Engineering University of Minnesota-Twin Cities Minneapolis Minnesota USA Department of Integrated Biosciences Graduate School of Frontier Sciences University of Tokyo Kashiwa Chiba Japan Donnelly Centre University of Toronto Toronto Ontario Canada Institute of Molecular and Cellular Biosciences Center for Epigenetic Disease University of Tokyo Bunkyo-ku Tokyo Japan Lewis-Sigler Institute for Integrative Genomics Princeton University Princeton New Jersey USA Department of Biochemistry University of Toronto Toronto Ontario Canada.

Chemical-genetic approaches offer the potential for unbiased functional annotation of chemical libraries. Mutations can alter the response of cells in the presence of a compound, revealing chemical-genetic interactions that can elucidate a compound’s mode of action. We developed a highly parallel, unbiased yeast chemical-genetic screening system involving three key components. First, in a drug-sensitive genetic background, we constructed an optimized diagnostic mutant collection that is predictive for all major yeast biological processes. Second, we implemented a multiplexed (768-plex) barcode-sequencing protocol, enabling the assembly of thousands of chemical-genetic profiles. Finally, based on comparison of the chemical-genetic profiles with a compendium of genome-wide genetic interaction profiles, we predicted compound functionality. Applying this high-throughput approach, we screened seven different compound libraries and annotated their functional diversity. We further validated biological process predictions, prioritized a diverse set of compounds, and identified compounds that appear to have dual modes of action.

关键词： Chemical genetics High-throughput screening Networks and systems biology Target identification

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AKT3-mediated IWS1 phosphorylation promotes the proliferation of EGFR-mutant lung adenocarcinomas through cell cycle-regulated U2AF2 RNA splicing

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Nature communications 2021年第1期12卷 4624页

作者： Georgios I Laliotis Evangelia Chavdoula Maria D Paraskevopoulou Abdul Kaba Alessandro La Ferlita Satishkumar Singh Vollter Anastas Keith A Nair Arturo Orlacchio Vasiliki Taraslia Ioannis Vlachos Marina Capece Artemis Hatzigeorgiou Dario Palmieri Christos Tsatsanis Salvatore Alaimo Lalit Sehgal David P Carbone Vincenzo Coppola Philip N Tsichlis Department of Cancer Biology and Genetics The Ohio State University Columbus OH USA. glaliot1@jhmi.edu. The Ohio State University Comprehensive Cancer Center-Arthur G. James Cancer Hospital and Richard J. Solove Research Institute Columbus OH USA. glaliot1@jhmi.edu. School of Medicine University of Crete Heraklion Crete Greece. glaliot1@jhmi.edu. Department of Oncology Sidney Kimmel Comprehensive Cancer Center Johns Hopkins School of Medicine Baltimore MD USA. glaliot1@jhmi.edu. Department of Cancer Biology and Genetics The Ohio State University Columbus OH USA. The Ohio State University Comprehensive Cancer Center-Arthur G. James Cancer Hospital and Richard J. Solove Research Institute Columbus OH USA. Molecular Oncology Research Institute Tufts Medical Center Boston MA USA. Department of Clinical and Experimental Medicine Bioinformatics Unit University of Catania Catania Italy. Department of Medicine Division of Hematology The Ohio State University Columbus OH USA. Tufts Graduate School of Biomedical Sciences Program in Genetics Boston MA USA. Center of Basic Research Biomedical Research Foundation of the Academy of Athens Athens Greece. DIANA-Lab Hellenic Pasteur Institute Athens Greece. Department Of Pathology Beth Israel-Deaconess Medical Center Harvard Medical School Boston MA USA. School of Medicine University of Crete Heraklion Crete Greece. Institute of Molecular Biology and Biotechnology Foundation for Research and Technology Hellas Heraklion Crete Greece. Department of Internal Medicine Division of Medical Oncology The Ohio State University Medical Center Columbus OH USA. Department of Cancer Biology and Genetics The Ohio State University Columbus OH USA. Philip.tsichlis@osumc.edu. The Ohio State University Comprehensive Cancer Center-Arthur G. James Cancer Hospital and Richard J. Solove Research Institute Columbus OH USA. Philip.tsichlis@osumc.edu. Tufts Graduate School of Biomedical Sciences Program in Genetics Boston MA USA.

AKT-phosphorylated IWS1 regulates alternative RNA splicing via a pathway that is active in lung cancer. RNA-seq studies in lung adenocarcinoma cells lacking phosphorylated IWS1, identified a exon 2-deficient U2AF2 splice variant. Here, we show that exon 2 inclusion in the U2AF2 mRNA is a cell cycle-dependent process that is regulated by LEDGF/SRSF1 splicing complexes, whose assembly is controlled by the IWS1 phosphorylation-dependent deposition of histone H3K36me3 marks in the body of target genes. The exon 2-deficient U2AF2 mRNA encodes a Serine-Arginine-Rich (RS) domain-deficient U2AF65, which is defective in CDCA5 pre-mRNA processing. This results in downregulation of the CDCA5-encoded protein Sororin, a phosphorylation target and regulator of ERK, G2/M arrest and impaired cell proliferation and tumor growth. Analysis of human lung adenocarcinomas, confirmed activation of the pathway in EGFR-mutant tumors and showed that pathway activity correlates with tumor stage, histologic grade, metastasis, relapse after treatment, and poor prognosis.

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Discovery Biological Motifs Using Heuristics Approaches

Discovery Biological Motifs Using Heuristics Approaches

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Brazilian Conference on Intelligent Systems (BRACIS)

作者： Jader M. Caldonazzo Garbelini André Yoshiaki Kashiwabara Danilo Sipoli Sanches Graduate Program in Bioinformatics (PPGBIOINFO) Federal University of Technology Cornélio Procópio PR Brazil

ISBN: (纸本)9781509035670

The identification of transcription factors binding sites (TFBS) - also called motifs - in DNA sequences is the first step to understanding how works gene regulation. Recognizing these patterns in the promoter regions of co-expressed genes is a determining key for this. Although there are several algorithms for this purpose, the problem is still far from being solved because of the great diversity of gene expression and the binding sites low specificity. State of the art algorithms have limitations, such as the high number of false positives and low accuracy for Identifying weak motifs. In this article we proposed a new approach based on memetic algorithms (DMMA) for discovery mofifs. The proposed approach was developed using evolutionary computation along with the local search algorithms simulated annealing and variable neighborhood search. To attest the algorithm ability, tests were conducted in four datasets - two real and two synthetic - and the results were compared with other approaches in the literature.

关键词： Sociology Statistics Complexity theory Biology Probability Mathematical model Simulated annealing

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Targeting the HTLV-I-Regulated BATF3/IRF4 Transcriptional Network in Adult T-Cell Leukemia/Lymphoma

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BLOOD 2017年第Sup1期130卷 731-731页

作者： Nakagawa, Masao Shaffer, Arthur L. Ceribelli, Michele Zhang, Meili Wright, George Huang, Da Wei Xiao, Wenming Powell, John Petrus, Michael N. Yang, Yibin Phelan, James D. Kohlhammer, Holger Yoo, Hee Min Yang, Yandan Xu, Weihong Yu, Xin Zhao, Hong Bryant, Bonita R. Maeda, Michiyuki Waldmann, Thomas A. Staudt, Lou Department of Hematology Hokkaido University Graduate School of Medicine Sapporo Japan NIH NCI CCR Bethesda MD National Cancer Institute National Institutes of Health Bethesda NCATS NIH ROCKVILLE National Cancer Institute National Institutes of Health Center for Cancer Research Bethesda MD NCI NIH Bethesda Division of Bioinformatics and Biostatistics NCTR/FDA Jefferson Center for Information Technology National Institutes of Health Bethesda Blood Cell Development and Function Program Fox Chase Cancer Center Philadelphi Institute for Virus Research Kyoto University Kyoto Japan National Cancer Institute National Institutes of Health Bethesda MD National Cancer Institute National Institutes of Health Bethesda MD

After neonatal HTLV-I infection through breast feeding, approximately 5% of HTLV-I carriers eventually develop Adult T-Cell Leukemia/Lymphoma (ATLL) with a latency of ~50 years, suggesting that acquired genetic and epigenetic changes in cellular genes act in concert with HTLV-I to initiate and maintain oncogenic transformation. We and others have recently utilized next generation sequencing technology to identify mutated genes that could be pivotal in the pathogenesis of ATLL. However, due to the complexity of genomic/epigenetic alteration in the ATLL genome, the identification of indispensable genes for proliferation and/or survival of ATLL cells remains a formidable challenge. To discover essential regulatory networks that are required for the proliferation and survival of ATLL cells, we performed a pooled shRNA screen in 8 ATLL cell lines using a library enriched for shRNAs targeting lymphoid regulatory factors and discovered that two BATF3 shRNAs and one IRF4 shRNA were highly toxic for all ATLL lines, but had little if any effect in other T cell and B cell lines. It is recently shown that a transcriptional complex of Irf4 and Batf binds to AP1-IRF composite (AICE) DNA motifs and plays key roles in the differentiation and function of certain mouse helper T cell subsets. A close paralogue of Batf, Batf3, is an indispensable transcription factor in a mouse dendritic cell subset, but also appears to play a redundant role with Batf in the differentiation of TH2 cells and can substitute for Batf in Batf knockout T cells. Our observations from shRNA screening suggested that IRF4 and BATF3 may cooperate to drive a transcriptional program that is essential for ATLL viability. We next used genome-wide chromatin precipitation (ChIP-seq) to identify the loci that are bound by BATF3 and IRF4. The set of binding peaks and the associated genes in IRF4 and BATF3 ChIP-seq intersected significantly. By integrating the ChIP-seq and gene expression profiling data of shBATF3- and s

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Highly accurate model for prediction of lung nodule malignancy with ct scans

arXiv

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arXiv 2018年

作者： Causey, Jason Zhang, Junyu Ma, Shiqian Jiang, Bo Qualls, Jake Politte, David G. Prior, Fred Zhang, Shuzhong Huang, Xiuzhen Department of Computer Science Arkansas State University JonesboroAR72467 United States Department of Industrial and Systems Engineering University of Minnesota MinneapolisMN55455 United States Department of Systems Engineering and Engineering Management Chinese University of Hong Kong Shatin N.T. Hong Kong Research Center for Management Science and Data Analytics School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai200433 China Mallinckrodt Institute of Radiology Washington University St. LouisMO63110 United States Department of Biomedical Informatics University of Arkansas for Medical Sciences Little RockAR72205 United States UALR/UAMS Joint Graduate Program in Bioinformatics Little RockAR72204 United States

Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://***/NoduleX. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

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Evolution at the edge of expanding populations

arXiv

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arXiv 2017年

作者： Deforet, Maxime Carmona-Fontaine, Carlos Korolev, Kirill S. Xavier, Joao B. Sorbonne Université Centre National de la Recherche Rcientifique Laboratoire Jean Perrin LJP Paris75005 France Center for Genomics and Systems Biology Department of Biology New York University New York CityNY10003 United States Department of Physics Graduate Program in Bioinformatics Boston University BostonMA02215 Program in Computational Biology Memorial Sloan-Kettering Cancer Center New York CityNY10065 United States

Predicting evolution of expanding populations is critical to control biological threats such as invasive species and cancer metastasis. Expansion is primarily driven by reproduction and dispersal, but nature abounds with examples of evolution where organisms pay a reproductive cost to disperse faster. When does selection favor this 'survival of the fastest?' We searched for a simple rule, motivated by evolution experiments where swarming bacteria evolved into an hyperswarmer mutant which disperses ∼ 100% faster but pays a growth cost of ∼ 10% to make many copies of its flagellum. We analyzed a two-species model based on the Fisher equation to explain this observation: the population expansion rate (v) results from an interplay of growth (r) and dispersal (D) and is independent of the carrying capacity: v = 2 √rD. A mutant can take over the edge only if its expansion rate (v2) exceeds the expansion rate of the established species' (v1);this simple condition (v2> v1) determines the maximum cost in slower growth that a faster mutant can pay and still be able to take over. Numerical simulations and time-course experiments where we tracked evolution by imaging bacteria suggest that our findings are general: less favorable conditions delay but do not entirely prevent the success of the fastest. Thus, the expansion rate defines a traveling wave fitness, which could be combined with trade-offs to predict evolution of expanding populations. Copyright © 2017, The Authors. All rights reserved.

关键词： Bacteria

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The importance of transparency and reproducibility in artificial intelligence research

arXiv

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arXiv 2020年

作者： Haibe-Kains, Benjamin Adam, George Alexandru Hosny, Ahmed Khodakarami, Farnoosh Waldron, Levi Wang, Bo McIntosh, Chris Kundaje, Anshul Greene, Casey S. Hoffman, Michael M. Leek, Jeffrey T. Huber, Wolfgang Brazma, Alvis Pineau, Joelle Tibshirani, Robert Hastie, Trevor Ioannidis, John P.A. Quackenbush, John Aerts, Hugo J.W.L. Princess Margaret Cancer Centre University Health Network TorontoON Canada Department of Medical Biophysics University of Toronto TorontoON Canada Department of Computer Science University of Toronto TorontoON Canada Ontario Institute for Cancer Research TorontoON Canada Vector Institute for Artificial Intelligence TorontoON Canada Program Brigham and Women's Hospital Harvard Medical School BostonMA United States Radiation Oncology and Radiology Dana-Farber Cancer Institute Brigham and Women's Department of Epidemiology and Biostatistics Institute for Implementation Science in Population Health CUNY Graduate School of Public Health and Health Policy New YorkNY United States Peter Munk Cardiac Centre University Health Network TorontoON Canada Hospital Harvard Medical School BostonMA United States Department of Genetics Stanford University School of Medicine StanfordCA United States Dept. of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Childhood Cancer Data Lab Alex's Lemonade Stand Foundation PhiladelphiaPA United States Department of Biostatistics Johns Hopkins Bloomberg School of Public Health BaltimoreMD United States European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany European Molecular Biology Laboratory European Bioinformatics Institute EMBL-EBI Hinxton United Kingdom McGill University MontrealQC Canada Montreal Institute for Learning Algorithms QC Canada StanfordCA United States Department of Biomedical Data Science Stanford University School of Medicine StanfordCA United States Departments of Medicine of Health Research and Policy and of Biomedical Data Science Stanford University School of Medicine StanfordCA United States Department of Statistics Stanford University School of Humanities and Sciences StanfordCA United States Department of Biostatistics Harvard T.H Chan School of Public Health BostonMA Un

In their study, McKinney et al. showed the high potential of artificial intelligence for breast cancer screening. However, the lack of detailed methods and computer code undermines its scientific value. We identify obstacles hindering transparent and reproducible AI research as faced by McKinney et al and provide solutions with implications for the broader field. Copyright © 2020, The Authors. All rights reserved.

关键词： Artificial intelligence

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Author Correction: Proteogenomic landscape of human pancreatic ductal adenocarcinoma in an Asian population reveals tumor cell-enriched and immune-rich subtypes

Nature cancer

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Nature cancer 2023年 2023 May 19页

作者： Do Young Hyeon Dowoon Nam Youngmin Han Duk Ki Kim Gibeom Kim Daeun Kim Jingi Bae Seunghoon Back Dong-Gi Mun Inamul Hasan Madar Hangyeore Lee Su-Jin Kim Hokeun Kim Sangyeop Hyun Chang Rok Kim Seon Ah Choi Yong Ryoul Kim Juhee Jeong Suwan Jeon Yeon Woong Choo Kyung Bun Lee Wooil Kwon Seunghyuk Choi Taewan Goo Taesung Park Young-Ah Suh Hongbeom Kim Ja-Lok Ku Min-Sik Kim Eunok Paek Daechan Park Keehoon Jung Sung Hee Baek Jin-Young Jang Daehee Hwang Sang-Won Lee School of Biological Sciences Seoul National University Seoul Republic of Korea. Department of Chemistry and Center for Proteogenome Research Korea University Seoul Republic of Korea. Department of Surgery and Cancer Research Institute Seoul National University College of Medicine Seoul Republic of Korea. Department of Anatomy and Cell Biology and Department of Biomedical Sciences Seoul National University College of Medicine Seoul Republic of Korea. Graduate School of Medical Science and Engineering Korea Advanced Institute of Science and Technology (KAIST) Seoul Republic of Korea. Creative Research Initiatives Center for Epigenetic Code and Diseases Seoul National University Seoul Republic of Korea. Department of Biological Sciences College of Natural Sciences and Department of Molecular Science and Technology Ajou University Suwon Republic of Korea. Department of Pathology Seoul National University College of Medicine Seoul Korea. Department of Computer Science Hanyang University Seoul Republic of Korea. Interdisciplinary Program in Bioinformatics Seoul National University Seoul Republic of Korea. Department of Statistics Seoul National University Seoul Republic of Korea. Korean Cell Line Bank Laboratory of Cell Biology Cancer Research Institute Seoul National University College of Medicine Seoul Korea. Department of New Biology Daegu Gyeongbuk Institute of Science and Technology Daegu Republic of Korea. Department of Biological Sciences College of Natural Sciences and Department of Molecular Science and Technology Ajou University Suwon Republic of Korea. dpark@ajou.ac.kr. Department of Anatomy and Cell Biology and Department of Biomedical Sciences Seoul National University College of Medicine Seoul Republic of Korea. keehoon.jung@snu.ac.kr. Institute of Allergy and Clinical Immunology Seoul National University Medical Research Center Seoul Republic of Korea. keehoon.jung@snu.ac.kr. School of Biological Sciences Seoul National University Seoul Republic of

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