no code implementations • 30 Jul 2023 • Md Nurul Muttakin, Malik Shahid Sultan, Robert Hoehndorf, Hernando Ombao
Generative Adversarial Networks are used for generating the data using a generator and a discriminator, GANs usually produce high-quality images, but training GANs in an adversarial setting is a difficult task.
1 code implementation • 11 Jul 2023 • Tiffany J. Callahan, Ignacio J. Tripodi, Adrianne L. Stefanski, Luca Cappelletti, Sanya B. Taneja, Jordan M. Wyrwa, Elena Casiraghi, Nicolas A. Matentzoglu, Justin Reese, Jonathan C. Silverstein, Charles Tapley Hoyt, Richard D. Boyce, Scott A. Malec, Deepak R. Unni, Marcin P. Joachimiak, Peter N. Robinson, Christopher J. Mungall, Emanuele Cavalleri, Tommaso Fontana, Giorgio Valentini, Marco Mesiti, Lucas A. Gillenwater, Brook Santangelo, Nicole A. Vasilevsky, Robert Hoehndorf, Tellen D. Bennett, Patrick B. Ryan, George Hripcsak, Michael G. Kahn, Michael Bada, William A. Baumgartner Jr, Lawrence E. Hunter
Translational research requires data at multiple scales of biological organization.
1 code implementation • 11 May 2023 • Fernando Zhapa-Camacho, Robert Hoehndorf
We developed CatE, which uses the category-theoretical formulation of the semantics of the Description Logic $\mathcal{ALC}$ to generate a graph representation for ontology axioms.
1 code implementation • 29 Mar 2023 • Fernando Zhapa-Camacho, Robert Hoehndorf
Several approaches have been developed that generate embeddings for Description Logic ontologies and use these embeddings in machine learning.
1 code implementation • ICLR 2023 • Anuj Daga, Sumeer Ahmad Khan, David Gomez Cabrero, Robert Hoehndorf, Narsis A. Kiani, Jesper Tegnér
The LEP-AD model scales favorably in performance with the size of training data.
Ranked #1 on Protein Language Model on DAVIS-DTA
1 code implementation • 16 Aug 2022 • Zhenwei Tang, Tilman Hinnerichs, Xi Peng, Xiangliang Zhang, Robert Hoehndorf
Many ontologies, i. e., Description Logic (DL) knowledge bases, have been developed to provide rich knowledge about various domains, and a lot of them are based on ALC, i. e., a prototypical and expressive DL, or its extensions.
no code implementations • 29 May 2022 • Zhenwei Tang, Shichao Pei, Xi Peng, Fuzhen Zhuang, Xiangliang Zhang, Robert Hoehndorf
Neural logical reasoning (NLR) is a fundamental task to explore such knowledge bases, which aims at answering multi-hop queries with logical operations based on distributed representations of queries and answers.
no code implementations • 2 May 2022 • Zhenwei Tang, Shichao Pei, Zhao Zhang, Yongchun Zhu, Fuzhen Zhuang, Robert Hoehndorf, Xiangliang Zhang
Most real-world knowledge graphs (KG) are far from complete and comprehensive.
no code implementations • 28 Feb 2022 • Xi Peng, Zhenwei Tang, Maxat Kulmanov, Kexin Niu, Robert Hoehndorf
Since the intersection of boxes remains as a box, the intersectional closure is satisfied.
no code implementations • Conference 2020 • Jun Chen, Robert Hoehndorf, Mohamed Elhoseiny, Xiangliang Zhang
In natural language processing, relation extraction seeks to rationally understand unstructured text.
Ranked #16 on Relation Extraction on TACRED
1 code implementation • 27 Feb 2019 • Maxat Kulmanov, Wang Liu-Wei, Yuan Yan, Robert Hoehndorf
We address the problem of finding vector space embeddings for theories in the Description Logic $\mathcal{EL}^{++}$ that are also models of the TBox.
1 code implementation • 29 Apr 2018 • Fatima Zohra Smaili, Xin Gao, Robert Hoehndorf
Second, we evaluate our method on predicting gene-disease associations based on phenotype similarity by generating vector representations of genes and diseases using a phenotype ontology, and applying the obtained vectors to predict gene-disease associations.
1 code implementation • 31 Jan 2018 • Fatima Zohra Smaili, Xin Gao, Robert Hoehndorf
We propose the Onto2Vec method, an approach to learn feature vectors for biological entities based on their annotations to biomedical ontologies.
1 code implementation • 15 May 2017 • Maxat Kulmanov, Mohammed Asif Khan, Robert Hoehndorf
The functions of proteins are classified using the Gene Ontology (GO), which contains over 40, 000 classes.
1 code implementation • 13 Dec 2016 • Mona Alshahrani, Mohammed Asif Khan, Omar Maddouri, Akira R Kinjo, Núria Queralt-Rosinach, Robert Hoehndorf
Motivation: Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries.