The scientific knowledge about which genes are involved in which diseases grows rapidly, which makes it difficult to keep up with new publications and genetics datasets. The DISEASES database aims to provide a comprehensive overview by systematically integrating and assigning confidence scores to evidence for disease-gene associations from curated databases, genome-wide association studies (GWAS) and automatic text mining of the biomedical literature. Here, we present a major update to this resource, which greatly increases the number of associations from all these sources. This is especially true for the text-mined associations, which have increased by at least 9-fold at all confidence cutoffs. We show that this dramatic increase is primarily due to adding full-text articles to the text corpus, secondarily due to improvements to both the disease and gene dictionaries used for named entity recognition, and only to a very small extent due to the growth in number of PubMed abstracts. DISEASES now also makes use of a new GWAS database, Target Illumination by GWAS Analytics, which considerably increased the number of GWAS-derived disease-gene associations. DISEASES itself is also integrated into several other databases and resources, including GeneCards/MalaCards, Pharos/Target Central Resource Database and the Cytoscape stringApp. All data in DISEASES are updated on a weekly basis and is available via a web interface at https://diseases.jensenlab.org, from where it can also be downloaded under open licenses. Database URL: https://diseases.jensenlab.org.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9216524 | PMC |
| http://dx.doi.org/10.1093/database/baac019 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Therapeutic gene target prediction using novel deep hypergraph representation learning.
Brief Bioinform
November 2024
Division of Artificial Intelligence, Pusan National University, 2 Busandaehak-ro 63beon-gil, Geumjeong-gu, Busan 46241, South Korea.
Identifying therapeutic genes is crucial for developing treatments targeting genetic causes of diseases, but experimental trials are costly and time-consuming. Although many deep learning approaches aim to identify biomarker genes, predicting therapeutic target genes remains challenging due to the limited number of known targets. To address this, we propose HIT (Hypergraph Interaction Transformer), a deep hypergraph representation learning model that identifies a gene's therapeutic potential, biomarker status, or lack of association with diseases.
View Article and Find Full Text PDFRYR3 Variants Are Potentially Associated With Idiopathic (Non-Lesional) Partial Epilepsy/Susceptibility of Seizures, Toward Understanding the Gene-Disease Association by Genetic Dependent Nature.
Am J Med Genet B Neuropsychiatr Genet
January 2025
Department of Neurology, Institute of Neuroscience, Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, the Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, China.
The RYR3 gene encodes a brain-type ryanodine receptor that functions to release calcium from intracellular storage and plays an essential role in calcium signaling. The associations between RYR3 variants and brain disorders remain unknown. We performed whole-exome sequencing in patients with idiopathic (non-lesional) partial epilepsy of unknown etiology.
View Article and Find Full Text PDFA Universal Therapeutic Vaccine Leveraging Autologous Pre-Existing Immunity to Eliminate in Situ Uniformly Engineered Heterogeneous Tumor Cells.
Adv Mater
January 2025
Sichuan Provincial Key Laboratory for Human Disease Gene Study and the Center for Medical Genetics, Department of Laboratory Medicine, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610054, P. R. China.
Tumor vaccines that activate the autologous immune system to eliminate tumor cells represent a promising approach in cancer immunotherapy. However, challenges such as tumor heterogeneity, limited antigen selection, insufficient antigen presentation, and the slow onset of de novo immune responses have resulted in poor universality and suboptimal response rates. In contrast, pathogen-specific pre-existing immunity acquired through infection or vaccination, can rapidly generate a more potent and enduring immune response upon re-encounter with the same antigen.
View Article and Find Full Text PDFIntegrating pharmacogenomics and cheminformatics with diverse disease phenotypes for cell type-guided drug discovery.
Genome Med
January 2025
Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, 181 Longwood Avenue, Boston, MA, 02115, USA.
Background: Large-scale pharmacogenomic resources, such as the Connectivity Map (CMap), have greatly assisted computational drug discovery. However, despite their widespread use, CMap-based methods have thus far been agnostic to the biological activity of drugs as well as to the genomic effects of drugs in multiple disease contexts. Here, we present a network-based statistical approach, Pathopticon, that uses CMap to build cell type-specific gene-drug perturbation networks and integrates these networks with cheminformatic data and diverse disease phenotypes to prioritize drugs in a cell type-dependent manner.
View Article and Find Full Text PDFMetalloparticle-Engineered Pickering Emulsion Displaying AAV-Vectored Vaccine for Enhancing Antigen Expression and Immunogenicity Against Pathogens.
Adv Mater
January 2025
Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu, 610041, China.
Recombinant adeno-associated viruses (rAAVs) have emerged as promising vaccine vectors due to their enduring efficacy with a single dose. However, insufficient cellular immune responses and the random and non-specific distribution of AAVs post-injection may hinder the development of AAV vaccines. Here, a novel Pickering emulsion platform stabilized by biomineralized manganese nanoparticles and aluminum hydroxide, which can rapidly and efficiently load AAVs, is reported.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!