Hala Alshamlan

783 total citations
23 papers, 522 citations indexed

About

Hala Alshamlan is a scholar working on Molecular Biology, Artificial Intelligence and Health Information Management. According to data from OpenAlex, Hala Alshamlan has authored 23 papers receiving a total of 522 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Molecular Biology, 9 papers in Artificial Intelligence and 2 papers in Health Information Management. Recurrent topics in Hala Alshamlan's work include Gene expression and cancer classification (14 papers), Machine Learning in Bioinformatics (11 papers) and Bioinformatics and Genomic Networks (9 papers). Hala Alshamlan is often cited by papers focused on Gene expression and cancer classification (14 papers), Machine Learning in Bioinformatics (11 papers) and Bioinformatics and Genomic Networks (9 papers). Hala Alshamlan collaborates with scholars based in Saudi Arabia, Canada and Egypt. Hala Alshamlan's co-authors include Yousef Al-Ohali, Ghada Badr, Ali El‐Zaart and Samar Omar and has published in prestigious journals such as International Journal of Molecular Sciences, IEEE Access and Sensors.

In The Last Decade

Hala Alshamlan

21 papers receiving 491 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Hala Alshamlan Saudi Arabia 9 325 296 88 35 24 23 522
Ghada Badr Canada 7 287 0.9× 251 0.8× 67 0.8× 16 0.5× 20 0.8× 19 458
Shemim Begum India 7 136 0.4× 188 0.6× 96 1.1× 15 0.4× 24 1.0× 11 306
Pedro H. Bugatti Brazil 12 84 0.3× 81 0.3× 137 1.6× 34 1.0× 17 0.7× 36 335
Santos Kumar Baliarsingh India 9 89 0.3× 178 0.6× 63 0.7× 20 0.6× 27 1.1× 26 303
Patryk Orzechowski United States 7 117 0.4× 275 0.9× 33 0.4× 16 0.5× 31 1.3× 32 416
Haixia Long China 9 71 0.2× 74 0.3× 49 0.6× 66 1.9× 15 0.6× 24 260
Xuequn Shang China 10 225 0.7× 130 0.4× 28 0.3× 33 0.9× 29 1.2× 51 398
Tapas Bhadra India 10 150 0.5× 139 0.5× 80 0.9× 9 0.3× 36 1.5× 21 339
C. Gunavathi India 9 93 0.3× 110 0.4× 26 0.3× 40 1.1× 15 0.6× 29 282
Nicoletta Dessì Italy 8 114 0.4× 132 0.4× 62 0.7× 7 0.2× 17 0.7× 30 290

Countries citing papers authored by Hala Alshamlan

Since Specialization
Citations

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

Fields of papers citing papers by Hala Alshamlan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hala Alshamlan

This figure shows the co-authorship network connecting the top 25 collaborators of Hala Alshamlan. A scholar is included among the top collaborators of Hala Alshamlan 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 Hala Alshamlan. Hala Alshamlan 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.
Alshamlan, Hala, et al.. (2025). Performance Evaluation of Hybrid Bio-Inspired and Deep Learning Algorithms in Gene Selection and Cancer Classification. IEEE Access. 13. 59977–59990. 2 indexed citations
2.
Alshamlan, Hala, et al.. (2024). Improving Alzheimer’s Disease Prediction with Different Machine Learning Approaches and Feature Selection Techniques. Diagnostics. 14(19). 2237–2237. 8 indexed citations
3.
Alshamlan, Hala, et al.. (2024). Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection. Computers, materials & continua/Computers, materials & continua (Print). 79(1). 675–694. 7 indexed citations
4.
Alshamlan, Hala, et al.. (2023). Identifying Effective Feature Selection Methods for Alzheimer’s Disease Biomarker Gene Detection Using Machine Learning. Diagnostics. 13(10). 1771–1771. 11 indexed citations
5.
6.
Alshamlan, Hala, et al.. (2023). An Interactive Augmented and Virtual Reality System for Managing Dental Anxiety among Young Patients: A Pilot Study. Applied Sciences. 13(9). 5603–5603. 7 indexed citations
7.
Alshamlan, Hala, et al.. (2023). Personalized Car Recommendations Using Knowledge-Based Methods. 539–544. 3 indexed citations
8.
Alshamlan, Hala, et al.. (2022). A New Algorithm for Cancer Biomarker Gene Detection Using Harris Hawks Optimization. Sensors. 22(19). 7273–7273. 4 indexed citations
9.
Alshamlan, Hala, et al.. (2021). Intelligent Multiclass Skin Cancer Detection Using Convolution Neural Networks. Computers, materials & continua/Computers, materials & continua (Print). 69(1). 831–847. 4 indexed citations
10.
Alshamlan, Hala. (2021). An Effective Filter Method Towards the Performance Improvement of FF-SVM Algorithm. IEEE Access. 9. 140835–140840. 2 indexed citations
11.
Alshamlan, Hala, et al.. (2020). A Gene Prediction Function for Type 2 Diabetes Mellitus using Logistic Regression. 1–4. 6 indexed citations
12.
Alshamlan, Hala. (2018). DQB: A novel dynamic quantitive classification model using artificial bee colony algorithm with application on gene expression profiles. Saudi Journal of Biological Sciences. 25(5). 932–946. 6 indexed citations
13.
Alshamlan, Hala. (2018). Co-ABC: Correlation artificial bee colony algorithm for biomarker gene discovery using gene expression profile. Saudi Journal of Biological Sciences. 25(5). 895–903. 30 indexed citations
14.
Alshamlan, Hala, Ghada Badr, & Yousef Al-Ohali. (2016). ABC-SVM: Artificial Bee Colony and SVM Method for Microarray Gene Selection and Multi Class Cancer Classification. International Journal of Machine Learning and Computing. 6(3). 184–190. 40 indexed citations
15.
Alshamlan, Hala, Ghada Badr, & Yousef Al-Ohali. (2015). mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling. BioMed Research International. 2015. 1–15. 155 indexed citations
16.
Alshamlan, Hala, Ghada Badr, & Yousef Al-Ohali. (2015). Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification. Computational Biology and Chemistry. 56. 49–60. 164 indexed citations
17.
Alshamlan, Hala, Ghada Badr, & Yousef Al-Ohali. (2014). A Review of Effective Gene Selection Methods for Cancer Classification Using Microarray Gene Expression Profile. 1(6). 2 indexed citations
18.
Alshamlan, Hala, Ghada Badr, & Yousef Al-Ohali. (2014). The Performance of Bio-Inspired Evolutionary Gene Selection Methods for Cancer Classification Using Microarray Dataset. International Journal of Bioscience Biochemistry and Bioinformatics. 4(3). 166–170. 15 indexed citations
19.
Alshamlan, Hala, et al.. (2012). Solving Shortest Hamiltonion Path Problem Using DNA Computing. Journal of Proteomics & Bioinformatics. 76–82.
20.
Alshamlan, Hala & Ali El‐Zaart. (2010). Feature extraction values for breast cancer mammography images. 335–340. 30 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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