28 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…
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…
David N. Nicholson, Casey S. Greene
Knowledge graphs can support many biomedical applications. These graphs represent biomedical concepts and relationships in the form of nodes and edges. In this review, we discuss how these graphs are constructed and applied with a particular focus on how machine learning approaches are changing these processes.…
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…
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…
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…
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…
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…
Mona Alshahrani, Maha A. Thafar, Magbubah Essack, Othman Soufan
Linked data and bio-ontologies enabling knowledge representation, standardization, and dissemination are an integral part of developing biological and biomedical databases. That is, linked data and bio-ontologies are employed in databases to maintain data integrity, data organization, and to empower search…
Tiffany J. Callahan, Ignacio J. Tripodi, Lawrence E. Hunter, William A. Baumgartner
Although knowledge graphs (KGs) are used extensively in biomedical research to model complex phenomena, many KG construction methods remain largely unable to account for the use of different standardized terminologies or vocabularies, are often difficult to use, and perform poorly as the size of the KG increases in…
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…
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…
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…
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…
Tiffany J Callahan, Harrison Pielke-Lombardo, Ignacio J. Tripodi, Lawrence Hunter
'Lawrence Hunter'] Knowledge-based biomedical data science (KBDS) involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey the progress in the…
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…
Authors not listed
Molecular property prediction has become essential in accelerating advancements in drug discovery and materials science. Graph Neural Networks have recently demonstrated remarkable success in molecular representation learning; however, their broader adoption is impeded by two significant challenges: (1) data scarcity…
Authors not listed
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…
Authors not listed
Protein-ligand interaction prediction with proteochemometric (PCM) models can provide valuable insights during early drug discovery and chemical safety assessment. These models have benefitted from the large amount of data available in bioactivity databases. However, an issue that is often overlooked when using this…
Jose L. Medina-Franco, Edgar López-López, Johny R. Rodríguez-Pérez, Héctor F. Cortés-Hernández + 1 more
In Chemoinformatics, as in many other computational-related disciplines, it is a common practice to identify the “single best” approach or methodology, for instance, identify the best fingerprint representation, the best single virtual screening approach or protocol, the optimal representation of the chemical space…