The DisGeNET cytoscape app: Exploring and visualizing disease genomics data

Computational and Structural Biotechnology Journal, Год журнала: 2021, Номер 19, С. 2960 - 2967

Опубликована: Янв. 1, 2021

Thanks to the unbiased exploration of genomic variants at large scale, hundreds thousands disease-associated loci have been uncovered. In parallel, network-based approaches proven be essential understand molecular mechanisms underlying human diseases. The use these has boosted by abundance information about disease associated genes and variants, high quality interactomics data, emergence new types omics data. DisGeNET Cytoscape App combines capabilities with those DisGeNET, a knowledge platform based on comprehensive catalogue variants. contains functions query, analyze, visualize different network representations gene-disease variant-disease associations available in DisGeNET. It supports wide variety applications through its query filter functionalities, including annotation foreign networks generated other apps or uploaded user. release designed support 3.x incorporates novel distinctive features such as visualization analysis networks, enrichment for analytic Automation. Moreover, an API access core functionalities via REST protocol fostering development reproducible scalable workflows

Язык: Английский

---

Enrichr-KG: bridging enrichment analysis across multiple libraries

Nucleic Acids Research, Год журнала: 2023, Номер 51(W1), С. W168 - W179

Опубликована: Май 11, 2023

Gene and protein set enrichment analysis is a critical step in the of data collected from omics experiments. Enrichr popular gene web-server search engine that contains hundreds thousands annotated sets. While has been useful providing with many libraries different categories, integrating results across domains knowledge can further hypothesis generation. To this end, Enrichr-KG graph database application combines selected for integrative visualization. The are presented as subgraphs made nodes links connect genes to their enriched terms. In addition, users add gene-gene links, well predicted subgraphs. This graphical representation cross-library illuminate hidden associations between terms datasets resources. currently serves 26 categories include transcription, pathways, ontologies, diseases/drugs, cell types. demonstrate utility we provide several case studies. freely available at: https://maayanlab.cloud/enrichr-kg.

Язык: Английский

---

Artificial Intelligence in Pharmaceutical Sciences

Engineering, Год журнала: 2023, Номер 27, С. 37 - 69

Опубликована: Апрель 28, 2023

Drug discovery and development affects various aspects of human health dramatically impacts the pharmaceutical market. However, investments in a new drug often go unrewarded due to long complex process research (R&D). With advancement experimental technology computer hardware, artificial intelligence (AI) has recently emerged as leading tool analyzing abundant high-dimensional data. Explosive growth size biomedical data provides advantages applying AI all stages R&D. Driven by big biomedicine, led revolution R&D, its ability discover drugs more efficiently at lower cost. This review begins with brief overview common models field discovery; then, it summarizes discusses depth their specific applications such target discovery, design, preclinical research, automated synthesis, influences Finally, major limitations R&D are fully discussed possible solutions proposed.

Язык: Английский

---

Node-adaptive graph Transformer with structural encoding for accurate and robust lncRNA-disease association prediction

BMC Genomics, Год журнала: 2024, Номер 25(1)

Опубликована: Янв. 18, 2024

Abstract Background Long noncoding RNAs (lncRNAs) are integral to a plethora of critical cellular biological processes, including the regulation gene expression, cell differentiation, and development tumors cancers. Predicting relationships between lncRNAs diseases can contribute better understanding pathogenic mechanisms disease provide strong support for advanced treatment methods. Results Therefore, we present an innovative Node-Adaptive Graph Transformer model predicting unknown LncRNA-Disease Associations, named NAGTLDA. First, utilize node-adaptive feature smoothing (NAFS) method learn local information nodes encode structural fusion similarity network using Structural Deep Network Embedding (SDNE). Next, module is used capture potential association nodes. Finally, employ with two multi-headed attention layers learning global-level embedding fusion. structure coding added as inductive bias compensate missing message-passing mechanism in Transformer. NAGTLDA achieved average AUC 0.9531 AUPR 0.9537 significantly higher than state-of-the-art methods 5-fold cross validation. We perform case studies on 4 diseases; 55 out 60 associations have been validated literatures. The results demonstrate enormous graph incorporate uncovering lncRNA-disease correlations. Conclusions Our proposed serve highly efficient computational associations.

Язык: Английский

---

Gut Microbiome-Derived Metabolites and Their Impact on Gene Regulatory Networks in Gestational Diabetes

The Journal of Steroid Biochemistry and Molecular Biology, Год журнала: 2025, Номер unknown, С. 106674 - 106674

Опубликована: Янв. 1, 2025

Язык: Английский

---

Network Pharmacology and Molecular Docking to Elucidate the Potential Mechanism of Ligusticum Chuanxiong Against Osteoarthritis

Frontiers in Pharmacology, Год журнала: 2022, Номер 13

Опубликована: Апрель 14, 2022

Background: Osteoarthritis (OA) is a degenerative disease which serious affects patients. Ligusticum chuanxiong (CX) has been shown to have certain curative effect on osteoarthritis in traditional Chinese medicine therapy. This study based network pharmacology and molecular docking technology explore the potential mechanism of CX. Methods: Components CX treat were screened TCMSP database targets predicted by PharmMapper database, collected from GeneCards intersection genes found be possible anti-OA. The STRING Cytoscape software utilized for protein-protein interaction analysis further screening core targets. Metascape was used KEGG GO enrichment analyses. Then, top 10 pathways selected construct "drug-compound-target-pathway-disease" analysis. Finally, analyze binding affinity seven compounds with TNF-α. Results: Seven 253 non-repetitive 60 anti-OA found. PPI showed that ALB, AKT1, IGF1, CASP3, MAPK1, ANXA5, MAPK14, while pathway analyses relevant biological processes involved treatment might include MAPK cascade reactive oxygen species metabolic process. result mainly associated signaling PI3K-AKT pathway. We docked ingredients MAPK1 MAPK14 enriched pathway, TNF-α as typical inflammatory cytokine. results also good affinity, especially FA, may most important component Conclusion: Our research revealed OA, our findings can pave way subsequent basic experimental verification new direction.

Язык: Английский

---

SperoPredictor: An Integrated Machine Learning and Molecular Docking-Based Drug Repurposing Framework With Use Case of COVID-19

Frontiers in Public Health, Год журнала: 2022, Номер 10

Опубликована: Июнь 16, 2022

The global spread of the SARS coronavirus 2 (SARS-CoV-2), its manifestation in human hosts as a contagious disease, and variants have induced pandemic resulting deaths over 6,000,000 people. Extensive efforts been devoted to drug research cure refrain COVID-19, but only one has received FDA approval yet. Traditional discovery is inefficient, costly, unable react threats. Drug repurposing represents an effective strategy for reduces time cost compared de novo discovery. In this study, generic framework (SperoPredictor) developed which systematically integrates various types drugs disease data takes advantage machine learning (Random Forest, Tree Ensemble, Gradient Boosted Trees) repurpose potential candidates against any interest. FDA-approved (n = 2,865), containing four features three features, were collected from chemical biological databases integrated with form drug-disease association tables. dataset was split into 70% training, 15% testing, remaining validation. testing validation accuracies models 99.3% Random Forest 99.03% Ensemble. practice, SperoPredictor identified 25 6 host-target proteomes systematic review journals. Literature-based indicated 12 predicted (48%) already used COVID-19 followed by molecular docking re-docking 4 13 (30%) be pre-clinically clinically validated. Finally, results illustrated ability platform rapidly deployed rapid response emergent situations (like other pandemics).

Язык: Английский

---

Saffron essential oil ameliorates CUMS-induced depression-like behavior in mice via the MAPK-CREB1-BDNF signaling pathway

Journal of Ethnopharmacology, Год журнала: 2022, Номер 300, С. 115719 - 115719

Опубликована: Сен. 17, 2022

Язык: Английский

---

How can natural language processing help model informed drug development?: a review

JAMIA Open, Год журнала: 2022, Номер 5(2)

Опубликована: Апрель 6, 2022

Abstract Objective To summarize applications of natural language processing (NLP) in model informed drug development (MIDD) and identify potential areas improvement. Materials Methods Publications found on PubMed Google Scholar, websites GitHub repositories for NLP libraries models. describing MIDD were reviewed. The stratified into 3 stages: discovery, clinical trials, pharmacovigilance. Key functionalities used these assessed. Programming open-source resources the implementation identified. Results has been utilized to aid various processes lifecycle such as gene-disease mapping, biomarker patient-trial matching, adverse events detection, etc. These commonly use named entity recognition, word embeddings, resolution, assertion status relation extraction, topic modeling. current state-of-the-art implementing are transformer models that utilize transfer learning enhanced performance. Various python, R, Java like huggingface, sparkNLP, KoRpus well platforms DisGeNet, DeepEnroll, Transmol have enabled convenient applications. Discussion Challenges reproducibility, explainability, fairness, limited data, language-support, security need be overcome ensure wider adoption landscape. There opportunities improve performance existing expand newer MIDD. Conclusions This review provides an overview pitfalls approaches

Язык: Английский

---

Molecular programs of regional specification and neural stem cell fate progression in macaque telencephalon

Science, Год журнала: 2023, Номер 382(6667)

Опубликована: Окт. 12, 2023

During early telencephalic development, intricate processes of regional patterning and neural stem cell (NSC) fate specification take place. However, our understanding these in primates, including both conserved species-specific features, remains limited. Here, we profiled 761,529 single-cell transcriptomes from multiple regions the prenatal macaque telencephalon. We deciphered molecular programs organizing centers their cross-talk with NSCs, revealing primate-biased galanin-like peptide ( GALP ) signaling anteroventral Regional transcriptomic variations were observed along frontotemporal axis during stages neocortical NSC progression neurons astrocytes. Additionally, found that genes associated neuropsychiatric disorders brain cancer risk might play critical roles organizers progression.

Язык: Английский

---