Chuming Chen

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10ranked-venue papers

4as first author

4since 2021 · last 2023

0000-0002-7287-9013ORCID · verified

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 9 · 3 first-author · 4 since 2021Artificial intelligence and machine learning · 1 · 1 first-authorDatabases, data management, data science and information retrieval · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing 7 papers		Bioinformatics and computational biology · 100%
Databases, data mining, and information retrieval 1 paper		Data integration and cleaning · 100%

Topics — the 7 heaviest of 9, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Bioinformatics and computational biology › genome annotation automatic annotation	0.9	2	2021	UniRule: a unified rule resource for automatic annotation in the UniProt Knowledgebase · Bioinform. 2021 UniRule: a unified rule resource for automatic annotation in the UniProt Knowledgebase · Bioinform. 2020
Bioinformatics and computational biology protein function prediction	0.9	2	2021	UniRule: a unified rule resource for automatic annotation in the UniProt Knowledgebase · Bioinform. 2021 UniRule: a unified rule resource for automatic annotation in the UniProt Knowledgebase · Bioinform. 2020
Bioinformatics and computational biology › protein analysis › protein bioinformatics protein annotation	0.7	1	2023	Annotation of biologically relevant ligands in UniProtKB using ChEBI · Bioinform. 2023
Bioinformatics and computational biology › knowledge representation in biology biomedical knowledge graph	0.5	1	2021	COVID-19 Knowledge Graph from semantic integration of biomedical literature and databases · Bioinform. 2021
Bioinformatics and computational biology proteomics	0.2	2	2013	A fast Peptide Match service for UniProt Knowledgebase · Bioinform. 2013 AGML Central: web based gel proteomic infrastructure · Bioinform. 2005
Bioinformatics and computational biology biomedical text mining	0.1	1	2021	COVID-19 Knowledge Graph from semantic integration of biomedical literature and databases · Bioinform. 2021
Bioinformatics and computational biology › protein sequence analysis protein sequence annotation	0.1	1	2016	Computational clustering for viral reference proteomes · Bioinform. 2016

Methods — techniques the papers use, named apart from their topics

interpro signatures · 0.9ontology-based annotation · 0.7SPARQL querying · 0.7SPARQL · 0.5RDF · 0.5sequence similarity clustering · 0.2BLASTP · 0.2text search engine · 0.2web services · 0.1markup language · 0.1

Year	Publication	Venue	Position
2023	Annotation of biologically relevant ligands in UniProtKB using ChEBI abstract MOTIVATION: To provide high quality, computationally tractable annotation of binding sites for biologically relevant (cognate) ligands in UniProtKB using the chemical ontology ChEBI (Chemical Entities of Biological Interest), to better support efforts to study and predict functionally relevant interactions between protein sequences and structures and small molecule ligands. RESULTS: We structured the data model for cognate ligand binding site annotations in UniProtKB and performed a complete reannotation of all cognate ligand binding sites using stable unique identifiers from ChEBI, which we now use as the reference vocabulary for all such annotations. We developed improved search and query facilities for cognate ligands in the UniProt website, REST API and SPARQL endpoint that leverage the chemical structure data, nomenclature and classification that ChEBI provides. AVAILABILITY AND IMPLEMENTATION: Binding site annotations for cognate ligands described using ChEBI are available for UniProtKB protein sequence records in several formats (text, XML and RDF) and are freely available to query and download through the UniProt website (www.uniprot.org), REST API (www.uniprot.org/help/api), SPARQL endpoint (sparql.uniprot.org/) and FTP site (https://ftp.uniprot.org/pub/databases/uniprot/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Elisabeth Coudert, Sebastien Gehant, Edouard De Castro, Monica Pozzato, Delphine Baratin, Teresa Batista Neto, Christian J. A. Sigrist, Nicole Redaschi, Alan J. Bridge, Lucila Aimo, Ghislaine Argoud-Puy, Andrea H. Auchincloss, Kristian B. Axelsen, Parit Bansal, Marie-Claude Blatter, Jerven T. Bolleman, Emmanuel Boutet, Lionel Breuza, Blanca Cabrera Gil, Cristina Casals-Casas, Kamal Chikh Echioukh, Béatrice A. Cuche, Anne Estreicher, Maria Livia Famiglietti, Marc Feuermann, Elisabeth Gasteiger, Pascale Gaudet, Vivienne Baillie Gerritsen, Arnaud Gos, Nadine Gruaz-Gumowski, Chantal Hulo, Nevila Hyka-Nouspikel, Florence Jungo, Arnaud Kerhornou, Philippe Le Mercier, Damien Lieberherr, Patrick Masson, Anne Morgat, Venkatesh Muthukrishnan, Salvo Paesano, Ivo Pedruzzi, Sandrine Pilbout, Lucille Pourcel, Sylvain Poux, Manuela Pruess, Catherine Rivoire, Karin Sonesson, Shyamala Sundaram, Alex Bateman, Maria Jesus Martin, Sandra E. Orchard, Michele Magrane, Shadab Ahmad, Emanuele Alpi, Emily H. Bowler-Barnett, Ramona Britto, Hema Bye-A-Jee, Austra Cukura, Paul Denny 0002, Tunca Dogan, Thankgod Ebenezer, Penelope Garmiri, Leonardo Jose da Costa Gonzales, Emma Hatton-Ellis, Abdulrahman Hussein, Alexandr Ignatchenko, Giuseppe Insana, Rizwan Ishtiaq, Vishal Joshi, Dushyanth Jyothi, Swaathi Kandasamy, Antonia Lock, Aurelien Luciani, Marija Lugaric, Yvonne Lussi, Alistair MacDougall, Fábio Madeira, Mahdi Mahmoudy, Alok Mishra 0004, Katie Moulang, Andrew Nightingale, Sangya Pundir, Guoying Qi, Shriya Raj, Pedro Raposo, Daniel Rice, Rabie Saidi, Elena Speretta, James D. Stephenson, Prabhat Totoo, Edward Turner, Nidhi Tyagi, Preethi Vasudev, Kate Warner, Xavier Watkins, Rossana Zaru, Hermann Zellner, Cathy H. Wu, Cecilia N. Arighi, Leslie Arminski, Chuming Chen, Yongxing Chen, Hongzhan Huang, Kati Laiho, Peter B. McGarvey, Darren A. Natale, Karen E. Ross, C. R. Vinayaka, Qinghua Wang 0003	Bioinform.	104
2021	Understanding the factors related to the opioid epidemic using machine learning abstract In recent years, the US has experienced an opioid epidemic with an unprecedented number of drugs overdose deaths. Research finds such overdose deaths are linked to neighborhood-level traits, thus providing opportunity to identify effective interventions. Typically, techniques such as Ordinary Least Squares (OLS) or Maximum Likelihood Estimation (MLE) are used to document neighborhood-level factors significant in explaining such adverse outcomes. These techniques are, however, less equipped to ascertain non-linear relationships between confounding factors. Hence, in this study we apply machine learning based techniques to identify opioid risks of neighborhoods in Delaware and explore the correlation of these factors using Shapley Additive explanations (SHAP). We discovered that the factors related to neighborhoods’ environment, followed by education and then crime, were highly correlated with higher opioid risk. We also explored the change in these correlations over the years to understand the changing dynamics of the epidemic. Furthermore, we discovered that, as the epidemic has shifted from legal (i.e., prescription opioids) to illegal (e.g., heroin and fentanyl) drugs in recent years, the correlation of environment, crime and health related variables with the opioid risk has increased significantly while the correlation of economic and socio-demographic variables has decreased. The correlation of education related factors has been higher from the start and has increased slightly in recent years suggesting a need for increased awareness about the opioid epidemic. Sachin Gavali, Chuming Chen, Julie Cowart, Xi Peng 0005, Cathy H. Wu, Tammy Anderson	BIBM	2
2021	COVID-19 Knowledge Graph from semantic integration of biomedical literature and databases abstract SUMMARY: The global response to the COVID-19 pandemic has led to a rapid increase of scientific literature on this deadly disease. Extracting knowledge from biomedical literature and integrating it with relevant information from curated biological databases is essential to gain insight into COVID-19 etiology, diagnosis and treatment. We used Semantic Web technology RDF to integrate COVID-19 knowledge mined from literature by iTextMine, PubTator and SemRep with relevant biological databases and formalized the knowledge in a standardized and computable COVID-19 Knowledge Graph (KG). We published the COVID-19 KG via a SPARQL endpoint to support federated queries on the Semantic Web and developed a knowledge portal with browsing and searching interfaces. We also developed a RESTful API to support programmatic access and provided RDF dumps for download. AVAILABILITY AND IMPLEMENTATION: The COVID-19 Knowledge Graph is publicly available under CC-BY 4.0 license at https://research.bioinformatics.udel.edu/covid19kg/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Chuming Chen, Karen E. Ross, Sachin Gavali, Julie Cowart, Cathy H. Wu	Bioinform.	1
2021	UniRule: a unified rule resource for automatic annotation in the UniProt Knowledgebase Alistair MacDougall, Vladimir Volynkin, Rabie Saidi, Diego Poggioli, Hermann Zellner, Emma Hatton-Ellis, Vishal Joshi, Claire O'Donovan, Sandra E. Orchard, Andrea H. Auchincloss, Delphine Baratin, Jerven T. Bolleman, Elisabeth Coudert, Edouard De Castro, Chantal Hulo, Patrick Masson, Ivo Pedruzzi, Catherine Rivoire, Cecilia N. Arighi, Qinghua Wang 0003, Chuming Chen, Hongzhan Huang, John S. Garavelli, C. R. Vinayaka, Lai-Su Yeh, Darren A. Natale, Kati Laiho, Maria Jesus Martin, Alexandre Renaux, Klemens Pichler	Bioinform.	21
2020	UniRule: a unified rule resource for automatic annotation in the UniProt Knowledgebase abstract MOTIVATION: The number of protein records in the UniProt Knowledgebase (UniProtKB: https://www.uniprot.org) continues to grow rapidly as a result of genome sequencing and the prediction of protein-coding genes. Providing functional annotation for these proteins presents a significant and continuing challenge. RESULTS: In response to this challenge, UniProt has developed a method of annotation, known as UniRule, based on expertly curated rules, which integrates related systems (RuleBase, HAMAP, PIRSR, PIRNR) developed by the members of the UniProt consortium. UniRule uses protein family signatures from InterPro, combined with taxonomic and other constraints, to select sets of reviewed proteins which have common functional properties supported by experimental evidence. This annotation is propagated to unreviewed records in UniProtKB that meet the same selection criteria, most of which do not have (and are never likely to have) experimentally verified functional annotation. Release 2020_01 of UniProtKB contains 6496 UniRule rules which provide annotation for 53 million proteins, accounting for 30% of the 178 million records in UniProtKB. UniRule provides scalable enrichment of annotation in UniProtKB. AVAILABILITY AND IMPLEMENTATION: UniRule rules are integrated into UniProtKB and can be viewed at https://www.uniprot.org/unirule/. UniRule rules and the code required to run the rules, are publicly available for researchers who wish to annotate their own sequences. The implementation used to run the rules is known as UniFIRE and is available at https://gitlab.ebi.ac.uk/uniprot-public/unifire. Alistair MacDougall, Vladimir Volynkin, Rabie Saidi, Diego Poggioli, Hermann Zellner, Emma Hatton-Ellis, Vishal Joshi, Claire O'Donovan, Sandra E. Orchard, Andrea H. Auchincloss, Delphine Baratin, Jerven T. Bolleman, Elisabeth Coudert, Edouard De Castro, Chantal Hulo, Patrick Masson, Ivo Pedruzzi, Catherine Rivoire, Cecilia N. Arighi, Qinghua Wang 0003, Chuming Chen, Hongzhan Huang, John S. Garavelli, C. R. Vinayaka, Lai-Su Yeh, Darren A. Natale, Kati Laiho, Maria Jesus Martin, Alexandre Renaux, Klemens Pichler	Bioinform.	21
2016	Computational clustering for viral reference proteomes abstract MOTIVATION: The enormous number of redundant sequenced genomes has hindered efforts to analyze and functionally annotate proteins. As the taxonomy of viruses is not uniformly defined, viral proteomes pose special challenges in this regard. Grouping viruses based on the similarity of their proteins at proteome scale can normalize against potential taxonomic nomenclature anomalies. RESULTS: We present Viral Reference Proteomes (Viral RPs), which are computed from complete virus proteomes within UniProtKB. Viral RPs based on 95, 75, 55, 35 and 15% co-membership in proteome similarity based clusters are provided. Comparison of our computational Viral RPs with UniProt's curator-selected Reference Proteomes indicates that the two sets are consistent and complementary. Furthermore, each Viral RP represents a cluster of virus proteomes that was consistent with virus or host taxonomy. We provide BLASTP search and FTP download of Viral RP protein sequences, and a browser to facilitate the visualization of Viral RPs. AVAILABILITY AND IMPLEMENTATION: http://proteininformationresource.org/rps/viruses/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Chuming Chen, Hongzhan Huang, Raja Mazumder, Darren A. Natale, Peter B. McGarvey, Jian Zhang 0051, Shawn W. Polson, Cathy H. Wu	Bioinform.	1
2013	An Integrated Approach for Interpretation of Clinical NGS Genomic Variant Data Erin L. Crowgey, Chuming Chen, Shawn W. Polson, Hongzhan Huang, Cathy H. Wu	AMIA	2
2013	A fast Peptide Match service for UniProt Knowledgebase abstract SUMMARY: We have developed a new web application for peptide matching using Apache Lucene-based search engine. The Peptide Match service is designed to quickly retrieve all occurrences of a given query peptide from UniProt Knowledgebase (UniProtKB) with isoforms. The matched proteins are shown in summary tables with rich annotations, including matched sequence region(s) and links to corresponding proteins in a number of proteomic/peptide spectral databases. The results are grouped by taxonomy and can be browsed by organism, taxonomic group or taxonomy tree. The service supports queries where isobaric leucine and isoleucine are treated equivalent, and an option for searching UniRef100 representative sequences, as well as dynamic queries to major proteomic databases. In addition to the web interface, we also provide RESTful web services. The underlying data are updated every 4 weeks in accordance with the UniProt releases. AVAILABILITY: http://proteininformationresource.org/peptide.shtml. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Chuming Chen, Hongzhan Huang, Baris E. Suzek, Cathy H. Wu	Bioinform.	1
2007	Extending Description Logic for Reasoning about Ontology Evolution abstract Ontologies play a key role in achieving global automatic information integration and sharing on the Semantic Web. They allow intelligent applications to exchange information through a shared and formal conceptualization of an application domain. Understanding ontology evolution can help both ontology developers and users evaluating the potential consequences of ontology changes and act accordingly. Our contribution is proposing a temporal paradigm for ontology evolution and extending Description Logic with Temporal Logic operators to formally characterize and reason about ontology evolution. We investigate related reasoning problems and algorithm. Chuming Chen, Manton M. Matthews	Web Intelligence	1
2005	AGML Central: web based gel proteomic infrastructure abstract SUMMARY: AGML Central is a web-based open-source public infrastructure for dissemination of two-dimensional Gel Electrophoresis (2-DE) proteomics data in AGML format (Annotated Gel Markup Language). It includes a growing collection of converters from proprietary formats such as those produced by PDQUEST (BioRad), PHORETIX 2-D (Nonlinear Dynamics) and Melanie (GenBio SA). The resulting unifying AGML formatted entry, with or without the raw gel images, is optionally stored in a database for future reference. AGML Central was developed to provide a common platform for data dissemination and development of 2-DE data analysis tools. This resource responds to an increasing use of AGML for 2-DE public source data representation which requires automated tools for conversion from proprietary formats. Conversion and short-term storage is made publicly available, permanent storage requires prior registering. A JAVA applet visualizer was developed to visualize the AGML data with cross-reference links. In order to facilitate automated access a SOAP web service is also included in the AGML Central infrastructure. AVAILABILITY: http://bioinformatics.musc.edu/agmlcentral. Romesh Stanislaus, Chuming Chen, Jennifer Franklin, John M. Arthur, Jonas S. Almeida	Bioinform.	2