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SWEET: a single-sample network inference method for deciphering individual features in disease

Briefings in bioinformatics 2023年第2期24卷 bbad032页

作者： Hsin-Hua Chen Chun-Wei Hsueh Chia-Hwa Lee Ting-Yi Hao Tzu-Ying Tu Lan-Yun Chang Jih-Chin Lee Chun-Yu Lin Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu 300 Taiwan. School of Medical Laboratory Science and Biotechnology College of Medical Science and Technology Taipei Medical University Taipei 110 Taiwan. TMU Research Center of Cancer Translational Medicine Taipei Medical University Taipei 110 Taiwan. Ph.D. Program in Medical Biotechnology College of Medical Science and Technology Taipei Medical University Taipei Taiwan. Department of Otolaryngology-Head and Neck Surgery Tri-Service General Hospital National Defense Medical Center Taipei 110 Taiwan. Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu 300 Taiwan. Institute of Data Science and Engineering National Yang Ming Chiao Tung University Hsinchu 300 Taiwan. Center for Intelligent Drug Systems and Smart Bio-devices National Yang Ming Chiao Tung University Hsinchu 300 Taiwan. School of Dentistry Kaohsiung Medical University Kaohsiung 807 Taiwan.

Recently, extracting inherent biological system information (e.g. cellular networks) from genome-wide expression profiles for developing personalized diagnostic and therapeutic strategies has become increasingly important. However, accurately constructing single-sample networks (SINs) to capture individual characteristics and heterogeneity in disease remains challenging. Here, we propose a sample-specific-weighted correlation network (SWEET) method to model SINs by integrating the genome-wide sample-to-sample correlation (i.e. sample weights) with the differential network between perturbed and aggregate networks. For a group of samples, the genome-wide sample weights can be assessed without prior knowledge of intrinsic subpopulations to address the network edge number bias caused by sample size differences. Compared with the state-of-the-art SIN inference methods, the SWEET SINs in 16 cancers more likely fit the scale-free property, display higher overlap with the human interactomes and perform better in identifying three types of cancer-related genes. Moreover, integrating SWEET SINs with a network proximity measure facilitates characterizing individual features and therapy in diseases, such as somatic mutation, mut-driver and essential genes. Biological experiments further validated two candidate repurposable drugs, albendazole for head and neck squamous cell carcinoma (HNSCC) and lung adenocarcinoma (LUAD) and encorafenib for HNSCC. By applying SWEET, we also identified two possible LUAD subtypes that exhibit distinct clinical features and molecular mechanisms. Overall, the SWEET method complements current SIN inference and analysis methods and presents a view of biological systems at the network level to offer numerous clues for further investigation and clinical translation in network medicine and precision medicine.

关键词： drug repurposing gene expression network inference network medicine precision medicine single-sample network

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Complete edge-colored permutation graphs

arXiv

引用

arXiv 2020年

作者： Hartmann, Tom Bannach, Max Middendorf, Martin Stadler, Peter F. Wieseke, Nicolas Hellmuth, Marc Bioinformatics Group Department of Computer Science & Interdisciplinary Center for Bioinformatics Universität Leipzig Härtelstraße 16–18 LeipzigD-04107 Germany Institute for Theoretical Computer Science Universität zu Lübeck Ratzeburger Allee 160 LübeckD-23562 Germany Swarm Intelligence and Complex Systems Group Faculty of Mathematics and Computer Science University of Leipzig Augustusplatz 10 LeipzigD-04109 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 at 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 Sede Bogotá Colombia Santa Fe Insitute 1399 Hyde Park Rd. Santa FeNM87501 United States School of Computing University of Leeds EC Stoner Building LeedsLS2 9JT United Kingdom

We introduce the concept of complete edge-colored permutation graphs as complete graphs that are the edge-disjoint union of "classical" permutation graphs. We show that a graph G = (V, E) is a complete edge-colored permutation graph if and only if each monochromatic subgraph of G is a "classical" permutation graph and G does not contain a triangle with 3 different colors. Using the modular decomposition as a framework we demonstrate that complete edge-colored permutation graphs are characterized in terms of their strong prime modules, which induce also complete edge-colored permutation graphs. This leads to an O(|V |2)-time recognition algorithm. We show, moreover, that complete edge-colored permutation graphs form a superclass of so-called symbolic ultrametrics and that the coloring of such graphs is always a Gallai coloring. Copyright © 2020, The Authors. All rights reserved.

关键词： Graphic methods

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Indirect identification of horizontal gene transfer

arXiv

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arXiv 2020年

作者： Schaller, David Lafond, Manuel Stadler, Peter F. Wieseke, Nicolas Hellmuth, Marc Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics Universität Leipzig Härtelstraße 16–18 LeipzigD-04107 Germany Department of Computer Science Université de Sherbrooke 2500 boul. de l’Université SherbrookeQCJ1K 2R1 Canada Halle-Jena-Leipzig Competence Center for Scalable Data Services and Solutions Dresden-Leipzig Leipzig Research Center for Civilization Diseases Centre for Biotechnology and Biomedicine at Leipzig University Universität Leipzig Institute for Theoretical Chemistry University of Vienna Währingerstrasse 17 WienA-1090 Austria Facultad de Ciencias Universidad National de Colombia Sede Bogotá Colombia Santa Fe Insitute 1399 Hyde Park Rd. Santa FeNM87501 United States Swarm Intelligence and Complex Systems Group Department of Computer Science Leipzig University Augustusplatz 10 LeipzigD-04109 Germany Department of Mathematics Faculty of Science Stockholm University StockholmSE - 106 91 Sweden

Several implicit methods to infer Horizontal Gene Transfer (HGT) focus on pairs of genes that have diverged only after the divergence of the two species in which the genes reside. This situation defines the edge set of a graph, the later-divergence-time (LDT) graph, whose vertices correspond to genes colored by their species. We investigate these graphs in the setting of relaxed scenarios, i.e., evolutionary scenarios that encompass all commonly used variants of duplication-transfer-loss scenarios in the literature. We characterize LDT graphs as a subclass of properly vertex-colored cographs, and provide a polynomial-time recognition algorithm as well as an algorithm to construct a relaxed scenario that explains a given LDT. An edge in an LDT graph implies that the two corresponding genes are separated by at least one HGT event. The converse is not true, however. We show that the complete xenology relation is described by an rs-Fitch graph, i.e., a complete multipartite graph satisfying constraints on the vertex coloring. This class of vertex-colored graphs is also recognizable in polynomial time. We finally address the question "how much information about all HGT events is contained in LDT graphs" with the help of simulations of evolutionary scenarios with a wide range of duplication, loss, and HGT events. In particular, we show that a simple greedy graph editing scheme can be used to efficiently detect HGT events that are implicitly contained in LDT graphs. © 2020, CC BY.

关键词： Phylogenetic

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Generalized Space Time Autoregressive of Chili Prices 3

Generalized Space Time Autoregressive of Chili Prices

引用

3rd International Conference on Information Management and Technology, ICIMTech 2018

作者： Jamilatuzzahro Caraka, Rezzy Eko Herliansyah, Riki Asmawati, S. Sari, Dian Megah Pardamean, Bens Bandung Institute of Technology Actuarial Science Bandung Indonesia Bina Nusantara University Department of Statistics Bioinformatics and Data Science Research Center Bandung Jakarta11480 Indonesia Department of Mathematics Kalimantan Institute of Technology Kalimantan Timur Indonesia Informatics Faculty of Engineering West Sulawesi University Sulawesi Barat Indonesia

ISBN: (纸本)9781538658215

Indonesian people are mostly like spicy. Therefore, Chili becomes an ingredient in cooking that cannot be separated from Indonesian people. In certain months there is too much demand from the public and inversely proportional to the stock of chili. So, appreciate the chili prices to rise. In the heart of statistics, several methods can be used as forecasting as well as data series from univariate, bivariate and multivariate cases. The reason for choosing the method is following the analysis needs and available data. One of the popular methods in the multivariate case is Generalized Spacetime Autoregressive (GSTAR) the advantage of this method is that it can capture the characteristics of location-based data as well as time. For this reason, in this paper, we used chili prices data in 4 major cities in Java Province, Indonesia. Such as Jakarta, Bandung, Semarang, and Yogyakarta. As a result, GSTAR (21) I (1with inverse weighted be the best models because it fulfilled white noise and normal multivariate assumption with the lowest RMSE and MAPE. © 2018 IEEE.

关键词： Costs

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Proteomics Standards Initiative's ProForma 2.0: Unifying the Encoding of Proteoforms and Peptidoforms

arXiv

引用

arXiv 2021年

作者： LeDuc, Richard D. Deutsch, Eric W. Binz, Pierre-Alain Fellers, Ryan T. Cesnik, Anthony J. Klein, Joshua A. Van Den Bossche, Tim Gabriels, Ralf Yalavarthi, Arshika Perez-Riverol, Yasset Carver, Jeremy Bittremieux, Wout Kawano, Shin Pullman, Benjamin Bandeira, Nuno Kelleher, Neil L. Thomas, Paul M. Vizcaíno, Juan Antonio National Resource for Translational and Developmental Proteomics Northwestern University EvanstonIL60611 United States Institute for Systems Biology SeattleWA98109 United States Clinical Chemistry Service Lausanne University Hospital Lausanne1011 Switzerland Department of Genetics Stanford University StanfordCA94305 United States Chan Zuckerberg Biohub 499 Illinois St San FranciscoCA94158 United States SciLifeLab School of Engineering Sciences in Chemistry Biotechnology and Health KTH-Royal Institute of Technology Stockholm 113 51 SolnaSE-171 21 Sweden Program for Bioinformatics Boston University BostonMA02215 United States VIB-UGent Center for Medical Biotechnology VIB Technologiepark 75-FSVM II Ghent9052 Belgium Department of Biomolecular Medicine Faculty of Medicine and Health Sciences Ghent University Ghent9000 Belgium Cambridge HinxtonCB10 1SD United Kingdom La Jolla CA92093 United States La Jolla CA92093 United States La Jolla CA92093 United States Toyama University of International Studies Toyama Higashikuromaki 6 5-1 Toyama930-1292 Japan Database Center for Life Science Joint Support-Center for Data Science Research Research Organization of Information and Systems Chiba Kashiwa277-0871 Japan

It is important for the proteomics community to have a standardized manner to represent all possible variations of a protein or peptide primary sequence, including natural, chemicallyinduced and artifactualmodifications. The Human Proteome Organization (HUPO) Proteomics Standards Initiative (PSI) in collaboration with several members of the Consortium for Top-Down Proteomics (CTDP) has developed a standard notation called ProForma 2.0, which is a substantial extension of the original ProForma notation developed by the CTDP. ProForma 2.0 aims to unify the representation of proteoforms and peptidoforms. ProForma 2.0 supports use cases needed for bottom-up and middle-/top-down proteomics approaches and allows the encoding of highly modified proteins and peptides using a humanand machine-readable string. ProForma 2.0 can be used to represent protein modifications in a specified or ambiguous location, designated by mass shifts, chemical formulas, or controlled vocabulary terms, including cross-links (natural and chemical), and atomic isotopes. Notational conventions are based on public controlled vocabularies and ontologies. The most up-to-date full specification document and information about software implementations are available at http://***/proforma. © 2021, CC0.

关键词： Mass spectrometry

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Analyzing important factors in e-commerce for sustainable industry

引用

IOP Conference Series: Earth and Environmental Science 2021年第1期794卷

作者： Yulius Lie Kartika Purwandari Bharuno Mahesworo Reza Rahutomo Anzaludin Samsinga Perbangsa Hery Harjono Muljo Bens Pardamean Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Accounting Information Systems Program Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480

The rapid growth of e-commerce for sustainable industry in Indonesia leads to competition for customer acquisition. Instead of gaining loyalty, customers are trapped in a price war and take advantage to compare and choose the one with the best offer. This study aims to analyze important factors of an online retailer to determine customer loyalty in the productive age group. This study contributes to design a questionnaire that can be utilized in analyzing customer loyalty to a retailer in e-commerce (popularly known as the term e-tailer). The questionnaire can be distributed through the Internet to gather demography, population, and e-loyalty factors. Questionnaire design is proposed containing three parts: Part A – Demography, Part B – Targeted Population, and Part C - e-Loyalty Factors. While Part A records basic information and Part B validates, Part C records respondent's assessment to an e - tailer. Therefore, the outcome delivers a data engineering method to help e-tailers determine important factors to gain customer loyalty.

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Cross-platform transcriptomic profiling of the response to recombinant human erythropoietin

Research Square

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Research Square 2021年

作者： Wang, Guan Kitaoka, Traci Crawford, Ali Mao, Qian Hesketh, Andrew Guppy, Fergus M. Ash, Garrett I. Liu, Jason Gerstein, Mark B. Pitsiladis, Yannis P. Sport and Exercise Science and Sports Medicine Research and Enterprise Group University of Brighton Brighton United Kingdom Centre for Regenerative Medicine and Devices University of Brighton Brighton United Kingdom Illumina San DiegoCA United States BGI Shenzhen China School of Pharmacy and Biomolecular Sciences University of Brighton Brighton United Kingdom Centre for Stress and Age-related Disease University of Brighton Brighton United Kingdom Veterans Affairs Connecticut Healthcare System West HavenCT United States Center for Medical Informatics Yale University New HavenCT United States Program in Computational Biology & Bioinformatics Yale University New HavenCT United States Department of Molecular Biophysics & Biochemistry Yale University New HavenCT United States Department of Computer Science Yale University New HavenCT United States Department of Statistics and Data Science Yale University New HavenCT United States

RNA-seq has matured and become an important tool for studying RNA biology. Here we compared two RNA-seq (Illumina sequencing by synthesis and MGI DNBSEQTM) and two microarray platforms (Illumina Expression BeadChip and GeneChipTM Human Transcriptome Array 2.0) in healthy individuals administered recombinant human erythropoietin for transcriptome-wide quantification of differential gene expression. The results show that total RNA sequencing combined with DNB-seq produced a multitude of genes of biological relevance and significance in response to recombinant human erythropoietin, in contrast to other platforms. Through data triangulation linking genes to functions, genes representing the processes of erythropoiesis as well as non-erythropoietic functions of erythropoietin were unveiled. This study provides a knowledge base of genes characterising the responses to recombinant human erythropoietin through cross-platform comparison and validation. © 2021, CC BY.

关键词： RNA

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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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Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

arXiv

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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

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578P Increased therapeutic index of muzastotug (ADG126), a masked anti-CTLA-4 antibody, in combination with pembrolizumab (pembro) enables significant clinical benefits and supports further clinical development in patients with metastatic MSS CRC

引用

Annals of Oncology 2024年 35卷 S468-S469页

作者： D. Li S.Y. Kim H.K. Kim S. Sharma S.J. Shin J. Lee S-A. Im X.K. She Y. Li L.E.C. Chung P. Xiao G. Liu S. Zeng D. HuLowe M. Chisamore P.P. Luo J. Zha M.R. Patel Medical Oncology Department City of Hope Comprehensive Cancer Center Duarte CA USA Oncology Department Asan Medical Center - University of Ulsan Seoul Songpa-gu Republic of Korea Medical Oncology Department Chungbuk National University Hospital College of Medicine Cheongju Chungcheongbuk-do Republic of Korea Huntsman Cancer Institute University of Utah Health - Huntsman Cancer Institute Salt Lake City UT USA Medical Oncology Department Yonsei University College of Medicine Seoul Republic of Korea Division of Hem/Oncology Samsung Medical Center Sungkyunkwan University Seoul Republic of Korea Internal Medicine Dept Cancer Research Institute Seoul National University Hospital Seoul National University College of Medicine Seoul Republic of Korea Clinical Development Adagene Inc. San Diego CA USA Data and Bioinformatics Adagene Inc. San Diego CA USA Clinical Development Adagene (Suzhou) Limited Suzhou China Pharmacology Adagene Inc. San Diego CA USA Clinical Pharmacology Adagene Inc San Diego CA USA Oncology Early Clinical Development Merck & Co. Inc. - Corporate Headquarters Whitehouse Station NJ USA Adagene Inc. San Diego CA USA Drug Development Florida Cancer Specialists/Sarah Cannon Research Institute Fort Myers FL USA

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