Farhan Quadir

512 total citations
10 papers, 137 citations indexed

About

Farhan Quadir is a scholar working on Molecular Biology, Materials Chemistry and Computational Theory and Mathematics. According to data from OpenAlex, Farhan Quadir has authored 10 papers receiving a total of 137 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Molecular Biology, 6 papers in Materials Chemistry and 3 papers in Computational Theory and Mathematics. Recurrent topics in Farhan Quadir's work include Protein Structure and Dynamics (8 papers), Enzyme Structure and Function (5 papers) and Machine Learning in Bioinformatics (3 papers). Farhan Quadir is often cited by papers focused on Protein Structure and Dynamics (8 papers), Enzyme Structure and Function (5 papers) and Machine Learning in Bioinformatics (3 papers). Farhan Quadir collaborates with scholars based in United States and Bangladesh. Farhan Quadir's co-authors include Jianlin Cheng, Raj S. Roy, Zhiye Guo, Tianqi Wu, Jian Liu, Chen Chen, Randal Halfmann, Jie Hou, Jeffrey Skolnick and Sifat Momen and has published in prestigious journals such as Bioinformatics, Scientific Reports and BMC Bioinformatics.

In The Last Decade

Farhan Quadir

10 papers receiving 137 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Farhan Quadir United States	7	120	43	34	7	6	10	137
Takahiro KUDOU Japan	4	123 1.0×	60 1.4×	16 0.5×	12 1.7×	5 0.8×	6	145
Kanaka Durga Kedarisetti Canada	3	322 2.7×	64 1.5×	51 1.5×	12 1.7×	6 1.0×	7	344
Jeffrey M. Lotthammer United States	8	199 1.7×	36 0.8×	57 1.7×	14 2.0×	14 2.3×	11	243
Tadeo E. Saldaño Argentina	6	174 1.4×	53 1.2×	48 1.4×	11 1.6×	10 1.7×	10	194
Julia Marchetti Argentina	4	99 0.8×	39 0.9×	30 0.9×	5 0.7×	3 0.5×	5	114
Fusong Ju China	7	153 1.3×	55 1.3×	39 1.1×	10 1.4×	3 0.5×	17	191
Peicong Lin China	7	92 0.8×	36 0.8×	20 0.6×	5 0.7×	3 0.5×	7	118
Victoria Ruiz‐Serra Spain	4	108 0.9×	31 0.7×	21 0.6×	9 1.3×	2 0.3×	6	117
Hanlun Jiang United States	2	160 1.3×	23 0.5×	12 0.4×	4 0.6×	3 0.5×	2	190
Yasser Mohseni Behbahani France	5	155 1.3×	34 0.8×	15 0.4×	14 2.0×	6 1.0×	8	176

Countries citing papers authored by Farhan Quadir

Since Specialization

Citations

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

Fields of papers citing papers by Farhan Quadir

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Farhan Quadir

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

All Works

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10 of 10 papers shown

Liu, Jian, Zhiye Guo, Tianqi Wu, et al.. (2023). Enhancing alphafold-multimer-based protein complex structure prediction with MULTICOM in CASP15. Communications Biology. 6(1). 1140–1140. 43 indexed citations

Gao, Mu, Bryan Piatkowski, Avinash Sreedasyam, et al.. (2023). Predicted structural proteome of Sphagnum divinum and proteome-scale annotation. Bioinformatics. 39(8). 2 indexed citations

Roy, Raj S., et al.. (2022). A deep dilated convolutional residual network for predicting interchain contacts of protein homodimers. Bioinformatics. 38(7). 1904–1910. 24 indexed citations

Guo, Zhiye, et al.. (2022). Multi-head attention-based U-Nets for predicting protein domain boundaries using 1D sequence features and 2D distance maps. BMC Bioinformatics. 23(1). 283–283. 3 indexed citations

Quadir, Farhan, et al.. (2021). DeepComplex: A Web Server of Predicting Protein Complex Structures by Deep Learning Inter-chain Contact Prediction and Distance-Based Modelling. Frontiers in Molecular Biosciences. 8. 716973–716973. 15 indexed citations

Quadir, Farhan, Raj S. Roy, Randal Halfmann, & Jianlin Cheng. (2021). DNCON2_Inter: predicting interchain contacts for homodimeric and homomultimeric protein complexes using multiple sequence alignments of monomers and deep learning. Scientific Reports. 11(1). 12295–12295. 18 indexed citations

Quadir, Farhan, et al.. (2021). Distance‐based reconstruction of protein quaternary structures from inter‐chain contacts. Proteins Structure Function and Bioinformatics. 90(3). 720–731. 9 indexed citations

Gao, Mu, Alex Morehead, Chen Chen, et al.. (2021). High-Performance Deep Learning Toolbox for Genome-Scale Prediction of Protein Structure and Function. PubMed. 2021. 46–57. 6 indexed citations

Hou, Jie, Tianqi Wu, Zhiye Guo, Farhan Quadir, & Jianlin Cheng. (2020). The MULTICOM Protein Structure Prediction Server Empowered by Deep Learning and Contact Distance Prediction. Methods in molecular biology. 2165. 13–26. 15 indexed citations

10.

Quadir, Farhan, et al.. (2014). Visualization and queuing analysis of spatio-temporal traffic data. 223–228. 2 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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