Siow‐Wee Chang

1.2k total citations
28 papers, 775 citations indexed

About

Siow‐Wee Chang is a scholar working on Molecular Biology, Cancer Research and Biomedical Engineering. According to data from OpenAlex, Siow‐Wee Chang has authored 28 papers receiving a total of 775 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 5 papers in Cancer Research and 4 papers in Biomedical Engineering. Recurrent topics in Siow‐Wee Chang's work include Cancer-related molecular mechanisms research (4 papers), Gene expression and cancer classification (4 papers) and Biosensors and Analytical Detection (3 papers). Siow‐Wee Chang is often cited by papers focused on Cancer-related molecular mechanisms research (4 papers), Gene expression and cancer classification (4 papers) and Biosensors and Analytical Detection (3 papers). Siow‐Wee Chang collaborates with scholars based in Malaysia, China and United States. Siow‐Wee Chang's co-authors include Hwa Jen Yap, Sameem Abdul Kareem, Rosnah Binti Zain, Kien‐Thai Yong, Amir Feisal Merican, T. Malathi Thevarajah, Sook Mei Khor, Keem Siah Yap, Shen Yuong Wong and Shing Chiang Tan and has published in prestigious journals such as PLoS ONE, Biosensors and Bioelectronics and Sensors and Actuators B Chemical.

In The Last Decade

Siow‐Wee Chang

28 papers receiving 729 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Siow‐Wee Chang Malaysia 15 199 176 126 97 83 28 775
Vimal K. Shrivastava India 19 433 2.2× 238 1.4× 38 0.3× 215 2.2× 206 2.5× 41 1.4k
Happy Nkanta Monday China 14 97 0.5× 252 1.4× 31 0.2× 279 2.9× 49 0.6× 49 606
Jie Xue China 16 135 0.7× 147 0.8× 144 1.1× 239 2.5× 45 0.5× 49 753
Sarinder Kaur Dhillon Malaysia 11 66 0.3× 202 1.1× 130 1.0× 125 1.3× 28 0.3× 30 539
Jatinder Manhas India 12 66 0.3× 186 1.1× 50 0.4× 103 1.1× 22 0.3× 32 475
Hasan Abbas United Kingdom 17 64 0.3× 88 0.5× 137 1.1× 22 0.2× 24 0.3× 92 819
Anushikha Singh India 13 136 0.7× 50 0.3× 42 0.3× 347 3.6× 100 1.2× 63 836
Shagun Sharma India 16 314 1.6× 281 1.6× 40 0.3× 250 2.6× 99 1.2× 121 1.2k
Hasan Erbay Türkiye 14 177 0.9× 194 1.1× 14 0.1× 86 0.9× 71 0.9× 56 704
Dalia Ezzat Egypt 6 108 0.5× 195 1.1× 49 0.4× 141 1.5× 42 0.5× 8 462

Countries citing papers authored by Siow‐Wee Chang

Since Specialization
Citations

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

Fields of papers citing papers by Siow‐Wee Chang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Siow‐Wee Chang. 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 Siow‐Wee Chang. The network helps show where Siow‐Wee Chang may publish in the future.

Co-authorship network of co-authors of Siow‐Wee Chang

This figure shows the co-authorship network connecting the top 25 collaborators of Siow‐Wee Chang. A scholar is included among the top collaborators of Siow‐Wee Chang 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 Siow‐Wee Chang. Siow‐Wee Chang 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.
Toe, Teoh Teik, et al.. (2025). Drug repurposing using artificial intelligence, molecular docking, and hybrid approaches: A comprehensive review in general diseases vs Alzheimer's disease. Computer Methods and Programs in Biomedicine. 261. 108604–108604. 2 indexed citations
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Thevarajah, T. Malathi, et al.. (2023). A biomarker discovery of acute myocardial infarction using feature selection and machine learning. Medical & Biological Engineering & Computing. 61(10). 2527–2541. 17 indexed citations
7.
Looi, Lai‐Meng, et al.. (2023). Epithelial-mesenchymal transition profiles in triple negative breast carcinoma may explain its aggressive nature.. PubMed. 45(3). 363–374. 1 indexed citations
8.
Thevarajah, T. Malathi, et al.. (2021). A review of risk prediction models in cardiovascular disease: conventional approach vs. artificial intelligent approach. Computer Methods and Programs in Biomedicine. 207. 106190–106190. 60 indexed citations
9.
Loh, Kar‐Hoe, et al.. (2021). Cephalopod species identification using integrated analysis of machine learning and deep learning approaches. PeerJ. 9. e11825–e11825. 9 indexed citations
10.
Cheah, Phaik‐Leng, et al.. (2021). A review on omics-based biomarkers discovery for Alzheimer's disease from the bioinformatics perspectives: Statistical approach vs machine learning approach. Computers in Biology and Medicine. 139. 104947–104947. 47 indexed citations
11.
Thevarajah, T. Malathi, et al.. (2020). Biosensing based on surface-enhanced Raman spectroscopy as an emerging/next-generation point-of-care approach for acute myocardial infarction diagnosis. Critical Reviews in Biotechnology. 40(8). 1191–1209. 23 indexed citations
12.
Chang, Siow‐Wee, et al.. (2020). Integrated analysis of machine learning and deep learning in chili pest and disease identification. Journal of the Science of Food and Agriculture. 101(9). 3582–3594. 54 indexed citations
13.
Musa, Rabiu Muazu, Anwar P. P. Abdul Majeed, Zahari Taha, et al.. (2019). A machine learning approach of predicting high potential archers by means of physical fitness indicators. PLoS ONE. 14(1). e0209638–e0209638. 33 indexed citations
14.
Cheah, Phaik‐Leng, et al.. (2019). Screening for microsatellite instability in colorectal carcinoma: Practical utility of immunohistochemistry and PCR with fragment analysis in a diagnostic histopathology setting.. PubMed. 41(2). 91–100. 28 indexed citations
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Chang, Siow‐Wee, et al.. (2018). Integrative machine learning analysis of multiple gene expression profiles in cervical cancer. PeerJ. 6. e5285–e5285. 26 indexed citations
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Chang, Siow‐Wee, et al.. (2018). Plant data visualisation using network graphs. PeerJ. 6. e5579–e5579. 1 indexed citations
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Chang, Siow‐Wee, Sameem Abdul Kareem, Amir Feisal Merican, & Rosnah Binti Zain. (2014). A Hybrid Prognostic Model for Oral Cancer based on Clinicopathologic and Genomic Markers. Sains Malaysiana. 43(4). 2 indexed citations
18.
Yap, Hwa Jen, Zahari Taha, Siti Zawiah Md Dawal, & Siow‐Wee Chang. (2014). Virtual Reality Based Support System for Layout Planning and Programming of an Industrial Robotic Work Cell. PLoS ONE. 9(10). e109692–e109692. 22 indexed citations
19.
Chang, Siow‐Wee, Sameem Abdul Kareem, Amir Feisal Merican, & Rosnah Binti Zain. (2013). Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods. BMC Bioinformatics. 14(1). 170–170. 114 indexed citations
20.
Chang, Siow‐Wee, Sameem Abdul Kareem, Thomas George Kallarakkal, et al.. (2011). Feature selection methods for optimizing clinicopathologic input variables in oral cancer prognosis.. PubMed. 12(10). 2659–64. 15 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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