Aayah Nounu

1.1k citations
7 papers · 295 indexed · h-index 5
Topics
Bioinformatics and Genomic Networks (3 papers)Machine Learning in Bioinformatics (2 papers)Protein Structure and Dynamics (2 papers)

In The Last Decade

Aayah Nounu

7 papers receiving 294 citations

Peers

Aayah Nounu
Comparison fields: 5 of 62
  • Molecular Biology 199
  • Computational Theory and Mathematics 121
  • Artificial Intelligence 98
  • Materials Chemistry 30
  • Genetics 24
Replace Soheil Moosavinasab with:
Soheil Moosavinasab United States
Christine Hessler United States
Michael Weidlich Germany
Sameh K. Mohamed Ireland
Yojana Gadiya Germany
Hung Yu Kao Taiwan
Elgar Pichler United States
Youngmi Yoon South Korea
Hwisang Jeon South Korea
Remzi Çelebi Netherlands
Aayah Nounu relative to Soheil Moosavinasab United States Soheil Moosavinasab's profile →
Citations per field
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Soheil Moosavinasab · 1×
Citations per year

Countries citing papers authored by Aayah Nounu

Since Specialization
Citations

This map shows the geographic impact of Aayah Nounu'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 Aayah Nounu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aayah Nounu more than expected).

Fields of papers citing papers by Aayah Nounu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Aayah Nounu. 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 Aayah Nounu. The network helps show where Aayah Nounu may publish in the future.

Co-authorship network of co-authors of Aayah Nounu

This figure shows the co-authorship network connecting the top 25 collaborators of Aayah Nounu. A scholar is included among the top collaborators of Aayah Nounu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Aayah Nounu. Aayah Nounu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

7 of 7 papers shown
#WorkIndexed citations
1 2
2 13
3 27
4 88
5
Predicting The Effects of Chemical-Protein Interactions On Proteins Using Tensor Factorisation.
1
6 17
7 147

About Aayah Nounu

Aayah Nounu is a scholar working on Computational Theory and Mathematics, Endocrinology, Diabetes and Metabolism and Molecular Biology, having authored 7 papers that have together received 295 indexed citations. Recurring topics across this work include Bioinformatics and Genomic Networks (3 papers), Machine Learning in Bioinformatics (2 papers) and Protein Structure and Dynamics (2 papers). The work is most often cited by research in Computational Theory and Mathematics (121 citations), Artificial Intelligence (98 citations) and Molecular Biology (199 citations). Aayah Nounu has collaborated with scholars based in United Kingdom, Ireland and Netherlands. Frequent co-authors include Sameh K. Mohamed, Vít Nováček, Caroline L. Relton, Rebecca C. Richmond, Siddhartha Kar, Chloe Friedman, Giulia Mancano, Rosa H. Mulder, Janine F. Felix and Dana Dabelea. Their work appears in journals such as Bioinformatics, Breast Cancer Research and Briefings in 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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