Author

Rabie Saidi

European Bioinformatics InstituteORCID 0000-0002-0449-5253Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Research Focus:database / resource· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Organism:C. elegans· 1 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Approach:deep learning· 1 Research Focus:dimensionality reduction· 1

Frequent Co-Authors

Tunca DoğaniD· 3 shared Ahmet Süreyya RifaioğluiD· 2 shared Andrew NightingaleiD· 2 shared Maria Jesus MartiniD· 2 shared Rengül Çetin-AtalayiD· 2 shared Vishal JoshiiD· 2 shared Volkan AtalayiD· 2 shared Abdulrahman HusseiniD· 1 shared

Activity by Year

2019-2022 · peak 1/yr

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10 top collaborators · 36 mutual co-author links

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3 shared papersBioinformatics

Tunca DoğanORCID

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3 papers

Recent·Most cited

Annotation of biologically relevant ligands in UniProtKB using ChEBI

Elisabeth Coudert, Sébastien Géhant, Edouard de Castro, Monica Pozzato, Delphine Baratin, Teresa Batista Neto, Christian J A Sigrist, Nicole Redaschi, Alan James Bridge, Lucila Aimo, Ghislaine Argoud‐Puy, Andrea H Auchincloss, Kristian B. Axelsen, Parit Bansal, Teresa M Batista Neto, , Elena Speretta

The paper reannotates ligand binding sites in UniProtKB using ChEBI identifiers and improves search facilities for these annotations.

Bioinformatics · Dec 9, 2022Peer-reviewedBioinformatics

CROssBAR: Comprehensive Resource of Biomedical Relations with Deep Learning Applications and Knowledge Graph Representations

Tunca Doğan, Heval Ataş, Vishal Joshi, Ahmet K. Atakan, Ahmet Süreyya Rifaioğlu, Esra Nalbat, Andrew Nightingale, Rabie Saidi, Vladimir Volynkin, Hermann Zellner, Rengül Çetin-Atalay, Maria Jesus Martin, Volkan Atalay

The paper introduces CROssBAR, a resource that integrates biomedical data and uses deep learning to predict entity relations, displaying results in knowledge graphs for systems-level analysis of diseases like COVID-19.

Nucleic Acids Research · Jun 11, 2021Peer-reviewedBioinformatics

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

Naihui Zhou, Yuxiang Jiang, Timothy R. Bergquist, Alexandra J. Lee, Balint Z Kacsoh, Alex W. Crocker, Kimberley A. Lewis, George P. Georghiou, Huy N. Nguyen, Md-Nafiz Hamid, Larry Davis, Tunca Doğan, Volkan Atalay, Ahmet Süreyya Rifaioğlu, Alperen Dalkıran, , Enrico Lavezzo

The paper reports results from the CAFA3 challenge, showing improved computational protein function prediction and new annotations for over 1000 genes through experimental screens in C. albicans, P. aureginosa, and Drosophila.

Genome biology · Nov 19, 2019Peer-reviewedBioinformatics