Sameh K. Mohamed

18 papers receiving 432 citations

Peers

Sameh K. Mohamed
Comparison fields: 5 of 57
  • Computational Theory and Mathematics 181
  • Computational Mathematics 3
  • Health Informatics 6
  • Artificial Intelligence 153
  • Molecular Biology 249
Replace Aayah Nounu with:
Aayah Nounu United Kingdom
Vít Nováček Ireland
André Nascimento Brazil
Xiaorui Su China
Huijun Wang United States
Soheil Moosavinasab United States
Jiahua Rao China
Samuel Lampa Sweden
Payal Chandak United States
Pingjian Ding China
Sameh K. Mohamed relative to Aayah Nounu United Kingdom Aayah Nounu's profile →
Citations per field
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Aayah Nounu · 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

18 of 18 papers shown
#Work
1 2019159
2 2020100
3 202042
4
Predicting Polypharmacy Side-effects Using Knowledge Graph Embeddings.
202028
5 201921
6 202017
7 202314
8
On Predicting Recurrence in Early Stage Non-small Cell Lung Cancer.
202111
9 20198
10 20198
11
Loss Functions in Knowledge Graph Embedding Models.
20197
12 20235
13 20194
14 20184
15 20213
16 20173
17 20221
18
Predicting The Effects of Chemical-Protein Interactions On Proteins Using Tensor Factorisation.
20201

About Sameh K. Mohamed

Sameh K. Mohamed is a scholar working on Molecular Biology, Artificial Intelligence, Computational Theory and Mathematics, Cardiology and Cardiovascular Medicine and Statistical and Nonlinear Physics, having authored 18 papers that have together received 436 indexed citations. Recurring topics across this work include Advanced Graph Neural Networks (7 papers), Bioinformatics and Genomic Networks (5 papers), Machine Learning in Bioinformatics (5 papers), Computational Drug Discovery Methods (4 papers), Topic Modeling (3 papers), Cardiovascular Function and Risk Factors (2 papers), Semantic Web and Ontologies (2 papers) and Complex Network Analysis Techniques (2 papers). The work is most often cited by research in Computational Theory and Mathematics (181 citations), Computational Mathematics (3 citations), Health Informatics (6 citations), Artificial Intelligence (153 citations) and Molecular Biology (249 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, Virginia Calvo and Walter Kölch. Their work appears in journals such as Journal of Clinical Medicine, Bioinformatics, JCO Clinical Cancer Informatics, Information Sciences and PLoS Computational Biology.

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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