Shahid Akbar
- Molecular Biology top 10%
- Computational Theory and Mathematics top 2%
- Microbiology top 2%
- Artificial Intelligence top 5%
- Radiology, Nuclear Medicine and Imaging top 10%
- Co-authors
- Maqsood HayatFarman AliQuan ZouAli RazaSalman KhanMuhammad TahirAshfaq AhmadMuhammad Iqbal
- Topics
- Machine Learning in Bioinformatics (30 papers)vaccines and immunoinformatics approaches (17 papers)Antimicrobial Peptides and Activities (10 papers)
- Partner nations
- PakistanChinaSaudi Arabia
In The Last Decade
Shahid Akbar
37 papers receiving 1.7k citations
Hit Papers
Peers
Comparison fields: 5 of 110
- Molecular Biology 1.2k
- Computational Theory and Mathematics 278
- Microbiology 250
- Artificial Intelligence 237
- Radiology, Nuclear Medicine and Imaging 120
Countries citing papers authored by Shahid Akbar
This map shows the geographic impact of Shahid Akbar'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 Shahid Akbar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shahid Akbar more than expected).
Fields of papers citing papers by Shahid Akbar
This network shows the impact of papers produced by Shahid Akbar. 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 Shahid Akbar. The network helps show where Shahid Akbar may publish in the future.
Co-authorship network of co-authors of Shahid Akbar
This figure shows the co-authorship network connecting the top 25 collaborators of Shahid Akbar. A scholar is included among the top collaborators of Shahid Akbar 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 Shahid Akbar. Shahid Akbar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 13 | |
| 4 | 16 | |
| 5 | 30 | |
| 6 | iAFPs-Mv-BiTCN: Predicting antifungal peptides using self-attention transformer embedding and transform evolutionary based multi-view features with bidirectional temporal convolutional networksbreakdown → | 67 |
| 7 | Deepstacked-AVPs: predicting antiviral peptides using tri-segment evolutionary profile and word embedding based multi-perspective features with deep stacking modelbreakdown → | 71 |
| 8 | 23 | |
| 9 | 20 | |
| 10 | 36 | |
| 11 | 3 | |
| 12 | 58 | |
| 13 | 33 | |
| 14 | 87 | |
| 15 | 71 | |
| 16 | 77 | |
| 17 | A Monte Carlo Simulation Analysis of Panel Stationarity Tests under a Single Framework | 0 |
| 18 | 85 | |
| 19 | 128 | |
| 20 | 148 |
About Shahid Akbar
Shahid Akbar is a scholar working on Microbiology, Molecular Biology and Computational Theory and Mathematics, having authored 40 papers that have together received 1.8k indexed citations. Recurring topics across this work include Machine Learning in Bioinformatics (30 papers), vaccines and immunoinformatics approaches (17 papers) and Antimicrobial Peptides and Activities (10 papers). The work is most often cited by research in Microbiology (250 citations), Health Information Management (97 citations) and Molecular Biology (1.2k citations). Shahid Akbar has collaborated with scholars based in Pakistan, China and Saudi Arabia. Frequent co-authors include Maqsood Hayat, Farman Ali, Quan Zou, Ali Raza, Salman Khan, Muhammad Tahir, Ashfaq Ahmad, Muhammad Iqbal, Fawaz Khaled Alarfaj and Mian Ahmad Jan. Their work appears in journals such as Bioinformatics, Scientific Reports and IEEE Access.
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.