Karim Abbasi

1.1k total citations
21 papers, 722 citations indexed

About

Karim Abbasi is a scholar working on Molecular Biology, Computational Theory and Mathematics and Materials Chemistry. According to data from OpenAlex, Karim Abbasi has authored 21 papers receiving a total of 722 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 13 papers in Computational Theory and Mathematics and 7 papers in Materials Chemistry. Recurrent topics in Karim Abbasi's work include Computational Drug Discovery Methods (13 papers), Protein Structure and Dynamics (7 papers) and Machine Learning in Materials Science (7 papers). Karim Abbasi is often cited by papers focused on Computational Drug Discovery Methods (13 papers), Protein Structure and Dynamics (7 papers) and Machine Learning in Materials Science (7 papers). Karim Abbasi collaborates with scholars based in Iran, United States and Finland. Karim Abbasi's co-authors include Parvin Razzaghi, Ali Masoudi‐Nejad, Jahan B. Ghasemi, Antti Poso, Massoud Amanlou, Sajjad Gharaghani, Hojjat Zeraati, Fatemeh Rafiei, Mahboubeh Parsaeian and Shima Rashidi and has published in prestigious journals such as Bioinformatics, PLoS ONE and Pharmacological Reviews.

In The Last Decade

Karim Abbasi

20 papers receiving 707 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Karim Abbasi Iran 15 455 446 180 108 51 21 722
Parvin Razzaghi Iran 11 339 0.7× 328 0.7× 133 0.7× 91 0.8× 92 1.8× 24 581
Qiujie Lv China 14 266 0.6× 275 0.6× 148 0.8× 99 0.9× 48 0.9× 23 543
Ziduo Yang China 15 460 1.0× 485 1.1× 239 1.3× 121 1.1× 65 1.3× 26 809
Shuting Jin China 14 507 1.1× 357 0.8× 139 0.8× 88 0.8× 15 0.3× 31 739
Xiaorui Su China 18 769 1.7× 549 1.2× 113 0.6× 156 1.4× 26 0.5× 43 1.0k
Siyi Zhu China 7 623 1.4× 499 1.1× 114 0.6× 110 1.0× 15 0.3× 11 916
Ruihan Yang China 9 335 0.7× 344 0.8× 126 0.7× 49 0.5× 70 1.4× 31 610
Tianfan Fu United States 11 368 0.8× 380 0.9× 198 1.1× 251 2.3× 18 0.4× 37 812
Yang Qiu China 13 509 1.1× 503 1.1× 159 0.9× 113 1.0× 11 0.2× 34 804

Countries citing papers authored by Karim Abbasi

Since Specialization
Citations

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

Fields of papers citing papers by Karim Abbasi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Karim Abbasi

This figure shows the co-authorship network connecting the top 25 collaborators of Karim Abbasi. A scholar is included among the top collaborators of Karim Abbasi 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 Karim Abbasi. Karim Abbasi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Abbasi, Karim, et al.. (2025). DFT_ANPD: A dual-feature two-sided attention network for anticancer natural products detection. Computers in Biology and Medicine. 194. 110442–110442. 3 indexed citations
3.
Abbasi, Karim, et al.. (2024). HGTDR: Advancing drug repurposing with heterogeneous graph transformers. Bioinformatics. 40(7). 20 indexed citations
4.
Abbasi, Karim, et al.. (2024). CCL-DTI: contributing the contrastive loss in drug–target interaction prediction. BMC Bioinformatics. 25(1). 48–48. 43 indexed citations
5.
Abbasi, Karim, et al.. (2024). DeepDRA: Drug repurposing using multi-omics data integration with autoencoders. PLoS ONE. 19(7). e0307649–e0307649. 18 indexed citations
6.
Rafiei, Fatemeh, Hojjat Zeraati, Karim Abbasi, et al.. (2024). CFSSynergy: Combining Feature-Based and Similarity-Based Methods for Drug Synergy Prediction. Journal of Chemical Information and Modeling. 64(7). 2577–2585. 29 indexed citations
7.
Razzaghi, Parvin, et al.. (2023). TripletMultiDTI: Multimodal representation learning in drug-target interaction prediction with triplet loss function. Expert Systems with Applications. 232. 120754–120754. 58 indexed citations
8.
Lanjanian, Hossein, et al.. (2023). A deep learning-based framework for predicting survival-associated groups in colon cancer by integrating multi-omics and clinical data. Heliyon. 9(7). e17653–e17653. 13 indexed citations
9.
Rafiei, Fatemeh, Hojjat Zeraati, Karim Abbasi, et al.. (2023). DeepTraSynergy: drug combinations using multimodal deep learning with transformers. Bioinformatics. 39(8). 57 indexed citations
10.
Abbasi, Karim, et al.. (2023). DeepCompoundNet: enhancing compound–protein interaction prediction with multimodal convolutional neural networks. Journal of Biomolecular Structure and Dynamics. 43(3). 1414–1423. 23 indexed citations
11.
Razzaghi, Parvin, et al.. (2022). Multimodal brain tumor detection using multimodal deep transfer learning. Applied Soft Computing. 129. 109631–109631. 43 indexed citations
12.
Chahooki, Mohammad Ali Zare, et al.. (2021). AutoDTI++: deep unsupervised learning for DTI prediction by autoencoders. BMC Bioinformatics. 22(1). 204–204. 27 indexed citations
13.
Abbasi, Karim. (2021). Deep Learning in Drug Target Interaction Prediction: Current and Future Perspectives. Current Medicinal Chemistry. 28(11). 2100–2113. 6 indexed citations
14.
Razzaghi, Parvin, et al.. (2021). Modality adaptation in multimodal data. Expert Systems with Applications. 179. 115126–115126. 19 indexed citations
15.
Abbasi, Karim, et al.. (2020). Deep Learning in Drug Target Interaction Prediction: Current and Future Perspectives. Current Medicinal Chemistry. 28(11). 2100–2113. 71 indexed citations
16.
Abbasi, Karim & Parvin Razzaghi. (2020). Incorporating part-whole hierarchies into fully convolutional network for scene parsing. Expert Systems with Applications. 160. 113662–113662. 7 indexed citations
17.
Abbasi, Karim, Parvin Razzaghi, Antti Poso, et al.. (2020). DeepCDA: deep cross-domain compound–protein affinity prediction through LSTM and convolutional neural networks. Bioinformatics. 36(17). 4633–4642. 179 indexed citations
18.
Razzaghi, Parvin, et al.. (2020). Learning spatial hierarchies of high-level features in deep neural network. Journal of Visual Communication and Image Representation. 70. 102817–102817. 14 indexed citations
19.
Razzaghi, Parvin, et al.. (2018). Transfer subspace learning via low-rank and discriminative reconstruction matrix. Knowledge-Based Systems. 163. 174–185. 32 indexed citations
20.
Bazrafkan, Leila, et al.. (2017). Evaluation of information literacy status among medical students at Shiraz University of Medical Sciences.. PubMed. 5(1). 42–48. 17 indexed citations

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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