Muhammad Ammad-ud-din
Impact in
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- Computational Drug Discovery Methods
- Health Informatics top 10%
- Artificial Intelligence in Healthcare and Education
Papers in
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- Computational Drug Discovery Methods 4
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- Recommender Systems and Techniques 2
- Educational Technology in Learning 1
- Co-authors
- Suleiman A. Khan (6 shared papers)Samuel Kaski (4 shared papers)Tero A. Aittokallio (3 shared papers)Krister Wennerberg (3 shared papers)Olli Kallioniemi (3 shared papers)Disha Malani (2 shared papers)Astrid Murumägi (1 shared paper)Tuomo Laitinen (1 shared paper)
- Journals
- Bioinformatics (2 papers)Royal Society Open Science (1 paper)Nature Communications (1 paper)Nicotine & Tobacco Research (1 paper)npj Precision Oncology (1 paper)
- Partner nations
- FinlandUnited StatesUnited Kingdom
In The Last Decade
Muhammad Ammad-ud-din
13 papers receiving 1k citations
Muhammad Ammad-ud-din's Hit Papers
Peers
Comparison fields: 5 of 83
- Computational Theory and Mathematics 532
- Health Informatics 31
- Cancer Research 128
- Molecular Biology 270
- Rheumatology 46
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 | A community effort to assess and improve drug sensitivity prediction algorithms Hit paper breakdown → | 2014 | 594 |
| 2 | 2014 | 95 | |
| 3 | 2016 | 90 | |
| 4 | 2016 | 64 | |
| 5 | 2017 | 56 | |
| 6 | 2021 | 52 | |
| 7 | 2021 | 21 | |
| 8 | 2017 | 17 | |
| 9 | 2021 | 13 | |
| 10 | 2020 | 9 | |
| 11 | 2024 | 9 | |
| 12 | How will digital media impact education | 2014 | 2 |
| 13 | 2016 | 2 |
About Muhammad Ammad-ud-din
Muhammad Ammad-ud-din is a scholar working on Computational Theory and Mathematics, Information Systems, Artificial Intelligence, Statistics and Probability and Hematology, having authored 13 papers that have together received 1k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (4 papers), Privacy-Preserving Technologies in Data (2 papers), Recommender Systems and Techniques (2 papers), Epigenetics and DNA Methylation (1 paper), Smoking Behavior and Cessation (1 paper), Sentiment Analysis and Opinion Mining (1 paper), Educational Technology in Learning (1 paper) and Rheumatoid Arthritis Research and Therapies (1 paper). The work is most often cited by research in Computational Theory and Mathematics (532 citations), Health Informatics (31 citations), Cancer Research (128 citations), Molecular Biology (270 citations) and Rheumatology (46 citations). Muhammad Ammad-ud-din has collaborated with scholars based in Finland, United States and United Kingdom. Frequent co-authors include Suleiman A. Khan, Samuel Kaski, Tero A. Aittokallio, Krister Wennerberg, Olli Kallioniemi, Disha Malani, Astrid Murumägi, Tuomo Laitinen, Elisabeth Georgii and Mehmet Gönen. Their work appears in journals such as Bioinformatics, Royal Society Open Science, Nature Communications, Nicotine & Tobacco Research and npj Precision Oncology.
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.