Elnaz Pashaei

961 total citations
30 papers, 741 citations indexed

About

Elnaz Pashaei is a scholar working on Molecular Biology, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Elnaz Pashaei has authored 30 papers receiving a total of 741 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Molecular Biology, 13 papers in Artificial Intelligence and 6 papers in Computer Vision and Pattern Recognition. Recurrent topics in Elnaz Pashaei's work include Gene expression and cancer classification (14 papers), Machine Learning in Bioinformatics (11 papers) and Metaheuristic Optimization Algorithms Research (9 papers). Elnaz Pashaei is often cited by papers focused on Gene expression and cancer classification (14 papers), Machine Learning in Bioinformatics (11 papers) and Metaheuristic Optimization Algorithms Research (9 papers). Elnaz Pashaei collaborates with scholars based in Türkiye, United States and Iran. Elnaz Pashaei's co-authors include Elham Pashaei, Nizamettin Aydın, Mustafa Özen, Esra Güzel, Seyedsaeid Mirkamali, Ali Asghar Rahmani Hosseinabadi, Seyedali Mirjalili, Adam Słowik, Michael K. Wendt and Xuehong Deng and has published in prestigious journals such as Nucleic Acids Research, PLoS ONE and Analytical Biochemistry.

In The Last Decade

Elnaz Pashaei

30 papers receiving 720 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Elnaz Pashaei Türkiye 13 386 290 112 100 69 30 741
Elham Pashaei Türkiye 14 241 0.6× 243 0.8× 76 0.7× 82 0.8× 43 0.6× 27 603
Bilal Mirza Singapore 10 430 1.1× 202 0.7× 103 0.9× 45 0.5× 24 0.3× 19 751
Andri Mirzal Malaysia 7 243 0.6× 235 0.8× 178 1.6× 28 0.3× 25 0.4× 23 653
Hua Chai China 14 296 0.8× 242 0.8× 139 1.2× 116 1.2× 22 0.3× 30 641
Andrea Bommert Germany 4 251 0.7× 121 0.4× 97 0.9× 18 0.2× 32 0.5× 8 583
Marek Krętowski Poland 15 190 0.5× 111 0.4× 83 0.7× 29 0.3× 37 0.5× 64 654
Alok Kumar Shukla India 18 583 1.5× 359 1.2× 126 1.1× 18 0.2× 92 1.3× 49 917
Yuhai Zhao China 16 421 1.1× 134 0.5× 152 1.4× 35 0.3× 95 1.4× 95 790
Yu‐Da Lin Taiwan 18 159 0.4× 405 1.4× 40 0.4× 71 0.7× 18 0.3× 66 796

Countries citing papers authored by Elnaz Pashaei

Since Specialization
Citations

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

Fields of papers citing papers by Elnaz Pashaei

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Elnaz Pashaei

This figure shows the co-authorship network connecting the top 25 collaborators of Elnaz Pashaei. A scholar is included among the top collaborators of Elnaz Pashaei 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 Elnaz Pashaei. Elnaz Pashaei 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.
Pashaei, Elnaz, et al.. (2025). Binary hiking optimization for gene selection: Insights from HNSCC RNA-Seq data. Expert Systems with Applications. 268. 126404–126404. 4 indexed citations
2.
Pashaei, Elnaz, et al.. (2025). Biomarker Identification for Alzheimer’s Disease Using a Multi-Filter Gene Selection Approach. International Journal of Molecular Sciences. 26(5). 1816–1816. 3 indexed citations
3.
Pashaei, Elnaz, Sheng Liu, Kailing Li, et al.. (2025). DiCE: differential centrality-ensemble analysis based on gene expression profiles and protein–protein interaction network. Nucleic Acids Research. 53(13). 1 indexed citations
4.
Liu, Sheng, Ziyu Zeng, Xuehong Deng, et al.. (2024). CDHu40: a novel marker gene set of neuroendocrine prostate cancer. Briefings in Bioinformatics. 25(6). 2 indexed citations
5.
Pashaei, Elnaz, et al.. (2024). An efficient high-dimensional gene selection approach based on the Binary Horse Herd Optimization Algorithm for biologicaldata classification. Iran Journal of Computer Science. 7(2). 279–309. 9 indexed citations
6.
Pashaei, Elnaz. (2023). An Efficient Binary Sand Cat Swarm Optimization for Feature Selection in High-Dimensional Biomedical Data. Bioengineering. 10(10). 1123–1123. 8 indexed citations
7.
8.
Pashaei, Elnaz, et al.. (2022). Hybrid Hypercube Optimization Search Algorithm and Multilayer Perceptron Neural Network for Medical Data Classification. Computational Intelligence and Neuroscience. 2022. 1–16. 8 indexed citations
9.
Pashaei, Elham & Elnaz Pashaei. (2022). Hybrid binary arithmetic optimization algorithm with simulated annealing for feature selection in high-dimensional biomedical data. The Journal of Supercomputing. 78(13). 15598–15637. 33 indexed citations
10.
Pashaei, Elnaz & Elham Pashaei. (2022). A fusion approach based on black hole algorithm and particle swarm optimization for image enhancement. Multimedia Tools and Applications. 82(1). 297–325. 12 indexed citations
11.
Pashaei, Elnaz & Elham Pashaei. (2022). An efficient binary chimp optimization algorithm for feature selection in biomedical data classification. Neural Computing and Applications. 34(8). 6427–6451. 91 indexed citations
12.
Pashaei, Elnaz & Elham Pashaei. (2021). Gene selection using hybrid dragonfly black hole algorithm: A case study on RNA-seq COVID-19 data. Analytical Biochemistry. 627. 114242–114242. 31 indexed citations
13.
Pashaei, Elham & Elnaz Pashaei. (2021). Training Feedforward Neural Network Using Enhanced Black Hole Algorithm: A Case Study on COVID-19 Related ACE2 Gene Expression Classification. Arabian Journal for Science and Engineering. 46(4). 3807–3828. 21 indexed citations
14.
Pashaei, Elnaz, et al.. (2021). Application of Horse Herd Optimization Algorithm for medical problems. 6. 1–6. 6 indexed citations
15.
Pashaei, Elnaz, Elham Pashaei, & Nizamettin Aydın. (2018). Gene selection using hybrid binary black hole algorithm and modified binary particle swarm optimization. Genomics. 111(4). 669–686. 70 indexed citations
16.
Pashaei, Elnaz, et al.. (2017). Meta-analysis of miRNA expression profiles for prostate cancer recurrence following radical prostatectomy. PLoS ONE. 12(6). e0179543–e0179543. 101 indexed citations
17.
Pashaei, Elnaz, et al.. (2016). A Meta-Analysis: Identification of Common Mir-145 Target Genes that have Similar Behavior in Different GEO Datasets. PLoS ONE. 11(9). e0161491–e0161491. 18 indexed citations
18.
Pashaei, Elnaz, Mustafa Özen, & Nizamettin Aydın. (2016). Biomarker discovery based on BBHA and AdaboostM1 on microarray data for cancer classification. PubMed. 2016. 3080–3083. 9 indexed citations
19.
Pashaei, Elnaz, Mustafa Özen, & Nizamettin Aydın. (2016). Gene selection and classification approach for microarray data based on Random Forest Ranking and BBHA. 308–311. 25 indexed citations
20.
Pashaei, Elnaz, Mustafa Özen, & Nizamettin Aydın. (2015). An application of black hole algorithm and decision tree for medical problem. 3. 1–6. 12 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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