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Hybrid feature selection to identify important single nucleotide polymorphism for asthma classification

AIP Conference Proceedings 2024年第1期2991卷

作者： Wan Ni Wong Weng Howe Chan Hui Wen Nies Kauthar Mohd Daud Artificial Intelligence and Bioinformatics Group School of Computing Faculty of Engineering Universiti Teknologi Malaysia 81310 Skudai Johor Malaysia UTM Big Data Centre Ibnu Sina Institute for Scientific and Industrial Research Universiti Teknologi Malaysia 81310 Skudai Johor Malaysia Center for Artificial Intelligence Technology Faculty of Information Science & Technology Universiti Kebangsaan Malaysia 43600 UKM Bangi Selangor Malaysia

Single nucleotide polymorphism (SNP), being the most common genetic variation, is found to be responsible for traits and complex diseases such as asthma. Polygenic risk scoring (PRS) and machine learning (ML) are the current two common types of genetic risk prediction models. PRS employs statistical association testing to identify alleles with risk. However, it was found not performing well in unraveling the SNP-SNP interactions and attaining low predictive value. In contrast, ML algorithms are more robust in detecting disease associated SNPs, resulting in an improvement in prediction performance. The challenge remains as the high dimensionality nature of SNP tends to cause overfitting problems and impact the classifier performance. This paper introduces a two-stage hybrid feature selection that integrates Mutual Information and Random Forest Recursive Feature Elimination for identifying informative SNPs from 176,287 SNPs in a dataset of 128 asthmatic and non-asthmatic samples. The reduced samples based on the identified informative SNPs are then used with Support Vector Machine to train a classification model. The model from this paper outperforms other ML models with an average accuracy, precision, recall and area under the curve of 98.91%, 98.36%, 99.72% and 0.99 respectively. Additional analysis shows that the genes associated with the top selected SNPs, namely Ring Finger Protein 217 (RNF217) and A-kinase-anchoring protein 6 (AKAP6) are correlated with respiratory diseases. This shows that the proposed method can identify important SNPs that are able to differentiate between asthmatic and non-asthmatic samples.

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Transit Functions and Pyramid-Like Binary Clustering Systems

arXiv

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arXiv 2022年

作者： Changat, Manoj Shanavas, Ameera Vaheeda Stadler, Peter F. Department of Futures Studies University of Kerala IN Trivandrum695 581 India Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics Universität Leipzig Härtelstraße 16-18 LeipzigD-04107 Germany Halle-Jena-Leipzig Competence Center for Scalable Data Services and Solutions Dresden-Leipzig Leipzig Research Center for Civilization Diseases Centre for Biotechnology and Biomedicine Leipzig University Universität Leipzig Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Institute for Theoretical Chemistry University of Vienna Währingerstrasse 17 WienA-1090 Austria Facultad de Ciencias Universidad National de Colombia Bogotá Colombia Santa Fe Insitute 1399 Hyde Park Rd. Santa FeNM87501 United States

Binary clustering systems are closely related to monotone transit functions. An interesting class are pyramidal transit functions defined by the fact that their transit sets form an interval hypergraph. We investigate here properties of transit function R, such as union-closure, that are sufficient to ensure that R is at least weakly pyramidal. Necessary conditions for pyramidal transit functions are derived from the five forbidden configurations in Tucker's characterization of interval hypergraphs. The first corresponds to β-acyclicity, also known as total balancedness, for which we obtain three alternative characterizations. For monotonous transit functions, the last forbidden configuration becomes redundant, leaving us with characterization of pyramidal transit functions in terms of four additional conditions. © 2022, CC BY-SA.

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Glioma subtype classification from histopathological images using in-domain and out-of-domain transfer learning: An experimental study

arXiv

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arXiv 2023年

作者： Despotovic, Vladimir Kim, Sang-Yoon Hau, Ann-Christin Kakoichankava, Aliaksandra Klamminger, Gilbert Georg Borgmann, Felix Bruno Kleine Frauenknecht, Katrin B.M. Mittelbronn, Michel Nazarov, Petr V. Bioinformatics Platform Data Integration and Analysis Unit Luxembourg Institute of Health Strassen Luxembourg Dr. Senckenberg Institute of Neurooncology University Hospital Frankfurt Frankfurt am Main Germany Edinger Institute Institute of Neurology Goethe University Frankfurt am Main Germany Frankfurt Cancer Institute Goethe University Frankfurt am Main Germany University Cancer Center Frankfurt Frankfurt am Main Germany University Hospital Goethe University Frankfurt am Main Germany Laboratoire national de santé National Center of Pathology Dudelange Luxembourg Multi-Omics Data Science group Department of Cancer Research Luxembourg Institute of Health Strassen Luxembourg Luxembourg Centre of Neuropathology Dudelange Luxembourg Klinik für Frauenheilkunde Geburtshilfe und Reproduktionsmedizin Saarland University Homburg Germany Department of Cancer Research Luxembourg Institute of Health Strassen Luxembourg Luxembourg Centre for Systems Biomedicine University of Luxembourg Belval Luxembourg Department of Life Sciences and Medicine University of Luxembourg Esch-sur-Alzette Luxembourg Faculty of Science Technology and Medicine University of Luxembourg Esch-sur-Alzette Luxembourg

We provide in this paper a comprehensive comparison of various transfer learning strategies and deep learning architectures for computer-aided classification of adult-type diffuse gliomas. We evaluate the generalizability of out-of-domain ImageNet representations for a target domain of histopathological images, and study the impact of in-domain adaptation using self-supervised and multi-task learning approaches for pretraining the models using the medium-to-large scale datasets of histopathological images. A semi-supervised learning approach is furthermore proposed, where the fine-tuned models are utilized to predict the labels of unannotated regions of the whole slide images (WSI). The models are subsequently retrained using the ground-truth labels and weak labels determined in the previous step, providing superior performance in comparison to standard in-domain transfer learning with balanced accuracy of 96.91% and F1-score 97.07%, and minimizing the pathologist’s efforts for annotation. Finally, we provide a visualization tool working at WSI level which generates heatmaps that highlight tumor areas;thus, providing insights to pathologists concerning the most informative parts of the WSI. © 2023, CC BY-NC-ND.

关键词： Tumors

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What makes a reaction network "chemical"?

arXiv

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arXiv 2022年

作者： Müller, Stefan Flamm, Christoph Stadler, Peter F. Faculty of Mathematics University of Vienna Oskar-Morgenstern-Platz 1 WienA-1090 Austria Department of Theoretical Chemistry University of Vienna Währinger Straße 17 WienA-1090 Austria Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics Universität Leipzig Härtelstraße 16-18 LeipzigD-04107 Germany Halle-Jena-Leipzig & Competence Center for Scalable Data Services and Solutions Dresden-Leipzig Leipzig Research Center for Civilization Diseases University Leipzig LeipzigD-04107 Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Faculdad de Ciencias Universidad Nacional de Colombia Sede Bogotá Ciudad Universitaria DC BogotáCOL-111321 Colombia Santa Fe Institute 1399 Hyde Park Road Santa FeNM87501 United States

Background: Reaction networks (RNs) comprise a set X of species and a set R of reactions Y → Y′, each converting a multiset of educts Y ⊆ X into a multiset Y′ ⊆ X of products. RNs are equivalent to directed hypergraphs. However, not all RNs necessarily admit a chemical interpretation. Instead, they might contradict fundamental principles of physics such as the conservation of energy and mass or the reversibility of chemical reactions. The consequences of these necessary conditions for the stoichiometric matrix S ∈ X×R have been discussed extensively in the chemical literature. Here, we provide sufficient conditions for S that guarantee the interpretation of RNs in terms of balanced sum formulas and structural formulas, respectively. Results: Chemically plausible RNs allow neither a perpetuum mobile, i.e., a "futile cycle" of reactions with non-vanishing energy production, nor the creation or annihilation of mass. Such RNs are said to be thermodynamically sound and conservative. For finite RNs, both conditions can be expressed equivalently as properties of the stoichiometric matrix S. The first condition is vacuous for reversible networks, but it excludes irreversible futile cycles and – in a stricter sense – futile cycles that even contain an irreversible reaction. The second condition is equivalent to the existence of a strictly positive reaction invariant. Furthermore, it is sufficient for the existence of a realization in terms of sum formulas, obeying conservation of "atoms". In particular, these realizations can be chosen such that any two species have distinct sum formulas, unless S implies that they are "obligatory isomers". In terms of structural formulas, every compound is a labeled multigraph, in essence a Lewis formula, and reactions comprise only a rearrangement of bonds such that the total bond order is preserved. In particular, for every conservative RN, there exists a Lewis realization, in which any two compounds are realized by pairwisely distinct mu

关键词： Energy conservation

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Translation dysregulation in cancer as a source for targetable antigens

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Cancer Cell 2025年第5期43卷 823-840.e18,20页

作者： Weller, Chen Bartok, Osnat McGinnis, Christopher S. Palashati, Heyilimu Chang, Tian-Gen Malko, Dmitry Shmueli, Merav D. Nagao, Asuteka Hayoun, Deborah Murayama, Ayaka Sakaguchi, Yuriko Poulis, Panagiotis Khatib, Aseel Erlanger Avigdor, Bracha Gordon, Sagi Cohen Shvefel, Sapir Zemanek, Marie J. Nielsen, Morten M. Boura-Halfon, Sigalit Sagie, Shira Gumpert, Nofar Yang, Weiwen Alexeev, Dmitry Kyriakidou, Pelgia Yao, Winnie Zerbib, Mirie Greenberg, Polina Benedek, Gil Litchfield, Kevin Petrovich-Kopitman, Ekaterina Nagler, Adi Oren, Roni Ben-Dor, Shifra Levin, Yishai Pilpel, Yitzhak Rodnina, Marina Cox, Jürgen Merbl, Yifat Satpathy, Ansuman T. Carmi, Yaron Erhard, Florian Suzuki, Tsutomu Buskirk, Allen R. Olweus, Johanna Ruppin, Eytan Schlosser, Andreas Samuels, Yardena Department of Molecular Cell Biology Weizmann Institute of Science Rehovot 7610001 Israel Department of Pathology Stanford University Stanford 94305 CA United States Parker Institute for Cancer Immunotherapy San Francisco 94129 CA United States Department of Cancer Immunology Institute for Cancer Research Oslo University Hospital Oslo 0379 Norway Precision Immunotherapy Alliance University of Oslo Oslo Norway Cancer Data Science Laboratory Center for Cancer Research National Cancer Institute National Institutes of Health Bethesda 20892 MD United States Department of Systems Immunology Weizmann Institute of Science Rehovot 7610001 Israel Department of Chemistry and Biotechnology of Engineering The University of Tokyo Tokyo 113-8656 Japan Department of Physical Biochemistry Max Planck Institute for Multidisciplinary Sciences Göttingen 37077 Germany Department of Pathology Sackler School of Medicine Tel Aviv University Tel Aviv 69978 Israel Department of Immunology and Regenerative Biology Weizmann Institute of Science Rehovot 7610001 Israel Computational Systems Biochemistry Research Group Max-Planck Institute of Biochemistry Martinsried 82152 Germany Department of Veterinary Resources Weizmann Institute of Science Rehovot 7610001 Israel Tissue Typing and Immunogenetics Unit Hadassah Hebrew University Hospital Jerusalem 9112102 Israel CRUK Lung Cancer Centre of Excellence University College London Cancer Institute London WC1E 6DD United Kingdom Tumour Immunogenomics and Immunosurveillance Laboratory University College London Cancer Institute London WC1E 6DD United Kingdom Department of Life Science Core Facilities Weizmann Institute of Science Rehovot 7610001 Israel Bioinformatics Unit Department of Life Sciences Core Facilities Weizmann Institute of Science Rehovot 7610001 Israel de Botton Institute for Protein Profiling the Nancy and Stephen Grand Israel National Center for Personalized Medicine Weizmann Institute of Science Rehovot

Aberrant peptides presented by major histocompatibility complex (MHC) molecules are targets for tumor eradication, as these peptides can be recognized as foreign by T cells. Protein synthesis in malignant cells is dysregulated, which may result in the generation and presentation of aberrant peptides that can be exploited for T cell-based therapies. To investigate the role of translational dysregulation in immunological tumor control, we disrupt translation fidelity by deleting tRNA wybutosine (yW)-synthesizing protein 2 (TYW2) in tumor cells and characterize the downstream impact on translation fidelity and immunogenicity using immunopeptidomics, genomics, and functional assays. These analyses reveal that TYW2 knockout (KO) cells generate immunogenic out-of-frame peptides. Furthermore, Tyw2 loss increases tumor immunogenicity and leads to anti-programmed cell death 1 (PD-1) checkpoint blockade sensitivity in vivo. Importantly, reduced TYW2 expression is associated with increased response to checkpoint blockade in patients. Together, we demonstrate that defects in translation fidelity drive tumor immunogenicity and may be leveraged for cancer immunotherapy. © 2025 The Author(s)

关键词： cancer immunopeptidome immune checkpoint blockade neoantigens non-canonical peptides translation fidelity

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Corrigendum to“Antibiotic Treatment Drives the Diversification of the Human Gut Resistome”[Genomics Proteomics bioinformatics 17(1)(2019)39-51]

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Genomics, Proteomics & bioinformatics 2020年第5期18卷 624-625页

作者： Jun Li Elizabeth A.Rettedal Eric van der Helm Mostafa Ellabaan Gianni Panagiotou Morten O.A.Sommer Department of Infectious Diseases and Public Health Colleague of Veterinary Medicine and Life SciencesCity University of Hong KongHong Kong Special Administrative RegionChina School of Data Science City University of Hong KongHong Kong Special Administrative RegionChina Novo Nordisk Foundation Center for Biosustainability DK-2900 HørsholmDenmark Systems Biology and Bioinformatics Unit Leibniz Institute for Natural Product Research and Infection Biology-Hans Knöll Institute07745 JenaGermany Systems Biology and Bioinformatics Group School of Biological SciencesFaculty of SciencesThe University of Hong KongHong Kong Special Administrative RegionChina Department of Microbiology Li Ka Shing Faculty of MedicineThe University of Hong KongHong Kong Special Administrative RegionChina

The editors regret there was an error in“Shotgun metagenome library construction and sequencing”section.“The raw sequences can be found in BGID(CRA000815)”should be corrected to“The raw metagenome sequencing data have been deposited in the Genome Sequence Archive at Beijing Institute of Genomics,Chinese Academy of sciences(GSA:CRA000815),and are publicly accessible at https://***/gsa/”.The correct section is shown *** editors would like to apologize for any inconvenience caused.

关键词： (1) Antibiotic Genome

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Orientation of Fitch graphs and detection of horizontal gene transfer in gene trees

arXiv

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arXiv 2021年

作者： Schaller, David Hellmuth, Marc Stadler, Peter F. Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics Leipzig University Härtelstraße 16-18 LeipzigD-04107 Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Department of Mathematics Faculty of Science Stockholm University StockholmSE - 106 91 Sweden Halle-Jena-Leipzig Competence Center for Scalable Data Services and Solutions Leipzig Research Center for Civilization Diseases Leipzig University Germany Inst. f. Theoretical Chemistry University of Vienna Währingerstraße 17 WienA-1090 Austria Facultad de Ciencias Universidad National de Colombia Sede Bogotá Colombia Santa Fe Institute 1399 Hyde Park Rd. Santa FeNM87501 United States

Horizontal gene transfer events partition a gene tree T and thus, its leaf set into subsets of genes whose evolutionary history is described by speciation and duplication events alone. Indirect phylogenetic methods can be used to infer such partitions p from sequence similarity or evolutionary distances without any a priory knowledge about the underlying tree T. In this contribution, we assume that such a partition p of a set of genes X is given and that, independently, an estimate T of the original gene tree on X has been derived. We then ask to what extent T and the xenology information, i.e., p can be combined to determine the horizontal transfer edges in T. We show that for each pair of genes x and y with x,y being in different parts of p, it can be decided whether there always exists or never exists a horizontal gene transfer in T along the path connecting y and the most recent common ancestor of x and y. This problem is equivalent to determining the presence or absence of the directed edge (x,y) in so-called Fitch graphs;a more fine-grained version of graphs that represent the dependencies between the sets in p. We then consider the generalization to insufficiently resolved gene trees and show that analogous results can be obtained. We show that the classification of (x,y) can be computed in constant time after linear-time preprocessing. Using simulated gene family histories, we observe empirically that the vast majority of horizontal transfer edges in the gene tree T can be recovered unambiguously. © 2021, CC BY-NC-SA.

关键词： Clustering algorithms

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Quasi-Best Match Graphs

arXiv

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arXiv 2021年

作者： Korchmaros, Annachiara Schaller, David Hellmuth, Marc Stadler, Peter F. Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics Universität Leipzig Härtelstraße 16-18 LeipzigD-04107 Germany Department of Mathematics Faculty of Science Stockholm University StockholmSE-10691 Sweden Halle-Jena-Leipzig Competence Center for Scalable Data Services and Solutions Dresden-Leipzig Leipzig Research Center for Civilization Diseases Centre for Biotechnology and Biomedicine Leipzig University Universität Leipzig Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Institute for Theoretical Chemistry University of Vienna Währingerstrasse 17 WienA-1090 Austria Facultad de Ciencias Universidad Nacional de Colombia Bogotá Colombia Santa Fe Institute 1399 Hyde Park Rd. Santa FeNM87501 United States

Quasi-best match graphs (qBMGs) are a hereditary class of directed, properly vertex-colored graphs. They arise naturally in mathematical phylogenetics as a generalization of best match graphs, which formalize the notion of evolutionary closest relatedness of genes (vertices) in multiple species (vertex colors). They are explained by rooted trees whose leaves correspond to vertices. In contrast to BMGs, qBMGs represent only best matches at a restricted phylogenetic distance. We provide characterizations of qBMGs that give rise to polynomial-time recognition algorithms and identify the BMGs as the qBMGs that are color-sink-free. Furthermore, two-colored qBMGs are characterized as directed graphs satisfying three simple local conditions, two of which have appeared previously, namely bi-transitivity in the sense of Das et al. (2021) and a hierarchy-like structure of out-neighborhoods, i.e., N(x) ∩ N(y) ∈ {N(x), N(y), ∅} for any two vertices x and y. Further results characterize qBMGs that can be explained by binary phylogenetic trees. Copyright © 2021, The Authors. All rights reserved.

关键词： Phylogenetic

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Landscape of global urban environmental resistome and its association with local socioeconomic and medical status

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science China(Life sciences) 2024年第6期67卷 1292-1301页

作者： Jun Wu Yige Hu Michael H Perlin David Danko Jun Lu Manuela Oliveira Johannes Werner Maria M Zambrano Maria A Sierra Olayinka O Osuolale Paweł Łabaj Nicolás Rascovan Nur Hazlin Hazrin-Chong Soojin Jang Haruo Suzuki Marina Nieto-Caballero Bharath Prithiviraj Patrick K H Lee Agnieszka Chmielarczyk Anna Różańska Yongxiang Zhao Lan Wang Christopher E Mason Tieliu Shi Center for Bioinformatics and Computational Biology and the Institute of Biomedical SciencesSchool of Life SciencesEast China Normal UniversityShanghai 200241China Department of Biology Program on Disease EvolutionUniversity of LouisvilleLouisville 40292USA Weill Cornell Medicine New York 10065USA The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine New York 10065USA Department of Pulmonary Medicine Shanghai Chest HospitalShanghai Jiao Tong UniversityShanghai 200025China i3S-Instituto de Investiga玢o e Inova玢o em Saúde Universidade do PortoPorto 4050-290Portugal Ipatimup-Instituto de Patologia e Imunologia Molecular da Universidade do Porto Porto 4200-465Portugal Departamento de Biologia Faculdade de CiênciasUniversidade do PortoPorto 4050-290Portugal High Performance and Cloud Computing Group Zentrum für Datenverarbeitung(ZDV)Eberhard Karls University of TübingenW鋍hterstra遝7672074TübingenGermany Corporación Corpogen Research Center Bogotá110311Colombia Applied Environmental Metagenomics and Infectious Diseases Research(AEMIDR) Department of Biological SciencesElizade UniversityIlara-Mokin 340271Nigeria Maopolska Centre of Biotechnology Jagiellonian UniversityKraków 30-005Poland Aix-Marseille Université Mediterranean Institute of OceanologyUniversitéde ToulonCNRSIRDUM 110Marseille 83041France Department of Biological Sciences and Biotechnology Faculty of Science and TechnologyUniversiti Kebangsaan Malaysia43600 UKM BangiSelangor Institut Pasteur Korea Seoul 13488Republic of Korea Faculty of Environment and Information Studies Keio UniversityFujisawaKanagawa 252-0882Japan University of Colorado at Boulder CivilEnvironmental and Architectural DepartmentBoulder 80303USA Brooklyn College of the City University of New York Brooklyn 11210USA School of Energy and Environment City University of Hong KongHong Kong 999077China Department of Microbiology Faculty of MedicineJagiellonian UniversityKraków 30-005Poland Biological Targeting Diagnosis and The

Antimicrobial resistance(AMR)poses a critical threat to global health and development,with environmental factors—particularly in urban areas—contributing significantly to the spread of antibiotic resistance genes(ARGs).However,most research to date has been conducted at a local level,leaving significant gaps in our understanding of the global status of antibiotic resistance in urban *** address this issue,we thoroughly analyzed a total of 86,213 ARGs detected within 4,728 metagenome samples,which were collected by the Meta SUB International Consortium involving diverse urban environments in 60 cities of 27 countries,utilizing a deep-learning based *** findings demonstrated the strong geographical specificity of urban environmental resistome,and their correlation with various local socioeconomic and medical *** also identified distinctive evolutionary patterns of ARG-related biosynthetic gene clusters(BGCs)across different countries,and discovered that the urban environment represents a rich source of novel *** study provides a comprehensive overview of the global urban environmental resistome,and fills a significant gap in our knowledge of large-scale urban antibiotic resistome analysis.

关键词： urban environment metagenome antibiotic resistance resistance diversity biosynthetic gene clusters

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19P Tumor-associated microbiome composition and response to neoadjuvant chemotherapy (NACT) in early triple-negative breast cancer (TNBC)

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ESMO Open 2023年第1期8卷 101243-101243页

作者： A. Papakonstantinou S. Napoli L. Joval M. Butjosa-Espin A. Mas Malavila M. Borrell Puy I. Pimentel E. Zamora P. Gonzalez Torres N. Carron Rodas C. Saura Manich J.A. Seoane L. Nonell P.G. Nuciforo M. Oliveira Breast Cancer Group Vall d'Hebron Institute of Oncology (VHIO) Barcelona Spain and Division of Breast Cancer Endocrine Tumors and Sarcoma Karolinska University Hospital Stockholm Sweden Molecular Oncology Group Vall d'Hebron Institute of Oncology (VHIO) Barcelona Spain Oncology Data Science Group (ODysSey) Vall d'Hebrón Institute of Oncology (VHIO) Barcelona Spain Cancer Computational Biology Group Vall d’Hebron Institute of Oncology (VHIO) Barcelona Spain Bioinformatics Unit Vall d’Hebron Institute of Oncology (VHIO) Barcelona Spain Breast Cancer Group Vall d'Hebron Institute of Oncology (VHIO)-Cellex Center Barcelona Spain Medical Oncology Dept. Vall d'Hebron University Hospital (VHIO) Barcelona Spain Microomics Microomics Systems S.L. Barcelona Spain

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