27 papers · ranked by Valyu relevance
Yu Sheng, Zheng Yuan, Jun Xia, Shengxuan Luo + 10 more
'Sihang Zeng' 'Jingyi Ren' 'Hongyi Yuan' 'Zhengyun Zhao' 'Yucong Lin' 'Keming Lu' 'Jing Wang' 'Yutao Xie' 'Heung‐Yeung Shum'] Biomedical knowledge graphs (BioMedKGs) are essential infrastructures for biomedical and healthcare big data and artificial intelligence (AI), facilitating natural language processing, model…
Tien Dang, Viet Thanh Duy Nguyen, Minh Tuan Le, Truong-Son Hy
Biomedical Knowledge Graphs (BKGs) integrate diverse datasets to elucidate complex relationships within the biomedical field. Effective link prediction on these graphs can uncover valuable connections, such as potential new drug-disease relations. We introduce a novel multimodal approach that unifies embeddings from…
Haniye Sherafatmandjoo, Mohammad Akbari, Zahed Rahmati
Knowledge graphs (KGs) have emerged as a promising solution for integrating and reasoning over complex biomedical and clinical data in healthcare. By representing structured relationships among entities such as diseases, drugs, symptoms, and patient records, KGs provide a semantic backbone for decision-making…
Jianqiang Dong, Junwu Liu, Yifan Wei, Peilin Huang + 1 more
In biomedical research, the utilization of Knowledge Graph (KG) has proven valuable in gaining deep understanding of various processes. In this study, we constructed a comprehensive biomedical KG, named as MegaKG, by integrating a total of 23 primary data sources, which finally consisted of 188, 844 nodes/entities and…
Yichun Feng, Lu Zhou, Yikai Zheng, Ruikun He + 2 more
In recent years, Large Language Models (LLMs) have shown promise in various domains, notably in biomedical sciences. However, their real-world application is often limited by issues like erroneous outputs and hallucinatory responses. We developed the Knowledge Graph-based Thought (KGT) framework, an innovative solution…
Bilal Abu-Salih, Muhammad Al-Qurishi, Mohammed Alweshah, Mohammad AL-Smadi + 2 more
'Mohammad AL-Smadi' 'Reem Alfayez' 'Heba Saadeh'] The incorporation of data analytics in the healthcare industry has made significant progress, driven by the demand for efficient and effective big data analytics solutions. Knowledge graphs (KGs) have proven utility in this arena and are rooted in a number of healthcare…
Yichun Feng, Lu Zhou, Chao Ma, Yikai Zheng + 2 more
In this article, we introduce an innovative framework called knowledge graph-based thought (KGT), which integrates LLMs with KGs through employing LLMs for reasoning on the schema of KGs to mitigate factual hallucinations of LLMs, as shown in Fig. [fig1]. Unlike traditional methods, KGT does not directly retrieve…
Deepak Unni, Sierra Moxon, Michael Bada, Matthew Brush + 20 more
'Richard Bruskiewich' 'Paul A. Clemons' 'Vlado Dančík' 'Michel Dumontier' 'Karamarie Fecho' 'Gustavo Glusman' 'Jennifer Hadlock' 'Nomi L. Harris' 'Arpita Joshi' 'Tim Putman' 'Guangrong Qin' 'Stephen A. Ramsey' 'Kent Shefchek' 'Harold R. Solbrig' 'Karthik Soman' 'Anne Thessen' 'Melissa Haendel' 'Chris Bizon' 'Chris…
Mona Alshahrani, Abdullah Almansour, Asma Alkhaldi, Maha A. Thafar + 4 more
'Mahmut Uludag' 'Magbubah Essack' 'Robert Hoehndorf' 'Shuihua Wang'] Biomedical knowledge is represented in structured databases and published in biomedical literature, and different computational approaches have been developed to exploit each type of information in predictive models. However, the information in…
Katherina G Cortes, Shilpa Sundar, Sarah Gehrke, Keenan Manpearl + 12 more
'Junxia Lin' 'Daniel Robert Korn' 'Harry Caufield' 'Kevin Schaper' 'Justin Reese' 'Kushal Koirala' 'Lawrence E Hunter' 'E. Kathleen Carter' 'Marcello DeLuca' 'Arjun Krishnan' 'Chris Mungall' 'Melissa Haendel'] Biomedical knowledge graphs (KGs) are widely used across research and translational settings, yet their design…
Tiffany J. Callahan, Ignacio J. Tripodi, Adrianne L. Stefanski, Luca Cappelletti + 28 more
Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods…
Guangrong Qin, Kamileh Narsinh, Qi Wei, Jared C. Roach + 15 more
As large clinical and multiomics datasets and knowledge resources accumulate, they need to be transformed into computable and actionable information to support automated reasoning. These datasets range from laboratory experiment results to electronic health records (EHRs). Barriers to accessibility and sharing of such…
Bilal Abu-Salih, Muhammad AL-Qurishi, Mohammed Alweshah, Mohammad AL-Smadi + 2 more
The incorporation of data analytics in the healthcare industry has made significant progress, driven by the demand for efficient and effective big data analytics solutions. Knowledge graphs (KGs) have proven utility in this arena and are rooted in a number of healthcare applications to furnish better data…
Yuanhao Huang, Zhaowei Han, Xin Luo, Xuteng Luo + 20 more
Knowledge graphs have recently emerged as a powerful data structure to organize biomedical knowledge with explicit representation of nodes and edges. The knowledge representation is in a machine-learning ready format and supports explainable AI models. However, PubMed, the largest and richest biomedical knowledge…
Ahmed Hossameldin Mohamed, Karim S Shalaby, Abish Kaladharan, Heval Atas Güvenilir + 1 more
Biomedical Knowledge Graphs (BKGs) offer integrative representations of complex biology, yet their utility is compromised by the limitations of current construction methods: manual curation offers high fidelity but is unscalable, whereas purely automated Large Language Model (LLM) approaches often yield broad networks…
Meije Mathé, Guillaume Laisney, Olivier Filangi, Franck Giacomoni + 4 more
Knowledge graphs (KGs) are a robust formalism for structuring biomedical knowledge, but large-scale KGs often require complex queries, are difficult for non-experts to explore, and lack real-world context (such as experimental data, clinical conditions, patients symptoms). This limits their usability for addressing…
Yuxing Lu, Sin Yee Goi, Xukai Zhao, Jinzhuo Wang
Applications Authors: ['Yuxing Lu' 'Sin Yee Goi' 'Xukai Zhao' 'Jinzhuo Wang'] Biomedical knowledge graphs (BKGs) have emerged as powerful tools for organizing and leveraging the vast and complex data found across the biomedical field. Yet, current reviews of BKGs often limit their scope to specific domains or methods…
Antonio Di Maria, Lorenzo Bellomo, Fabrizio Billeci, Alfio Cardillo + 5 more
Scientific investigations produce massive amounts of data collected daily in publications, databases, clinical trials, etc. In particular, in the bio-medical area, thanks to fast-track publication journals, the number of published papers has increased significantly (), so identifying relevant knowledge from such…
Yichun Feng, Jiawei Wang, Ruikun He, Lu Zhou + 1 more
The advancement of large language models (LLMs) has significantly accelerated progress in natural language processing (NLP), particularly in complex tasks like question answering (QA), with notable breakthroughs in specialized fields such as biomedical science . However, despite these advancements, the application of…
Yijia Xiao, Dylan Steinecke, Alexander R. Pelletier, Yushi Bai + 2 more
'Peipei Ping' 'Wei Wang'] Knowledge graphs (KGs) have emerged as a powerful framework for representing and integrating complex biomedical information. However, assembling KGs from diverse sources remains a significant challenge in several aspects, including entity alignment, scalability, and the need for continuous…
J. Harry Caufield, Tim Putman, Kevin Schaper, Deepak Unni + 22 more
'Harshad Hegde' 'Tiffany J Callahan' 'Luca Cappelletti' 'Sierra AT Moxon' 'Vida Ravanmehr' 'Seth Carbon' 'Lauren Chan' 'Katherina G Cortes' 'Kent Shefchek' 'Glass Elsarboukh' 'James P. Balhoff' 'Tommaso Fontana' 'Nicolas Matentzoglu' 'Richard M Bruskiewich' 'Anne Thessen' 'Nomi L. Harris' 'Monica Muñoz‐Torres' 'Melissa…
Lauren Nicole DeLong, Ramon Fernández Mir, Zonglin Ji, F. Smith + 1 more
'Jacques Fleuriot'] Biomedical datasets are often modeled as knowledge graphs (KGs) because they capture the multi-relational, heterogeneous, and dynamic natures of biomedical systems. KG completion (KGC), can, therefore, help researchers make predictions to inform tasks like drug repositioning. While previous…
Gergely Zahoránszky-Kőhalmi, Brandon Walker, Nathan Miller, Brett Yang + 11 more
The recent SmartGraph platform facilitates the execution of complex drug-discovery workflows with ease in the network-pharmacology paradigm. However, at the time of its publication, we identified the need for the development of an Application Programming Interface (API) that could promote biomedical data integration…
Frédéric Burdet, Pierre-Marie Allard, Louis-Felix Nothias, Olivier Kirchhoffer + 16 more
Plants have a complex chemo-diversity and represent a reservoir of potential new therapeutic agents. Within a Swiss research project, six scientific research groups from different disciplines are collaborating to investigate a collection of more than 17’000 unique dried plant extracts. It aims to find new bioactive…
Arnaud Gaudry, Marco Pagni, Florence Mehl, Sébastien Moretti + 10 more
Modern natural products (NPs) research relies on untargeted liquid chromatography coupled with mass spectrometry metabolomics. Together with cutting-edge processing and computational annotation strategies, such approaches can yield extensive spectral and structural information. However, current processing workflows…
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The nanosafety domain has seen significant advancements in data generation and sharing, yet challenges remain in ensuring data interoperability and reuse. This article focuses on developing a semantic interoperability framework for nanosafety data to maximize the FAIRness (Findability, Accessibility, Interoperability…
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Artificial intelligence (AI) is reshaping scientific research by accelerating discovery and enabling the analysis of complex data that traditional methods struggle to handle. This review examines over 310,000 journal articles and patents from the CAS Content Collection (2015–2025), with a focus on, biomedical research…