المصدر: Genetics. 227(1)

مصطلحات موضوعية: Caenorhabditis elegans, Drosophila, data integration, database, knowledgebase, mouse, software, text mining, yeast, zebrafish, Animals, Databases, Genetic, Genomics, Software, Genome, Mice

الوصف: The Alliance of Genome Resources (Alliance) is an extensible coalition of knowledgebases focused on the genetics and genomics of intensively studied model organisms. The Alliance is organized as individual knowledge centers with strong connections to their research communities and a centralized software infrastructure, discussed here. Model organisms currently represented in the Alliance are budding yeast, Caenorhabditis elegans, Drosophila, zebrafish, frog, laboratory mouse, laboratory rat, and the Gene Ontology Consortium. The project is in a rapid development phase to harmonize knowledge, store it, analyze it, and present it to the community through a web portal, direct downloads, and application programming interfaces (APIs). Here, we focus on developments over the last 2 years. Specifically, we added and enhanced tools for browsing the genome (JBrowse), downloading sequences, mining complex data (AllianceMine), visualizing pathways, full-text searching of the literature (Textpresso), and sequence similarity searching (SequenceServer). We enhanced existing interactive data tables and added an interactive table of paralogs to complement our representation of orthology. To support individual model organism communities, we implemented species-specific landing pages and will add disease-specific portals soon; in addition, we support a common community forum implemented in Discourse software. We describe our progress toward a central persistent database to support curation, the data modeling that underpins harmonization, and progress toward a state-of-the-art literature curation system with integrated artificial intelligence and machine learning (AI/ML).

وصف الملف: application/pdf

الوصول الحر: https://escholarship.org/uc/item/1548r7x2Test

المؤلفون: Aleksander, Suzi A, Balhoff, James, Carbon, Seth, Cherry, J Michael, Drabkin, Harold J, Ebert, Dustin, Feuermann, Marc, Gaudet, Pascale, Harris, Nomi L, Hill, David P, Lee, Raymond, Mi, Huaiyu, Moxon, Sierra, Mungall, Christopher J, Muruganugan, Anushya, Mushayahama, Tremayne, Sternberg, Paul W, Thomas, Paul D, Van Auken, Kimberly, Ramsey, Jolene, Siegele, Deborah A, Chisholm, Rex L, Fey, Petra, Aspromonte, Maria Cristina, Nugnes, Maria Victoria, Quaglia, Federica, Tosatto, Silvio, Giglio, Michelle, Nadendla, Suvarna, Antonazzo, Giulia, Attrill, Helen, dos Santos, Gil, Marygold, Steven, Strelets, Victor, Tabone, Christopher J, Thurmond, Jim, Zhou, Pinglei, Ahmed, Saadullah H, Asanitthong, Praoparn, Buitrago, Diana Luna, Erdol, Meltem N, Gage, Matthew C, Kadhum, Mohamed Ali, Li, Kan Yan Chloe, Long, Miao, Michalak, Aleksandra, Pesala, Angeline, Pritazahra, Armalya, Saverimuttu, Shirin CC, Su, Renzhi, Thurlow, Kate E, Lovering, Ruth C, Logie, Colin, Oliferenko, Snezhana, Blake, Judith, Christie, Karen, Corbani, Lori, Dolan, Mary E, Ni, Li, Sitnikov, Dmitry, Smith, Cynthia, Cuzick, Alayne, Seager, James, Cooper, Laurel, Elser, Justin, Jaiswal, Pankaj, Gupta, Parul, Naithani, Sushma, Lera-Ramirez, Manuel, Rutherford, Kim, Wood, Valerie, De Pons, Jeffrey L, Dwinell, Melinda R, Hayman, G Thomas, Kaldunski, Mary L, Kwitek, Anne E, Laulederkind, Stanley JF, Tutaj, Marek A, Vedi, Mahima, Wang, Shur-Jen, D’Eustachio, Peter, Aimo, Lucila, Axelsen, Kristian, Bridge, Alan, Hyka-Nouspikel, Nevila, Morgat, Anne, Engel, Stacia R, Karra, Kalpana, Miyasato, Stuart R, Nash, Robert S, Skrzypek, Marek S, Weng, Shuai, Wong, Edith D, Bakker, Erika, Berardini, Tanya Z

المصدر: Genetics. 224(1)

مصطلحات موضوعية: Biological Sciences, Bioinformatics and Computational Biology, Genetics, 1.1 Normal biological development and functioning, Underpinning research, Gene Ontology, Proteins, Molecular Sequence Annotation, Databases, Genetic, Computational Biology, gene annotation, gene function, knowledgebase, knowledge graphs, Gene Ontology Consortium, Developmental Biology, Biochemistry and cell biology

الوصف: The Gene Ontology (GO) knowledgebase (http://geneontology.orgTest) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO-a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations-evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)-mechanistic models of molecular "pathways" (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project.

وصف الملف: application/pdf

الوصول الحر: https://escholarship.org/uc/item/8v86p60bTest

المؤلفون: Anthony Huffman, Xumeng Zhang, Meghana Lanka, Jie Zheng, Anna Maria Masci, Yongqun He

المصدر: Journal of Biomedical Semantics, Vol 15, Iss 1, Pp 1-11 (2024)

مصطلحات موضوعية: Parasite vaccine, VIOLIN vaccine knowledgebase, Vaccine Ontology, Computer applications to medicine. Medical informatics, R858-859.7

الوصف: Abstract Background Pathogenic parasites are responsible for multiple diseases, such as malaria and Chagas disease, in humans and livestock. Traditionally, pathogenic parasites have been largely an evasive topic for vaccine design, with most successful vaccines only emerging recently. To aid vaccine design, the VIOLIN vaccine knowledgebase has collected vaccines from all sources to serve as a comprehensive vaccine knowledgebase. VIOLIN utilizes the Vaccine Ontology (VO) to standardize the modeling of vaccine data. VO did not model complex life cycles as seen in parasites. With the inclusion of successful parasite vaccines, an update in parasite vaccine modeling was needed. Results VIOLIN was expanded to include 258 parasite vaccines against 23 protozoan species, and 607 new parasite vaccine-related terms were added to VO since 2022. The updated VO design for parasite vaccines accounts for parasite life stages and for transmission-blocking vaccines. A total of 356 terms from the Ontology of Parasite Lifecycle (OPL) were imported to VO to help represent the effect of different parasite life stages. A new VO class term, ‘transmission-blocking vaccine,’ was added to represent vaccines able to block infectious transmission, and one new VO object property, ‘blocks transmission of pathogen via vaccine,’ was added to link vaccine and pathogen in which the vaccine blocks the transmission of the pathogen. Additionally, our Gene Set Enrichment Analysis (GSEA) of 140 parasite antigens used in the parasitic vaccines identified enriched features. For example, significant patterns, such as signal, plasma membrane, and entry into host, were found in the antigens of the vaccines against two parasite species: Plasmodium falciparum and Toxoplasma gondii. The analysis found 18 out of the 140 parasite antigens involved with the malaria disease process. Moreover, a majority (15 out of 54) of P. falciparum parasite antigens are localized in the cell membrane. T. gondii antigens, in contrast, have a majority (19/24) of their proteins related to signaling pathways. The antigen-enriched patterns align with the life cycle stage patterns identified in our ontological parasite vaccine modeling. Conclusions The updated VO modeling and GSEA analysis capture the influence of the complex parasite life cycles and their associated antigens on vaccine development.

وصف الملف: electronic resource

العلاقة: https://doaj.org/toc/2041-1480Test

الوصول الحر: https://doaj.org/article/633973e9409e48f1bf7f01151c48a31fTest

المؤلفون: Wei Jiang, Peng-Ying Wang, Qi Zhou, Qiu-Tong Lin, Yao Yao, Xun Huang, Xiaoming Tan, Shihui Yang, Weicai Ye, Yuedong Yang, Yun-Juan Bao

المصدر: Journal of Translational Medicine, Vol 21, Iss 1, Pp 1-16 (2023)

مصطلحات موضوعية: Cancer precision medicine, Knowledgebase, Reporting system, Medicine

الوصف: Abstract Background With the development of cancer precision medicine, a huge amount of high-dimensional cancer information has rapidly accumulated regarding gene alterations, diseases, therapeutic interventions and various annotations. The information is highly fragmented across multiple different sources, making it highly challenging to effectively utilize and exchange the information. Therefore, it is essential to create a resource platform containing well-aggregated, carefully mined, and easily accessible data for effective knowledge sharing. Methods In this study, we have developed “Consensus Cancer Core” (Tri©DB), a new integrative cancer precision medicine knowledgebase and reporting system by mining and harmonizing multifaceted cancer data sources, and presenting them in a centralized platform with enhanced functionalities for accessibility, annotation and analysis. Results The knowledgebase provides the currently most comprehensive information on cancer precision medicine covering more than 40 annotation entities, many of which are novel and have never been explored previously. Tri©DB offers several unique features: (i) harmonizing the cancer-related information from more than 30 data sources into one integrative platform for easy access; (ii) utilizing a variety of data analysis and graphical tools for enhanced user interaction with the high-dimensional data; (iii) containing a newly developed reporting system for automated annotation and therapy matching for external patient genomic data. Benchmark test indicated that Tri©DB is able to annotate 46% more treatments than two officially recognized resources, oncoKB and MCG. Tri©DB was further shown to have achieved 94.9% concordance with administered treatments in a real clinical trial. Conclusions The novel features and rich functionalities of the new platform will facilitate full access to cancer precision medicine data in one single platform and accommodate the needs of a broad range of researchers not only in translational medicine, but also in basic biomedical research. We believe that it will help to promote knowledge sharing in cancer precision medicine. Tri©DB is freely available at www.biomeddb.org , and is hosted on a cutting-edge technology architecture supporting all major browsers and mobile handsets.

وصف الملف: electronic resource

العلاقة: https://doaj.org/toc/1479-5876Test

الوصول الحر: https://doaj.org/article/5f3513a9400242d7b26c3281c88dee41Test

المؤلفون: Jennifer H. Olker

مصطلحات موضوعية: Toxicology (incl. clinical toxicology), Bioavailability and ecotoxicology, ECOTOX knowledgebase, ecotoxicology, Systematic Review, PFAS

الوصف: Presented virtually at the Cross-Agency discussion on PFAS on 03/07/2024 Search for CCTE records in EPA’s Science Inventory by typing in the title at this link. https://cfpub.epa.gov/si/si_public_search_results.cfm?advSearch=true&showCriteria=2&keyword=CCTE&TIMSType=&TIMSSubTypeID=&epaNumber=&ombCat=Any&dateBeginPublishedPresented=07/01/2017&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&DEID=&personName=&personID=&role=Any&journalName=&journalID=&publisherName=&publisherID=&sortBy=pubDate&count=25Test

العلاقة: https://figshare.com/articles/presentation/Identifying_and_Curating_Ecological_Toxicity_Data_for_Per-_and_Polyfluoroalkyl_Substances_PFAS_using_the_ECOTOXicology_Knowledgebase_Protocols/25582977Test

الإتاحة: https://doi.org/10.23645/epacomptox.25582977.v1Test
https://figshare.com/articles/presentation/Identifying_and_Curating_Ecological_Toxicity_Data_for_Per-_and_Polyfluoroalkyl_Substances_PFAS_using_the_ECOTOXicology_Knowledgebase_Protocols/25582977Test

المؤلفون: Agapite, Julie, Albou, Laurent-Philippe, Aleksander, Suzanne A, Alexander, Micheal, Anagnostopoulos, Anna V, Antonazzo, Giulia, Argasinska, Joanna, Arnaboldi, Valerio, Attrill, Helen, Becerra, Andrés, Bello, Susan M, Blake, Judith A, Blodgett, Olin, Bradford, Yvonne M, Bult, Carol J, Cain, Scott, Calvi, Brian R, Carbon, Seth, Chan, Juancarlos, Chen, Wen J, Cherry, J Michael, Cho, Jaehyoung, Christie, Karen R, Crosby, Madeline A, Davis, Paul, da Veiga Beltrame, Eduardo, De Pons, Jeffrey L, D’Eustachio, Peter, Diamantakis, Stavros, Dolan, Mary E, dos Santos, Gilberto, Douglass, Eric, Dunn, Barbara, Eagle, Anne, Ebert, Dustin, Engel, Stacia R, Fashena, David, Foley, Saoirse, Frazer, Ken, Gao, Sibyl, Gibson, Adam C, Gondwe, Felix, Goodman, Josh, Gramates, L Sian, Grove, Christian A, Hale, Paul, Harris, Todd, Hayman, G Thomas, Hill, David P, Howe, Douglas G, Howe, Kevin L, Hu, Yanhui, Jha, Sagar, Kadin, James A, Kaufman, Thomas C, Kalita, Patrick, Karra, Kalpana, Kishore, Ranjana, Kwitek, Anne E, Laulederkind, Stanley JF, Lee, Raymond, Longden, Ian, Luypaert, Manuel, MacPherson, Kevin A, Martin, Ryan, Marygold, Steven J, Matthews, Beverley, McAndrews, Monica S, Millburn, Gillian, Miyasato, Stuart, Motenko, Howie, Moxon, Sierra, Muller, Hans-Michael, Mungall, Christopher J, Muruganujan, Anushya, Mushayahama, Tremayne, Nalabolu, Harika S, Nash, Robert S, Ng, Patrick, Nuin, Paulo, Paddock, Holly, Paulini, Michael, Perrimon, Norbert, Pich, Christian, Quinton-Tulloch, Mark, Raciti, Daniela, Ramachandran, Sridhar, Richardson, Joel E, Gelbart, Susan Russo, Ruzicka, Leyla, Schaper, Kevin, Schindelman, Gary, Shimoyama, Mary, Simison, Matt, Shaw, David R, Shrivatsav, Ajay, Singer, Amy, Skrzypek, Marek, Smith, Constance M, Smith, Cynthia L

المصدر: Genetics. 220(4)

مصطلحات موضوعية: Biological Sciences, Bioinformatics and Computational Biology, Genetics, Biotechnology, Human Genome, Underpinning research, 1.5 Resources and infrastructure (underpinning), Alleles, Animals, Caenorhabditis elegans, Databases, Genetic, Drosophila, Gene Ontology, Humans, Internet, Mice, Molecular Sequence Annotation, Rats, Saccharomycetales, Zebrafish, Alliance of Genome Resources Consortium, biocuration, data mining, gene expression, gene function, gene interaction, genome, knowledgebase, phenotype, variants, Developmental Biology, Biochemistry and cell biology

الوصف: The Alliance of Genome Resources (the Alliance) is a combined effort of 7 knowledgebase projects: Saccharomyces Genome Database, WormBase, FlyBase, Mouse Genome Database, the Zebrafish Information Network, Rat Genome Database, and the Gene Ontology Resource. The Alliance seeks to provide several benefits: better service to the various communities served by these projects; a harmonized view of data for all biomedical researchers, bioinformaticians, clinicians, and students; and a more sustainable infrastructure. The Alliance has harmonized cross-organism data to provide useful comparative views of gene function, gene expression, and human disease relevance. The basis of the comparative views is shared calls of orthology relationships and the use of common ontologies. The key types of data are alleles and variants, gene function based on gene ontology annotations, phenotypes, association to human disease, gene expression, protein-protein and genetic interactions, and participation in pathways. The information is presented on uniform gene pages that allow facile summarization of information about each gene in each of the 7 organisms covered (budding yeast, roundworm Caenorhabditis elegans, fruit fly, house mouse, zebrafish, brown rat, and human). The harmonized knowledge is freely available on the alliancegenome.org portal, as downloadable files, and by APIs. We expect other existing and emerging knowledge bases to join in the effort to provide the union of useful data and features that each knowledge base currently provides.

وصف الملف: application/pdf

الوصول الحر: https://escholarship.org/uc/item/8xx9b11gTest

المؤلفون: Wilmot, David

مصطلحات موضوعية: nlp, machine learning, story, narrative, salience, suprise, suspense, self-supervised, unsupervised, knowledgebase, memory, language model

الوصف: Stories interest us not because they are a sequence of mundane and predictable events but because they have drama and tension. Crucial to creating dramatic and exciting stories are surprise and suspense. Likewise, certain events are key to the plot and more important than others. Importance is referred to as salience. Inferring suspense, surprise and salience are highly challenging for computational systems. It is difficult because all these elements require a strong comprehension of the characters and their motivations, places, changes over time, and the cause/effect of complex interactions. Recently advances in machine learning (often called deep learning) have substantially improved in many language-related tasks, including story comprehension and story writing. Most of these systems rely on supervision; that is, huge numbers of people need to tag large quantities of data to tell the system what to teach these systems. An example would be tagging which events are suspenseful. It is highly inflexible and costly. Instead, the thesis trains a series of deep learning models via only reading stories, a self-supervised (or unsupervised) system. Narrative theory methods (rules and procedures) are applied to the knowledge built into the deep learning models to directly infer salience, surprise, and salience in stories. Extensions add memory and external knowledge from story plots and from Wikipedia to infer salience on novels such as Great Expectations and plays such as Macbeth. Other work adapts the models as a planning system for generating new stories. The thesis finds that applying the narrative theory to deep learning models can align with the typical reader. In follow up work, the insights could help improve computer models for tasks such as automatic story writing, assistance for writing, summarising or editing stories. Moreover, the approach of applying narrative theory to the inherent qualities built in a system that learns itself (self-supervised) from reading from books, watching videos, listening to audio is much cheaper and more adaptable to other domains and tasks. Progress is swift in improving self-supervised systems. As such, the thesis's relevance is that applying domain expertise with these systems may be a more productive approach in many areas of interest for applying machine learning.

الوصول الحر: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.857954Test

المؤلفون: Prasad, A.D.¹

المصدر: Water and Energy International 66r(4):30-41. 2023

المؤلفون: Thanh Loan, Dao Thi, Tuan, Tran Anh

المصدر: Bulletin of Electrical Engineering and Informatics; Vol 13, No 4: August 2024; 2774-2783 ; 2302-9285 ; 2089-3191 ; 10.11591/eei.v13i4

مصطلحات موضوعية: Aspect, Customer knowledgebase, Customer satisfaction, Sentiment summarization, Visual summary

الوصف: Due to the COVID-19 pandemic, the shopping behavior of customers has been significantly affected and is being shifted towards online shopping. Understanding the customers’ opinions, attitudes, and emotions in feedback and comments plays an essential role in making decisions for organizations and individuals (e.g., companies and customers). In this study, we propose sentiment summaries from the customer knowledgebase (SSoCK) framework that analyses customer feedback and improve a mechanism for sentiment summarization by using text analysis including sentiment analysis. In the experiments, various domains from customer reviews (e.g., computer and Canon) are used to conduct. The results show that the proposed SSoCK framework has the high performance of sentiment classification in terms of its accuracy when compared to the other approaches. Moreover, the proposed framework generates various kinds of sentiment summaries that can support managers/potential customers understand trending/interesting aspects of the product with customer satisfaction and can be easily updated with new reviews within the same domain without storing any original data.

وصف الملف: application/pdf

العلاقة: https://beei.org/index.php/EEI/article/view/7668/3821Test; https://beei.org/index.php/EEI/article/view/7668Test

الإتاحة: https://doi.org/10.11591/eei.v13i4.766810.11591/eei.v13i4Test
https://beei.org/index.php/EEI/article/view/7668Test

المؤلفون: Rutherford, Kim M, Lera-Ramírez, Manuel, Wood, Valerie

مصطلحات موضوعية: Schizosaccharomyces pombe, FAIR, Global BioData Coalition, Global Core Biodata Resource, MOD, biocuration, database, fission yeast, knowledgebase, model organism, model organism database, standardization, Schizosaccharomyces, Databases, Genetic, Genome, Fungal

الوصف: PomBase (https://www.pombase.orgTest), the model organism database (MOD) for fission yeast, was recently awarded Global Core Biodata Resource (GCBR) status by the Global Biodata Coalition (GBC; https://globalbiodata.orgTest/) after a rigorous selection process. In this MOD review, we present PomBase's continuing growth and improvement over the last 2 years. We describe these improvements in the context of the qualitative GCBR indicators related to scientific quality, comprehensivity, accelerating science, user stories, and collaborations with other biodata resources. This review also showcases the depth of existing connections both within the biocuration ecosystem and between PomBase and its user community. ...

الإتاحة: https://doi.org/10.17863/cam.104792Test
https://www.repository.cam.ac.uk/handle/1810/362861Test