Maqsood Hayat

4.2k total citations · 1 hit paper
72 papers, 3.5k citations indexed

About

Maqsood Hayat is a scholar working on Molecular Biology, Computational Theory and Mathematics and Microbiology. According to data from OpenAlex, Maqsood Hayat has authored 72 papers receiving a total of 3.5k indexed citations (citations by other indexed papers that have themselves been cited), including 65 papers in Molecular Biology, 11 papers in Computational Theory and Mathematics and 5 papers in Microbiology. Recurrent topics in Maqsood Hayat's work include Machine Learning in Bioinformatics (61 papers), RNA and protein synthesis mechanisms (42 papers) and Genomics and Phylogenetic Studies (28 papers). Maqsood Hayat is often cited by papers focused on Machine Learning in Bioinformatics (61 papers), RNA and protein synthesis mechanisms (42 papers) and Genomics and Phylogenetic Studies (28 papers). Maqsood Hayat collaborates with scholars based in Pakistan, China and South Korea. Maqsood Hayat's co-authors include Asifullah Khan, Muhammad Tahir, Shahid Akbar, Farman Ali, Muhammad Kabir, Muhammad Iqbal, Kil To Chong, Salman Khan, Saeed Ahmad and Sher Afzal Khan and has published in prestigious journals such as Analytical Biochemistry, Scientific Reports and IEEE Access.

In The Last Decade

Maqsood Hayat

71 papers receiving 3.5k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

pACP-HybDeep: predicting anticancer peptides using binary tree growth based transformer and structural feature encoding with deep-hybrid learning

2025 29 citations Muhammad Khalil Shahid, Maqsood Hayat et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Maqsood Hayat Pakistan	36	3.0k	471	289	258	154	72	3.5k
Shahid Akbar Pakistan	26	1.2k 0.4×	278 0.6×	250 0.9×	237 0.9×	97 0.6×	40	1.8k
Yijie Ding China	37	3.4k 1.2×	1.2k 2.4×	121 0.4×	337 1.3×	94 0.6×	181	4.3k
Abdollah Dehzangi United States	32	2.6k 0.9×	472 1.0×	104 0.4×	367 1.4×	127 0.8×	102	3.5k
Farman Ali Pakistan	30	1.7k 0.6×	441 0.9×	252 0.9×	468 1.8×	109 0.7×	90	3.0k
Hao Lv China	30	2.0k 0.7×	198 0.4×	128 0.4×	117 0.5×	27 0.2×	65	2.3k
Phasit Charoenkwan Thailand	27	1.6k 0.5×	297 0.6×	263 0.9×	91 0.4×	26 0.2×	84	2.2k
Md Mehedi Hasan Japan	34	2.6k 0.9×	258 0.5×	394 1.4×	111 0.4×	20 0.1×	96	3.0k
Yaser Daanial Khan Pakistan	37	2.3k 0.8×	276 0.6×	107 0.4×	145 0.6×	41 0.3×	102	2.7k
Hui Ding China	51	7.5k 2.5×	759 1.6×	407 1.4×	224 0.9×	47 0.3×	135	8.2k
Pengmian Feng China	32	4.8k 1.6×	485 1.0×	264 0.9×	115 0.4×	22 0.1×	60	4.9k

Countries citing papers authored by Maqsood Hayat

Since Specialization

Citations

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

Fields of papers citing papers by Maqsood Hayat

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Maqsood Hayat

This figure shows the co-authorship network connecting the top 25 collaborators of Maqsood Hayat. A scholar is included among the top collaborators of Maqsood Hayat 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 Maqsood Hayat. Maqsood Hayat 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

Shahid, Muhammad Khalil, Maqsood Hayat, Shahid Akbar, et al.. (2025). pACPs-DNN: Predicting anticancer peptides using novel peptide transformation into evolutionary and structure matrix-based images with self-attention deep learning model. Computational Biology and Chemistry. 117. 108441–108441. 16 indexed citations

Tahir, Muhammad, Shahid Hussain, Sarah Gul, et al.. (2024). Unveiling the Potential Pattern Representation of RNA 5-Methyluridine Modification Sites Through a Novel Feature Fusion Model Leveraging Convolutional Neural Network and Tetranucleotide Composition. IEEE Access. 12. 10023–10035. 6 indexed citations

Ahmed, Saeed, et al.. (2023). A Novel Predictor for the Analysis and Prediction of Enhancers and Their Strength via Multi-View Features and Deep Forest. Information. 14(12). 636–636. 6 indexed citations

Ahmed, Jamal Uddin, et al.. (2020). ML-RBF: Predict protein subcellular locations in a multi-label system using evolutionary features. Chemometrics and Intelligent Laboratory Systems. 203. 104055–104055. 1 indexed citations

Arif, Muhammad, Farman Ali, Saeed Ahmad, et al.. (2019). Pred-BVP-Unb: Fast prediction of bacteriophage Virion proteins using un-biased multi-perspective properties with recursive feature elimination. Genomics. 112(2). 1565–1574. 52 indexed citations

Kabir, Muhammad, Saeed Ahmad, Muhammad Iqbal, & Maqsood Hayat. (2019). iNR-2L: A two-level sequence-based predictor developed via Chou's 5-steps rule and general PseAAC for identifying nuclear receptors and their families. Genomics. 112(1). 276–285. 59 indexed citations

Hayat, Maqsood, et al.. (2018). Predicting subcellular localization of multi-label proteins by incorporating the sequence features into Chou's PseAAC. Genomics. 111(6). 1325–1332. 68 indexed citations

Hayat, Maqsood, et al.. (2017). Bi-PSSM: Position specific scoring matrix based intelligent computational model for identification of mycobacterial membrane proteins. Journal of Theoretical Biology. 435. 116–124. 14 indexed citations

Tahir, Muhammad & Maqsood Hayat. (2017). Machine learning based identification of protein–protein interactions using derived features of physiochemical properties and evolutionary profiles. Artificial Intelligence in Medicine. 78. 61–71. 31 indexed citations

10.

Tahir, Muhammad & Maqsood Hayat. (2016). iNuc-STNC: a sequence-based predictor for identification of nucleosome positioning in genomes by extending the concept of SAAC and Chou's PseAAC. Molecular BioSystems. 12(8). 2587–2593. 92 indexed citations

11.

Iqbal, Muhammad & Maqsood Hayat. (2016). “iSS-Hyb-mRMR”: Identification of splicing sites using hybrid space of pseudo trinucleotide and pseudo tetranucleotide composition. Computer Methods and Programs in Biomedicine. 128. 1–11. 24 indexed citations

12.

Hayat, Maqsood & Muhammad Tahir. (2015). PSOFuzzySVM-TMH: identification of transmembrane helix segments using ensemble feature space by incorporated fuzzy support vector machine. Molecular BioSystems. 11(8). 2255–2262. 29 indexed citations

13.

Kabir, Muhammad & Maqsood Hayat. (2015). iRSpot-GAEnsC: identifing recombination spots via ensemble classifier and extending the concept of Chou’s PseAAC to formulate DNA samples. Molecular Genetics and Genomics. 291(1). 285–296. 125 indexed citations

14.

Ali, Farman & Maqsood Hayat. (2015). Classification of membrane protein types using Voting Feature Interval in combination with Chou׳s Pseudo Amino Acid Composition. Journal of Theoretical Biology. 384. 78–83. 144 indexed citations

15.

Ahmad, Saeed, Muhammad Kabir, & Maqsood Hayat. (2015). Identification of Heat Shock Protein families and J-protein types by incorporating Dipeptide Composition into Chou's general PseAAC. Computer Methods and Programs in Biomedicine. 122(2). 165–174. 95 indexed citations

16.

Khan, Zaheer Ullah, Maqsood Hayat, & Muazzam A. Khan. (2014). Discrimination of acidic and alkaline enzyme using Chou’s pseudo amino acid composition in conjunction with probabilistic neural network model. Journal of Theoretical Biology. 365. 197–203. 143 indexed citations

17.

Khan, Zaheer, Ashfaq Ahmad, & Maqsood Hayat. (2014). Hourly Based Climate Prediction Using Data Mining Techniquesby Comprising Entity Demean Algorithm. 6 indexed citations

18.

Hayat, Maqsood & Asifullah Khan. (2012). Mem-PHybrid: Hybrid features-based prediction system for classifying membrane protein types. Analytical Biochemistry. 424(1). 35–44. 40 indexed citations

19.

Hayat, Maqsood & Asifullah Khan. (2012). Discriminating Outer Membrane Proteins with Fuzzy K-Nearest Neighbor Algorithms Based on the General Form of Chou’s PseAAC. Protein and Peptide Letters. 19(4). 411–421. 140 indexed citations

20.

Hayat, Maqsood & Asifullah Khan. (2010). Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition. Journal of Theoretical Biology. 271(1). 10–17. 139 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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