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 4
- Bioinformatics and Genomic Networks 2
- Advanced biosensing and bioanalysis techniques 1
- Genetics, Bioinformatics, and Biomedical Research 1
- Protein Degradation and Inhibitors 1
- Co-authors
- Suleiman A. Khan (6 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)Tuomo Laitinen (1 shared paper)
- Journals
- Blood (3 papers)Bioinformatics (2 papers)Nicotine & Tobacco Research (1 paper)Royal Society Open Science (1 paper)Journal of Machine Learning Research (1 paper)
- Partner nations
- FinlandUnited StatesSweden
In The Last Decade
Muhammad Ammad-ud-din
13 papers receiving 286 citations
Peers
Comparison fields: 5 of 65
- Computational Theory and Mathematics 160
- Health Informatics 5
- Computational Mathematics 2
- Molecular Biology 205
- Cancer Research 42
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 | 84 | |
| 2 | 2016 | 81 | |
| 3 | 2017 | 55 | |
| 4 | 2021 | 16 | |
| 5 | 2017 | 15 | |
| 6 | 2021 | 12 | |
| 7 | 2016 | 9 | |
| 8 | 2020 | 7 | |
| 9 | 2024 | 5 | |
| 10 | 2016 | 2 | |
| 11 | 2018 | 1 | |
| 12 | Machine learning methods for improving drug response prediction in cancer | 2017 | 1 |
| 13 | 2023 | 1 | |
| 14 | 2023 | 0 |
About Muhammad Ammad-ud-din
Muhammad Ammad-ud-din is a scholar working on Molecular Biology, Artificial Intelligence, Computational Theory and Mathematics, Hematology and Oncology, having authored 14 papers that have together received 289 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (4 papers), Gene expression and cancer classification (4 papers), Acute Myeloid Leukemia Research (3 papers), Bioinformatics and Genomic Networks (2 papers), Advanced biosensing and bioanalysis techniques (1 paper), Genetics, Bioinformatics, and Biomedical Research (1 paper), Protein Degradation and Inhibitors (1 paper) and Smoking Behavior and Cessation (1 paper). The work is most often cited by research in Computational Theory and Mathematics (160 citations), Health Informatics (5 citations), Computational Mathematics (2 citations), Molecular Biology (205 citations) and Cancer Research (42 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, Tuomo Laitinen, Antti Poso and Elisabeth Georgii. Their work appears in journals such as Blood, Bioinformatics, Nicotine & Tobacco Research, Royal Society Open Science and Journal of Machine Learning 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.