Muhammad Ammad-ud-din
Impact in
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- Computational Drug Discovery Methods
Papers in
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- Gene expression and cancer classification 2
- Protein Degradation and Inhibitors 1
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- Privacy-Preserving Technologies in Data 2
- Co-authors
- Suleiman A. Khan (7 shared papers)Samuel Kaski (5 shared papers)Tero Aittokallio (4 shared papers)Krister Wennerberg (4 shared papers)Olli Kallioniemi (4 shared papers)Disha Malani (2 shared papers)Astrid Murumägi (1 shared paper)Elisabeth Georgii (1 shared paper)
- Journals
- Blood (3 papers)Bioinformatics (2 papers)Journal of Machine Learning Research (1 paper)npj Precision Oncology (1 paper)Nicotine & Tobacco Research (1 paper)
- Partner nations
- FinlandUnited StatesSweden
In The Last Decade
Muhammad Ammad-ud-din
15 papers receiving 349 citations
Peers
Comparison fields: 5 of 67
- Computational Mathematics 6
- Computational Theory and Mathematics 144
- Health Informatics 6
- Cancer Research 37
- Molecular Biology 173
Countries citing papers authored by Muhammad Ammad-ud-din
This map shows the geographic impact of Muhammad Ammad-ud-din'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 Muhammad Ammad-ud-din with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Muhammad Ammad-ud-din more than expected).
Fields of papers citing papers by Muhammad Ammad-ud-din
This network shows the impact of papers produced by Muhammad Ammad-ud-din. 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 Muhammad Ammad-ud-din. The network helps show where Muhammad Ammad-ud-din may publish in the future.
Co-authors
The 25 scholars most cited alongside Muhammad Ammad-ud-din, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2014 | 89 | |
| 2 | 2016 | 86 | |
| 3 | 2017 | 56 | |
| 4 | 2021 | 48 | |
| 5 | 2021 | 17 | |
| 6 | 2017 | 15 | |
| 7 | 2021 | 12 | |
| 8 | 2016 | 10 | |
| 9 | 2020 | 8 | |
| 10 | 2024 | 7 | |
| 11 | 2016 | 2 | |
| 12 | 2023 | 1 | |
| 13 | 2018 | 1 | |
| 14 | 2023 | 1 | |
| 15 | Machine learning methods for improving drug response prediction in cancer | 2017 | 1 |
About Muhammad Ammad-ud-din
Muhammad Ammad-ud-din is a scholar working on Molecular Biology, Artificial Intelligence, Computational Theory and Mathematics, Hematology and Information Systems, having authored 15 papers that have together received 354 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (3 papers), Acute Myeloid Leukemia Research (3 papers), Gene expression and cancer classification (2 papers), Recommender Systems and Techniques (2 papers), Privacy-Preserving Technologies in Data (2 papers), Cancer Genomics and Diagnostics (1 paper), Protein Degradation and Inhibitors (1 paper) and Data Visualization and Analytics (1 paper). The work is most often cited by research in Computational Mathematics (6 citations), Computational Theory and Mathematics (144 citations), Health Informatics (6 citations), Cancer Research (37 citations) and Molecular Biology (173 citations). Muhammad Ammad-ud-din has collaborated with scholars based in Finland, United States and Sweden. Frequent co-authors include Suleiman A. Khan, Samuel Kaski, Tero Aittokallio, Krister Wennerberg, Olli Kallioniemi, Disha Malani, Astrid Murumägi, Elisabeth Georgii, Mehmet Gönen and Tuomo Laitinen. Their work appears in journals such as Blood, Bioinformatics, Journal of Machine Learning Research, npj Precision Oncology and Nicotine & Tobacco Research.
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.