Sameh K. Mohamed

16 papers receiving 376 citations

Peers

Sameh K. Mohamed
Comparison fields: 5 of 59
  • Computational Theory and Mathematics 170
  • Health Informatics 7
  • Computational Mathematics 3
  • Artificial Intelligence 135
  • Molecular Biology 254
Replace Tamer N. Jarada with:
Tamer N. Jarada Canada
Xiaorui Su China
Vít Nováček Ireland
Payal Chandak United States
Yael Garten United States
Aayah Nounu United Kingdom
Remzi Çelebi Netherlands
Eni Minerali United States
Alex G. C. de Sá Australia
Saber A. Akhondi Netherlands
Sameh K. Mohamed relative to Tamer N. Jarada Canada Tamer N. Jarada's profile →
Citations per field
00.5×1.5×2.4×
Tamer N. Jarada · 1×
Citations per year

Countries citing papers authored by Sameh K. Mohamed

Since Specialization
Citations

This map shows the geographic impact of Sameh K. Mohamed's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Sameh K. Mohamed with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sameh K. Mohamed more than expected).

Fields of papers citing papers by Sameh K. Mohamed

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sameh K. Mohamed. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Sameh K. Mohamed. The network helps show where Sameh K. Mohamed may publish in the future.

Co-authors

The 25 scholars most cited alongside Sameh K. Mohamed, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Sameh K. Mohamed Line = papers co-authored together Sameh K. Mohamed links everyone, so they are left out of the graph.

All Works

16 of 16 papers shown
#Work
1 2019147
2 202088
3 202037
4
Predicting Polypharmacy Side-effects Using Knowledge Graph Embeddings.
202024
5 202017
6 201917
7 202311
8
On Predicting Recurrence in Early Stage Non-small Cell Lung Cancer.
202110
9 20197
10
Loss Functions in Knowledge Graph Embedding Models.
20196
11 20235
12 20194
13 20183
14 20221
15
Predicting The Effects of Chemical-Protein Interactions On Proteins Using Tensor Factorisation.
20201
16 20211

About Sameh K. Mohamed

Sameh K. Mohamed is a scholar working on Molecular Biology, Artificial Intelligence, Computational Theory and Mathematics, Radiology, Nuclear Medicine and Imaging and Cardiology and Cardiovascular Medicine, having authored 16 papers that have together received 379 indexed citations. Recurring topics across this work include Bioinformatics and Genomic Networks (6 papers), Machine Learning in Bioinformatics (5 papers), Computational Drug Discovery Methods (5 papers), Advanced Graph Neural Networks (5 papers), Lung Cancer Diagnosis and Treatment (2 papers), Lung Cancer Treatments and Mutations (2 papers), Radiomics and Machine Learning in Medical Imaging (2 papers) and Protein Structure and Dynamics (2 papers). The work is most often cited by research in Computational Theory and Mathematics (170 citations), Health Informatics (7 citations), Computational Mathematics (3 citations), Artificial Intelligence (135 citations) and Molecular Biology (254 citations). Sameh K. Mohamed has collaborated with scholars based in Ireland, United Kingdom and Czechia. Frequent co-authors include Vít Nováček, Aayah Nounu, Pierre-Yves Vandenbussche, Luca Costabello, Mariano Provencio, Mohan Timilsina, María Torrente, Pasquale Minervini, Bartomeu Massutí and David Matallanas. Their work appears in journals such as Journal of Clinical Medicine, Information Sciences, JCO Clinical Cancer Informatics, PLoS Computational Biology and Bioinformatics.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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