Md Tamjidul Hoque

1.9k total citations
79 papers, 930 citations indexed

About

Md Tamjidul Hoque is a scholar working on Molecular Biology, Artificial Intelligence and Materials Chemistry. According to data from OpenAlex, Md Tamjidul Hoque has authored 79 papers receiving a total of 930 indexed citations (citations by other indexed papers that have themselves been cited), including 45 papers in Molecular Biology, 14 papers in Artificial Intelligence and 13 papers in Materials Chemistry. Recurrent topics in Md Tamjidul Hoque's work include Machine Learning in Bioinformatics (39 papers), Protein Structure and Dynamics (36 papers) and RNA and protein synthesis mechanisms (16 papers). Md Tamjidul Hoque is often cited by papers focused on Machine Learning in Bioinformatics (39 papers), Protein Structure and Dynamics (36 papers) and RNA and protein synthesis mechanisms (16 papers). Md Tamjidul Hoque collaborates with scholars based in United States, Australia and Bangladesh. Md Tamjidul Hoque's co-authors include Avdesh Mishra, Sumaiya Iqbal, Abdul Sattar, Madhu Chetty, Mahdi Abdelguerfi, Mahmood A. Rashid, Laurence S. Dooley, Andrew Lewis, Firas Khatib and Vicky M. Avery and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and PLoS ONE.

In The Last Decade

Md Tamjidul Hoque

69 papers receiving 914 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Md Tamjidul Hoque United States	18	573	137	107	92	51	79	930
Hanyu Zhang China	17	235 0.4×	118 0.9×	44 0.4×	147 1.6×	13 0.3×	47	699
Zhenfei Zhang China	16	150 0.3×	153 1.1×	115 1.1×	17 0.2×	161 3.2×	82	865
Tanel Pärnamaa Estonia	5	599 1.0×	228 1.7×	73 0.7×	132 1.4×	12 0.2×	8	1.2k
Merja Oja Finland	15	533 0.9×	161 1.2×	28 0.3×	26 0.3×	3 0.1×	24	931
Chengbin Peng China	19	147 0.3×	149 1.1×	28 0.3×	38 0.4×	24 0.5×	85	1.1k
Xianfang Wang China	12	180 0.3×	99 0.7×	46 0.4×	84 0.9×	34 0.7×	78	653
Giuseppe Nicosia Italy	18	473 0.8×	289 2.1×	47 0.4×	159 1.7×	20 0.4×	88	997
Lei Duan China	16	191 0.3×	225 1.6×	141 1.3×	59 0.6×	10 0.2×	99	896
Yanbu Guo China	13	240 0.4×	163 1.2×	46 0.4×	67 0.7×	24 0.5×	35	1.0k

Countries citing papers authored by Md Tamjidul Hoque

Since Specialization

Citations

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

Fields of papers citing papers by Md Tamjidul Hoque

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Md Tamjidul Hoque. 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 Md Tamjidul Hoque. The network helps show where Md Tamjidul Hoque may publish in the future.

Co-authorship network of co-authors of Md Tamjidul Hoque

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

All Works

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20 of 20 papers shown

Hoque, Md Tamjidul, et al.. (2025). Decoding Stock Trends: A Comparative Study of GRU, LSTM, and Transformer Models in Tech Sector Prediction. 13(3). 46–60.

Ferdaus, Md Meftahul, et al.. (2025). SHARP-Net: A Refined Pyramid Network for Deficiency Segmentation in Culverts and Sewer Pipes. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 18. 19223–19235.

Hoque, Md Tamjidul, et al.. (2025). Lyric-based passwords: Enhancing security and recall with AI. SHILAP Revista de lepidopterología. 3. 100108–100108.

Hoque, Md Tamjidul, et al.. (2025). Deep Learning Based Fault Detection Method in DC Motor Start. IEEE Transactions on Energy Conversion. 40(2). 1678–1681.

Hoque, Md Tamjidul, et al.. (2024). Application of Deep Learning for Segmenting Seepages in Levee Systems. Remote Sensing. 16(13). 2441–2441. 1 indexed citations

Hoque, Md Tamjidul, et al.. (2024). DisPredict3.0: Prediction of intrinsically disordered regions/proteins using protein language model. Applied Mathematics and Computation. 472. 128630–128630. 6 indexed citations

Wu, Jiande, et al.. (2023). PCa-Clf: A Classifier of Prostate Cancer Patients into Patients with Indolent and Aggressive Tumors Using Machine Learning. SHILAP Revista de lepidopterología. 5(4). 1302–1319.

Hoque, Md Tamjidul, et al.. (2023). AGRN: accurate gene regulatory network inference using ensemble machine learning methods. Bioinformatics Advances. 3(1). 12 indexed citations

Hoque, Md Tamjidul, et al.. (2021). Machine Learning based Restaurant Sales Forecasting. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations

10.

Mishra, Avdesh, et al.. (2021). diSBPred: A machine learning based approach for disulfide bond prediction. Computational Biology and Chemistry. 91. 107436–107436. 10 indexed citations

11.

Hoque, Md Tamjidul, et al.. (2020). Flight Data of Airplane for Wind Forecasting. 1853–1856.

12.

Ahmed, Md. Toukir, et al.. (2018). RBSURFpred: Modeling protein accessible surface area in real and binary space using regularized and optimized regression. Journal of Theoretical Biology. 441. 44–57. 7 indexed citations

13.

Iqbal, Sumaiya & Md Tamjidul Hoque. (2018). PBRpredict-Suite: a suite of models to predict peptide-recognition domain residues from protein sequence. Bioinformatics. 34(19). 3289–3299. 36 indexed citations

14.

Walsh, Kenneth R., Md Tamjidul Hoque, & Kim Williams. (2017). Human Machine Learning Symbiosis.. 13(1). 55–62. 6 indexed citations

15.

Hoque, Md Tamjidul, Yuedong Yang, Avdesh Mishra, & Yaoqi Zhou. (2016). sDFIRE: Sequence‐specific statistical energy function for protein structure prediction by decoy selections. Journal of Computational Chemistry. 37(12). 1119–1124. 18 indexed citations

16.

Rashid, Mahmood A., Swakkhar Shatabda, M. A. Hakim Newton, Md Tamjidul Hoque, & Abdul Sattar. (2014). A Parallel Framework for Multipoint Spiral Search in ab Initio Protein Structure Prediction. SHILAP Revista de lepidopterología. 2014. 1–17. 2 indexed citations

17.

Rashid, Mahmood A., M. A. Hakim Newton, Md Tamjidul Hoque, & Abdul Sattar. (2013). Collaborative Parallel Local Search for Simplified Protein Structure Prediction. Victoria University Research Repository (Victoria University). 966–973. 3 indexed citations

18.

Stantić, Bela, et al.. (2008). Optimal length of fragments for use in protein structure prediction. Griffith Research Online (Griffith University, Queensland, Australia). 156–161. 1 indexed citations

19.

Hoque, Md Tamjidul, Madhu Chetty, & Laurence S. Dooley. (2006). A Guided Genetic Algorithm for Protein Folding Prediction Using 3D Hydrophobic-Hydrophilic Model. Griffith Research Online (Griffith University, Queensland, Australia). 48. 2339–2346. 17 indexed citations

20.

Hoque, Md Tamjidul, Madhu Chetty, & Laurence S. Dooley. (2005). Fast computation of the fitness function for protein folding prediction in a 2D hydrophilic-hydrophobic model. International Journal of Simulation Systems Science & Technology. 6. 27–37. 1 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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