IUPHAR review: Drug Repurposing in Schizophrenia – An Updated Review of Clinical Trials DOI Creative Commons
Jihan K. Zaki, Jakub Tomasik,

Sabine Bahn

et al.

Pharmacological Research, Journal Year: 2025, Volume and Issue: unknown, P. 107633 - 107633

Published: Jan. 1, 2025

There is an urgent need for mechanistically novel and more efficacious treatments schizophrenia, especially those targeting negative cognitive symptoms with a favorable side-effect profile. Drug repurposing-the process of identifying new therapeutic uses already approved compounds-offers promising approach to overcoming the lengthy, costly, high-risk traditional CNS drug discovery. This review aims update our previous findings on clinical repurposing pipeline in schizophrenia. We examined studies conducted between 2018 2024, 61 trials evaluating 40 unique repurposed candidates. These encompassed broad range pharmacological mechanisms, including immunomodulation, enhancement, hormonal, metabolic, neurotransmitter modulation. A notable development combination muscarinic modulators xanomeline, compound antipsychotic properties, trospium, included mitigate peripheral side effects, now by FDA as first decades fundamentally mechanism action. Moving beyond dopaminergic paradigm such highlight opportunities improve treatment-resistant alleviate adverse effects. Overall, evolving landscape illustrates significant shift rationale schizophrenia development, highlighting potential silico strategies, biomarker-based patient stratification, personalized that align underlying pathophysiological processes.

Language: Английский

Biomedical Big Data Technologies, Applications, and Challenges for Precision Medicine: A Review DOI Creative Commons
Xue Yang, Kexin Huang, Dewei Yang

et al.

Global Challenges, Journal Year: 2023, Volume and Issue: 8(1)

Published: Nov. 20, 2023

The explosive growth of biomedical Big Data presents both significant opportunities and challenges in the realm knowledge discovery translational applications within precision medicine. Efficient management, analysis, interpretation big data can pave way for groundbreaking advancements However, unprecedented strides automated collection large-scale molecular clinical have also introduced formidable terms analysis interpretation, necessitating development novel computational approaches. Some potential include curse dimensionality, heterogeneity, missing data, class imbalance, scalability issues. This overview article focuses on recent progress breakthroughs application Key aspects are summarized, including content, sources, technologies, tools, challenges, existing gaps. Nine fields-Datawarehouse electronic medical record, imaging informatics, Artificial intelligence-aided surgical design surgery optimization, omics health monitoring graph, public security privacy-are discussed.

Language: Английский

Citations

26
An introduction to machine learning and generative artificial intelligence for otolaryngologists—head and neck surgeons: a narrative review DOI
Isaac L. Alter,

Karly Chan,

Jérôme R. Lechien

et al.

European Archives of Oto-Rhino-Laryngology, Journal Year: 2024, Volume and Issue: 281(5), P. 2723 - 2731

Published: Feb. 23, 2024

Language: Английский

Citations

14
Augmented non-hallucinating large language models as medical information curators DOI Creative Commons
Stephen Gilbert, Jakob Nikolas Kather, Aidan Hogan

et al.

npj Digital Medicine, Journal Year: 2024, Volume and Issue: 7(1)

Published: April 23, 2024

Reliably processing and interlinking medical information has been recognized as a critical foundation to the digital transformation of workflows, despite development ontologies, optimization these major bottleneck medicine. The advent large language models brought great excitement, maybe solution medicines' 'communication problem' is in sight, but how can known weaknesses models, such hallucination non-determinism, be tempered? Retrieval Augmented Generation, particularly through knowledge graphs, an automated approach that deliver structured reasoning model truth alongside LLMs, relevant structuring therefore also decision support.

Language: Английский

Citations

14
Accurate and interpretable drug-drug interaction prediction enabled by knowledge subgraph learning DOI Creative Commons
Yaqing Wang, Zhimu Yang, Quanming Yao

et al.

Communications Medicine, Journal Year: 2024, Volume and Issue: 4(1)

Published: March 28, 2024

Abstract Background Discovering potential drug-drug interactions (DDIs) is a long-standing challenge in clinical treatments and drug developments. Recently, deep learning techniques have been developed for DDI prediction. However, they generally require huge number of samples, while known DDIs are rare. Methods In this work, we present KnowDDI, graph neural network-based method that addresses the above challenge. KnowDDI enhances representations by adaptively leveraging rich neighborhood information from large biomedical knowledge graphs. Then, it learns subgraph each drug-pair to interpret predicted DDI, where edges associated with connection strength indicating importance or resembling between whose unknown. Thus, lack implicitly compensated enriched propagated similarities. Results Here show evaluation results on two benchmark datasets. obtains state-of-the-art prediction performance better interpretability. We also find suffers less than existing works given sparser graph. This indicates similarities play more important role compensating when enriched. Conclusions nicely combines efficiency prior As an original open-source tool, can help detect possible broad range relevant interaction tasks, such as protein-protein interactions, drug-target disease-gene eventually promoting development biomedicine healthcare.

Language: Английский

Citations

13
Poisoning medical knowledge using large language models DOI
Junwei Yang, Hanwen Xu,

Srbuhi Mirzoyan

et al.

Nature Machine Intelligence, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 20, 2024

Language: Английский

Citations

13
Has multimodal learning delivered universal intelligence in healthcare? A comprehensive survey DOI Creative Commons
Qika Lin, Y. C. Zhu, Mei Xin

et al.

Information Fusion, Journal Year: 2024, Volume and Issue: unknown, P. 102795 - 102795

Published: Nov. 1, 2024

Language: Английский

Citations

8
Multimodal learning on graphs for disease relation extraction DOI Creative Commons
Yucong Lin,

Keming Lu,

Sheng Yu

et al.

Journal of Biomedical Informatics, Journal Year: 2023, Volume and Issue: 143, P. 104415 - 104415

Published: June 3, 2023

Language: Английский

Citations

17
A framework towards digital twins for type 2 diabetes DOI Creative Commons
Y Zhang, Guangrong Qin, Boris Aguilar

et al.

Frontiers in Digital Health, Journal Year: 2024, Volume and Issue: 6

Published: Jan. 26, 2024

Introduction A digital twin is a virtual representation of patient's disease, facilitating real-time monitoring, analysis, and simulation. This enables the prediction disease progression, optimization care delivery, improvement outcomes. Methods Here, we introduce framework for type 2 diabetes (T2D) that integrates machine learning with multiomic data, knowledge graphs, mechanistic models. By analyzing substantial clinical dataset, constructed predictive models to forecast progression. Furthermore, graphs were employed elucidate contextualize multiomic–disease relationships. Results discussion Our findings not only reaffirm known targetable components but also spotlight novel ones, unveiled through this integrated approach. The versatile presented in study can be incorporated into system, enhancing our grasp diseases propelling advancement precision medicine.

Language: Английский

Citations

7
Comprehensive review of Transformer‐based models in neuroscience, neurology, and psychiatry DOI Creative Commons
Shan Cong, Hang Wang, Yang Zhou

et al.

Brain‐X, Journal Year: 2024, Volume and Issue: 2(2)

Published: April 26, 2024

Abstract This comprehensive review aims to clarify the growing impact of Transformer‐based models in fields neuroscience, neurology, and psychiatry. Originally developed as a solution for analyzing sequential data, Transformer architecture has evolved effectively capture complex spatiotemporal relationships long‐range dependencies that are common biomedical data. Its adaptability effectiveness deciphering intricate patterns within medical studies have established it key tool advancing our understanding neural functions disorders, representing significant departure from traditional computational methods. The begins by introducing structure principles architectures. It then explores their applicability, ranging disease diagnosis prognosis evaluation cognitive processes decoding. specific design modifications tailored these applications subsequent on performance also discussed. We conclude providing assessment recent advancements, prevailing challenges, future directions, highlighting shift neuroscientific research clinical practice towards an artificial intelligence‐centric paradigm, particularly given prominence most successful large pre‐trained models. serves informative reference researchers, clinicians, professionals who interested harnessing transformative potential

Language: Английский

Citations

7
The OREGANO knowledge graph for computational drug repurposing DOI Creative Commons
Marina Boudin, Gayo Diallo, Martin Drancé

et al.

Scientific Data, Journal Year: 2023, Volume and Issue: 10(1)

Published: Dec. 6, 2023

Abstract Drug repositioning is a faster and more affordable solution than traditional drug discovery approaches. From this perspective, computational using knowledge graphs very promising direction. Knowledge constructed from data information can be used to generate hypotheses (molecule/drug - target links) through link prediction machine learning algorithms. However, it remains rare have holistically graph the broadest possible features characteristics, which freely available community. The OREGANO aims at filling gap. purpose of paper present graph, includes natural compounds related data. was developed scratch by retrieving directly sources integrated. We therefore designed expected model proposed method for merging nodes between different sources, finally, were cleaned. as well source codes ETL process, are openly on GitHub project ( https://gitub.u-bordeaux.fr/erias/oregano ).

Language: Английский

Citations

16