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9th International Conference on bioinformatics and Computational Biology, BICOB 2017

作者： Gao, Xiang Lin, Huaiying Dong, Qunfeng Department of Public Health Sciences Loyola University Chicago Health Sciences Division MaywoodIL60153 United States Center for Biomedical Informatics Loyola University Chicago Health Sciences Division MaywoodIL60153 United States Bioinformatics Program Loyola University Chicago Lake Shore Campus ChicagoIL60660 United States Department of Computer Science Loyola University Chicago Water Tower Campus ChicagoIL60611 United States

ISBN: (纸本)9781943436071

Dysbiosis of microbial communities is associated with various human diseases, raising the possibility of using microbial compositions as biomarkers for disease diagnosis. We have developed a Bayes classifier by modeling microbial compositions with Dirichlet-Multinomial distributions, which are widely used to model multi-categorical count data with extra variation. The parameters of the Dirichlet-Multinomial distributions are estimated from training microbiome datasets based on maximum likelihood. The posterior probability of a microbiome sample belonging to disease or healthy category is calculated based on Bayes' theorem, using the likelihood values computed from the estimated Dirichlet-Multinomial distribution, as well as a prior probability estimated from the training microbiome dataset or previously published information on disease prevalence. When tested on real-world microbiome datasets, our method, called DBMC, consistently shows better classification accuracy than a previously published Bayesian method based on a Dirichlet-Multinomial Mixture model. A unique advantage of DMBC is its builtin automatic feature selection, capable of identifying a subset of microbial taxa with the best classification accuracy between different classes of samples based on cross-validation. This unique ability enables DMBC to maintain and even improve its accuracy at modeling species-level taxa.

关键词： Classification (of information)

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A Bayesian framework for marker gene taxonomic classification based on sequence alignment 9

A Bayesian framework for marker gene taxonomic classificatio...

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9th International Conference on bioinformatics and Computational Biology, BICOB 2017

作者： Gao, Xiang Lin, Huaiying Revanna, Kashi Dong, Qunfeng Department of Public Health Sciences Loyola University Chicago Health Sciences Division MaywoodIL60153 United States Center for Biomedical Informatics Loyola University Chicago Health Sciences Division MaywoodIL60153 United States Bioinformatics Program Loyola University Chicago Lake Shore Campus ChicagoIL60660 United States Department of Computer Science Loyola University Chicago Water Tower Campus ChicagoIL60611 United States

ISBN: (纸本)9781943436071

Species-level classification for 16S rRNA gene sequences remains a serious challenge for microbiome researchers. The existing methods either lack solid probabilistic-based criteria to evaluate the confidence of their taxonomic assignments, typically ignoring other database sequences except the best hit, or use nucleotide kmer frequency as the proxy for sequence similarity measurement, a design driven by the pursuit of computational speed instead of classification accuracy. We have developed a method that shows significantly improved species-level classification results over existing methods. Our method calculates true sequence similarity between query sequences and database hits using pairwise sequence alignment. Taxonomic classifications are assigned from the species to the phylum levels based on the lowest common ancestors of multiple database hits for each query sequence, and further classification reliabilities are evaluated by bootstrap confidence scores. The novelty of our method is that the contribution of each database hit to the taxonomic assignment of the query sequence is weighted by a Bayesian posterior probability based upon the degree of sequence similarity of the database hit to the query sequence. Our software, called BLCA, is implemented in Python and freely available at https://***/qunfengdong/BLCA.

关键词： Classification (of information)

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An alignment-free method for profiling microbial community 8

An alignment-free method for profiling microbial community

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8th International Conference on bioinformatics and Computational Biology, BICOB 2016

作者： Pham, Diem-Trang Gao, Shanshan Phan, Vinhthuy Bioinformatics Program University of Memphis MemphisTN38111 United States Department of Computer Science University of Memphis MemphisTN38111 United States

ISBN: (纸本)9781943436033

Determining abundances of microbial genomes in metagenomic samples is an important problem in analyzing metagenomic data. Homology-based method requires aligning reads from metagenomic samples to reference genomes and can be a computationally expensive as microbial communities usually consists of hundreds to thousands of environmental microbial species. We introduce an efficient alignment-free approach to estimate abundances of microbial genomes in metagenomic samples. The approach is based on solving linear and quadratic programs, which are represented by genome-specific markers. In a controlled study at different levels of contamination, our method was able to achieve higher accuracy compared to current composition-based methods in estimating abundances of microbial genomes. Copyright ISCA.

关键词： Microorganisms

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Author Correction: Profiling immunoglobulin repertoires across multiple human tissues using RNA sequencing

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Nature communications 2020年第1期11卷 4499页

作者： Igor Mandric Jeremy Rotman Harry Taegyun Yang Nicolas Strauli Dennis J Montoya William Van Der Wey Jiem R Ronas Benjamin Statz Douglas Yao Velislava Petrova Alex Zelikovsky Roberto Spreafico Sagiv Shifman Noah Zaitlen Maura Rossetti K Mark Ansel Eleazar Eskin Serghei Mangul Department of Computer Science University of California Los Angeles 404 Westwood Plaza Los Angeles CA 90095 USA. Department of Clinical Pharmacy School of Pharmacy University of Southern California 1540 Alcazar Street Los Angeles CA 90033 USA. Bioinformatics Interdepartmental Ph.D. Program University of California Los Angeles 611 Charles E. Young Drive East Los Angeles CA 90095-1570 USA. Biomedical Sciences Graduate Program University of California San Francisco 1675 Owens Street Suite 310 San Francisco CA 94143-0523 USA. Department of Molecular Cell and Developmental Biology University of California Los Angeles 610 Charles E. Young Drive South Los Angeles CA 90095 USA. Department of Microbiology Immunology and Molecular Genetics University of California Los Angeles 609 Charles E. Young Drive East Los Angeles CA 90095 USA. Program in Bioinformatics and Integrative Genomics Harvard Medical School 10 Shattuck Street Suite 514 Boston MA 02115 USA. Wellcome Trust Sanger Institute Wellcome Genome Campus Hinxton Cambridge CB10 1SA UK. Department of Computer Science Georgia State University 33 Gilmer Street SE Atlanta GA 30303 USA. The Laboratory of Bioinformatics I.M. Sechenov First Moscow State Medical University Moscow 119991 Russia. Institute for Quantitative and Computational Biosciences University of California Los Angeles 611 Charles E. Young Drive East Los Angeles CA 90095 USA. Department of Genetics The Institute of Life Sciences The Hebrew University of Jerusalem Jerusalem 9190401 Israel. Department of Medicine University of California San Francisco 533 Parnassus Avenue San Francisco CA 94143 USA. Immunogenetics Center Department of Pathology and Laboratory Medicine David Geffen School of Medicine University of California Los Angeles 1000 Veteran Avenue Los Angeles CA 90095-1652 USA. Sandler Asthma Basic Research Center Department of Microbiology and Immunology University of California San Francisco 513 Parnass

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Cover Image, Volume 86, Issue 2

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Proteins: Structure, Function, and bioinformatics 2018年第2期86卷

作者： Guang Song Department of Computer Science Iowa State University Ames Iowa 50011 Program of Bioinformatics and Computational Biology Iowa State University Ames Iowa 50011 L. H. Baker Center for Bioinformatics and Biological Statistics Iowa State University Ames Iowa State University Ames Iowa 50011

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Author Correction: Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

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Nature communications 2020年第1期11卷 6232页

作者： Matthew H Bailey William U Meyerson Lewis Jonathan Dursi Liang-Bo Wang Guanlan Dong Wen-Wei Liang Amila Weerasinghe Shantao Li Yize Li Sean Kelso Gordon Saksena Kyle Ellrott Michael C Wendl David A Wheeler Gad Getz Jared T Simpson Mark B Gerstein Li Ding The McDonnell Genome Institute at Washington University St. Louis MO 63108 USA. Division of Oncology Department of Medicine Washington University School of Medicine St. Louis MO 63108 USA. Alvin J. Siteman Cancer Center Washington University School of Medicine St. Louis MO 63108 USA. Yale School of Medicine Yale University New Haven CT 06520 USA. Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 USA. Computational Biology Program Ontario Institute for Cancer Research Toronto ON M5G 0A3 Canada. The Hospital for Sick Children Toronto ON M5G 1X8 Canada. Broad Institute of MIT and Harvard Cambridge MA 02142 USA. Biomedical Engineering Oregon Health and Science University Portland OR 97239 USA. Department of Mathematics Washington University in St. Louis St. Louis MO 63130 USA. Department of Genetics Washington University School of Medicine St.Louis MO 63110 USA. Human Genome Sequencing Center Baylor College of Medicine Houston TX 77030 USA. Department of Molecular and Human Genetics Baylor College of Medicine Houston TX 77030 USA. Harvard Medical School Boston MA 02115 USA. Center for Cancer Research Massachusetts General Hospital Boston MA 02114 USA. Department of Pathology Massachusetts General Hospital Boston MA 02114 USA. Department of Computer Science University of Toronto Toronto ON M5S Canada. Program in Computational Biology and Bioinformatics Yale University New Haven CT 06520 USA. mark.gerstein@yale.edu. Department of Computer Science Yale University New Haven CT 06520 USA. mark.gerstein@yale.edu. Department of Molecular Biophysics and Biochemistry Yale University New Haven CT 06520 USA. mark.gerstein@yale.edu. The McDonnell Genome Institute at Washington University St. Louis MO 63108 USA. lding@wustl.edu. Division of Oncology Department of Medicine Washington University School of Medicine St. Louis MO 63108 USA. lding@wustl.edu. Alvin J. Siteman Cancer Center Wash

Correction to this paper has been published: https://***/10.1038/s41467-020-20128-w.

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Doubly Penalized LASSO for Reconstruction of Biological Networks

Doubly Penalized LASSO for Reconstruction of Biological Netw...

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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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Gene expression imputation across multiple brain regions provides insights into schizophrenia risk (vol 51, pg 659, 2019)

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NATURE GENETICS 2019年第6期51卷 1068-1068页

作者： Huckins, Laura M. Dobbyn, Amanda Ruderfer, Douglas M. Hoffman, Gabriel Wang, Weiqing Pardinas, Antonio F. Rajagopal, Veera M. Als, Thomas D. Nguyen, Hoang T. Girdhar, Kiran Boocock, James Roussos, Panos Fromer, Menachem Kramer, Robin Domenici, Enrico Gamazon, Eric R. Purcell, Shaun Demontis, Ditte Borglum, Anders D. Walters, James T. R. O'Donovan, Michael C. Sullivan, Patrick Owen, Michael J. Devlin, Bernie Sieberts, Solveig K. Cox, Nancy J. Im, Hae Kyung Sklar, Pamela Stahl, Eli A. Pamela Sklar Division of Psychiatric Genomics Icahn School of Medicine at Mount Sinai New York NY USA Department of Genetics and Genomics Icahn School of Medicine at Mount Sinai New York NY USA Department of Psychiatry Icahn School of Medicine at Mount Sinai New York NY USA Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai New York NY USA Vanderbilt University Medical Center Nashville TN USA MRC Centre for Neuropsychiatric Genetics and Genomics Cardiff University Cardiff UK Department of Biomedicine Aarhus University Aarhus Denmark The Lundbeck Foundation Initiative for Integrative Psychiatric Research iPSYCH Denmark Center for Integrative Sequencing Aarhus University Aarhus Denmark Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles CA USA Human Brain Collection Core National Institute of Mental Health Bethesda MD USA Laboratory of Neurogenomic Biomarkers Centre for Integrative Biology (CIBIO) University of Trento Trento Italy Clare Hall University of Cambridge Cambridge UK University of North Carolina at Chapel Hill Chapel Hill NC USA Karolinska Institutet Stockholm Sweden Department of Psychiatry University of Pittsburgh Pittsburgh PA USA Systems Biology Sage Bionetworks Seattle WA USA Section of Genetic Medicine Department of Medicine University of Chicago Chicago IL USA Integrated Technology Research Laboratories Pharmaceutical Research Division Takeda Pharmaceutical Company Limited Fujisawa Japan Neuropsychiatry Section Department of Psychiatry Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Neuropsychiatric Signaling Program Department of Psychiatry Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Psychiatry JJ Peters Virginia Medical Center Bronx NY USA Department of Neuroscience Icahn School of Medicine at Mount Sinai New York New York NY USA Friedman Brain Institute

Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression.

关键词： Gene expression Genome-wide association studies Schizophrenia Transcriptomics

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A second update on mapping the human genetic architecture of COVID-19

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Nature 2023年第7977期621卷 E7-E26页

作者： Masahiro Kanai Christine Stevens Benjamin M. Neale Mark Daly Andrea Ganna Amy Trankiem Mary K. Balaconis Samuli Ripatti Lindo Nkambul Mark J. Daly Sam Bryant Matthew Solomonson Shea J. Andrews Mattia Cordioli Juha Karjalainen Juha Mehtonen Matti Pirinen Andrea Corbetta Bradley Jermy Kati Donner Katja Kivinen Aarno Palotie Mari Kaunisto Kumar Veerapen Konrad J. Karczewski Alicia R. Martin Elizabeth G. Atkinson Kristin Tsuo Nikolas Baya Rahul Gupta Raymond K. Walters Duncan S. Palmer Gopal Sarma Nathan Cheng Wenhan Lu Claire Churchhouse Jacqueline I. Goldstein Daniel King Wei Zhou Cotton Seed Hilary Finucane F. Kyle Satterstrom Gita A. Pathak Akiko Iwasaki Karolina Chwialkowska Karolina Chwiałkowska Brooke N. Wolford Hajar Fauzan Ahmad Shea Andrews Kathrin Aprile von Hohenstaufen Puoti Palwende R. Boua Guillaume Butler-Laporte Celia M. T. Greenwood Bartha M. Knoppers Mark Lathrop Carmen L. Cadilla Francesca Colombo Nicole Dueker Chloe Kirk Atanu Kumar Dutta Yasser M. El-Sherbiny Jehan J. El-Jawhari Doaa Shahin Ahmed Elzeiny Tamer A. Mansour Madonna M. Eltoukhy Attia A. S. Mohamed Sahar Esmaeeli Annika Faucon Israel Fernández Cadenas Israel Fernandez-Cadenas Laia Llucià-Carol Natalia Cullell Elena Muiño Jara Cárcel-Márquez Margherita Francescatto Laurent Francioli Lindokuhle Nkambule Jatin Arora Soumya Raychaudhuri Caroline Cusick Lude Franke Macarena Fuentes Macarena Fuentes-Guajardo David Gómez Cabrero Emi N. Harry Philip Jansen József L. Szentpéteri Réka Várnai Elżbieta Kaja Athanasios Kousathanas Dorote Pasko Mark J. Caulfield Alex Stuckey Christopher A. Odhams Susan Walker Daniel Rhodes Richard H. Scott Loukas Moutsianas Afshan Siddiq Peter Goddard Sally Donovan Tala Zainy Fiona Maleady-Crowe Linda Todd Shahla Salehi Greg Elgar Georgia Chan Prabhu Arumugam Christine Patch Augusto Rendon Tom A. Fowler Jose E. Krieger Sanjay K. Patel Audrey Lemaçon Sophie Limou Pietro Lió Eirini Marouli Charles Hinds Minttu M. Marttila Carolina Medina-Gómez Isabelle Migeotte Soumyajit Mondal Andrés Moreno-Estrada Leire Moya Tomok Broad Institute of MIT and Harvard Cambridge MA USA Analytic and Translational Genetics Unit Massachusetts General Hospital Boston MA USA Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Program in Bioinformatics and Integrative Genomics Harvard Medical School Boston MA USA Institute for Molecular Medicine Finland (FIMM) University of Helsinki Helsinki Finland Institute for Molecular Medicine Finland University of Helsinki Helsinki Finland Institute for Molecular Medicine Finland (FIMM) HiLIFE University of Helsinki Helsinki Finland Public Health Faculty of Medicine University of Helsinki Helsinki Finland Analytic & Translational Genetics Unit Massachusetts General Hospital Boston MA USA Stanley Center for Psychiatric Research & Program in Medical and Population Genetics Cambridge UK Institute for Molecular Medicine Finland (FIMM) Univerisity of Helsinki Helsinki Finland University of California San Francisco San Francisco CA USA Department of Molecular and Human Genetics Baylor College of Medicine Houston TX USA Program in Biological and Biomedical Sciences Harvard Medical School Boston MA USA Department of Psychiatry School of Medicine Yale University New Haven CT USA Veteran Affairs Connecticut Healthcare System West Haven CT USA Yale University New Haven CT USA Centre for Bioinformatics and Data Analysis Medical University of Bialystok Bialystok Poland Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA Faculty of Industrial Sciences and Technology Universiti Malaysia Pahang Pahang Malaysia Icahn School of Medicine at Mount Sinai New York NY USA Genolier Innovation Network and Hub Swiss Medical Network Genolier Healthcare Campus Genolier Switzerland Immediate Independent Multidisciplinary Med & Biotech Research Association Milan Italy Clinical Research Unit of Nanor

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Corrigendum to "Dissecting the genetic heterogeneity of gastric cancer"

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EBioMedicine 2023年 94卷 104709页

作者： Timo Hess Carlo Maj Jan Gehlen Oleg Borisov Stephan L Haas Ines Gockel Michael Vieth Guillaume Piessen Hakan Alakus Yogesh Vashist Carina Pereira Michael Knapp Vitalia Schüller Alexander Quaas Heike I Grabsch Jessica Trautmann Ewa Malecka-Wojciesko Anna Mokrowiecka Jan Speller Andreas Mayr Julia Schröder Axel M Hillmer Dominik Heider Florian Lordick Ángeles Pérez-Aísa Rafael Campo Jesús Espinel Fernando Geijo Concha Thomson Luis Bujanda Federico Sopeña Ángel Lanas María Pellisé Claudia Pauligk Thorsten Oliver Goetze Carolin Zelck Julian Reingruber Emadeldin Hassanin Peter Elbe Sandra Alsabeah Mats Lindblad Magnus Nilsson Nicole Kreuser René Thieme Francesca Tavano Roberta Pastorino Dario Arzani Roberto Persiani Jin-On Jung Henrik Nienhüser Katja Ott Ralf R Schumann Oliver Kumpf Susen Burock Volker Arndt Anna Jakubowska Małgorzta Ławniczak Victor Moreno Vicente Martín Manolis Kogevinas Marina Pollán Justyna Dąbrowska Antonio Salas Olivier Cussenot Anne Boland-Auge Delphine Daian Jean-Francois Deleuze Erika Salvi Maris Teder-Laving Gianluca Tomasello Margherita Ratti Chiara Senti Valli De Re Agostino Steffan Arnulf H Hölscher Katharina Messerle Christiane Josephine Bruns Armands Sīviņš Inga Bogdanova Jurgita Skieceviciene Justina Arstikyte Markus Moehler Hauke Lang Peter P Grimminger Martin Kruschewski Nikolaos Vassos Claus Schildberg Philipp Lingohr Karsten Ridwelski Hans Lippert Nadine Fricker Peter Krawitz Per Hoffmann Markus M Nöthen Lothar Veits Jakob R Izbicki Adrianna Mostowska Federico Martinón-Torres Daniele Cusi Rolf Adolfsson Geraldine Cancel-Tassin Aksana Höblinger Ernst Rodermann Monika Ludwig Gisela Keller Andres Metspalu Hermann Brenner Joerg Heller Markus Neef Michael Schepke Franz Ludwig Dumoulin Lutz Hamann Renato Cannizzaro Michele Ghidini Dominik Plaßmann Michael Geppert Peter Malfertheiner Olivier Glehen Tomasz Skoczylas Marek Majewski Jan Lubiński Orazio Palmieri Stefania Boccia Anna Latiano Nuria Aragones Thomas Schmidt Mário Dinis-Ribeiro Rui Medeiros Salah-Eddin Al-Batran Mārcis Leja Juozas Institute of Human Genetics University of Marburg Marburg Germany Institute of Human Genetics University of Bonn School of Medicine & University Hospital Bonn Bonn Germany. Institute for Genomic Statistics and Bioinformatics Medical Faculty University of Bonn Bonn Germany. Institute of Human Genetics University of Marburg Marburg Germany. Department of Upper GI Diseases Karolinska University Hospital and Unit of Gastroenterology and Rheumatology Karolinska Institutet Stockholm Sweden. Department of Visceral Transplant Thoracic and Vascular Surgery University Hospital Leipzig Leipzig Germany. Institute for Pathology Friedrich-Alexander-University Erlangen-Nuernberg Klinikum Bayreuth Bayreuth Germany. Department of Digestive and Oncological Surgery Claude Huriez Hospital CHU Lille Lille France. Department of General Visceral Cancer and Transplant Surgery University of Cologne Cologne Germany. Department of General Visceral and Thoracic Surgery University Medical Center Hamburg-Eppendorf University of Hamburg Hamburg Germany Centre for Surgical Oncology Medias Klinikum Burghausen Burghausen Germany. Molecular Oncology and Viral Pathology Group IPO Porto Research Center (CI-IPOP) Portuguese Institute of Oncology of Porto (IPO Porto) 4200-072 Porto Portugal Porto Comprehensive Cancer Center & RISE @ CI-IPO University of Porto 4200-450 Porto Portugal. Institute of Medical Biometrics Informatics and Epidemiology (IMBIE) Medical Faculty University of Bonn Bonn Germany. Institute of Pathology University Hospital Cologne Medical Faculty University of Cologne Germany. Department of Pathology GROW School for Oncology and Developmental Biology Maastricht University Medical Center Maastricht the Netherlands Pathology and Data Analytics Leeds Institute of Medical Research at St James's University of Leeds Leeds United Kingdom. Department of Digestive Tract Diseases Medical University of Lodz Lodz Poland. Department of Mathematics and Computer Science Unive

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