Madhu Singh

3.3k total citations · 1 hit paper
74 papers, 1.6k citations indexed

About

Madhu Singh is a scholar working on Education, Molecular Biology and Oncology. According to data from OpenAlex, Madhu Singh has authored 74 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Education, 11 papers in Molecular Biology and 11 papers in Oncology. Recurrent topics in Madhu Singh's work include Higher Education Learning Practices (9 papers), Education Systems and Policy (9 papers) and Genomics, phytochemicals, and oxidative stress (7 papers). Madhu Singh is often cited by papers focused on Higher Education Learning Practices (9 papers), Education Systems and Policy (9 papers) and Genomics, phytochemicals, and oxidative stress (7 papers). Madhu Singh collaborates with scholars based in India, United States and Australia. Madhu Singh's co-authors include Devendra Parmar, Michael Michael, Parag P. Shah, Mohan C. Pant, Peter Gibbs, Isaac Kinde, Jeanne Tie, Christos S. Karapetis, Rachel Wong and Bert Vogelstein and has published in prestigious journals such as Journal of Clinical Oncology, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

Madhu Singh

64 papers receiving 1.5k citations

Hit Papers

Circulating tumor DNA as an early marker of therapeutic r... 2015 2026 2018 2022 2015 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Madhu Singh India 19 527 521 425 269 243 74 1.6k
Mohamad Y. Fares United States 18 452 0.9× 865 1.7× 665 1.6× 92 0.3× 258 1.1× 106 2.9k
Hussein H. Khachfe Lebanon 16 463 0.9× 883 1.7× 731 1.7× 89 0.3× 289 1.2× 49 2.4k
Hamza A. Salhab Lebanon 13 450 0.9× 859 1.6× 635 1.5× 88 0.3× 230 0.9× 28 2.2k
Wenjing Wang China 27 225 0.4× 577 1.1× 475 1.1× 128 0.5× 563 2.3× 119 1.9k
Sheng‐Fang Su Taiwan 23 245 0.5× 782 1.5× 341 0.8× 237 0.9× 185 0.8× 56 2.2k
Michael D. Thompson United States 31 161 0.3× 1.3k 2.5× 413 1.0× 125 0.5× 117 0.5× 84 2.9k
Kyung Mee Kim South Korea 24 225 0.4× 729 1.4× 305 0.7× 341 1.3× 381 1.6× 88 2.1k
Jibin Li China 31 293 0.6× 518 1.0× 939 2.2× 313 1.2× 435 1.8× 197 3.2k
Fiona Murray United States 31 195 0.4× 1.8k 3.5× 302 0.7× 445 1.7× 479 2.0× 97 3.7k
Guoliang Huang China 30 862 1.6× 1.2k 2.2× 286 0.7× 139 0.5× 120 0.5× 104 2.7k

Countries citing papers authored by Madhu Singh

Since Specialization
Citations

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

Fields of papers citing papers by Madhu Singh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Madhu Singh

This figure shows the co-authorship network connecting the top 25 collaborators of Madhu Singh. A scholar is included among the top collaborators of Madhu Singh 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 Madhu Singh. Madhu Singh 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.
Yu, An, Madhu Singh, Aditya Agarwal, et al.. (2025). Integrating manual preprocessing with automated feature extraction for improved rodent seizure classification. Epilepsy & Behavior. 165. 110306–110306. 2 indexed citations
2.
Jain, Ankit, et al.. (2025). Understanding TikTok’s Role in Young-Onset Colorectal Cancer Awareness and Education. Journal of Cancer Education. 40(6). 859–864. 1 indexed citations
3.
Singh, Madhu, Solenn Toupin, Guillaume Cayla, et al.. (2024). Sex-specific models to predict 5-year mortality after ST-elevation myocardial infarction using machine learning: insight from FAST-MI registry. European Heart Journal. 45(Supplement_1).
4.
Mileshkin, Linda, Richard W. Tothill, David D.L. Bowtell, et al.. (2022). Uncertainty and the unmet informational needs of patients with cancer of unknown primary (CUP): a cross-sectional multi-site study. Supportive Care in Cancer. 30(10). 8217–8229. 4 indexed citations
6.
Singh, Madhu, et al.. (2021). Prevalence and Determinants of Obesity/overweight and Undernutrition Among School Going Adolescents 10 to 17 years in Rural Area of South India. SHILAP Revista de lepidopterología. 2 indexed citations
7.
Singh, Madhu, et al.. (2021). A Rare Location of a Repeat Ectopic Pregnancy: A Case Report. Cureus. 13(6). e15982–e15982. 2 indexed citations
8.
Jain, Ayushi, et al.. (2021). A Rare Cause of Apparent Anuria After Caesarean Section: A Case Report. Cureus. 13(4). e14400–e14400. 1 indexed citations
9.
Gupta, Sunil, Truyen Tran, Wei Luo, et al.. (2014). Machine-learning prediction of cancer survival: a retrospective study using electronic administrative records and a cancer registry. BMJ Open. 4(3). e004007–e004007. 83 indexed citations
10.
Tie, Jeanne, Isaac Kinde, Hui‐Li Wong, et al.. (2013). Massively parallel sequencing (MPS) of circulating DNA in patients with metastatic colorectal cancer (mCRC): Prognostic significance and early changes during chemotherapy (CT).. Journal of Clinical Oncology. 31(15_suppl). 11015–11015. 1 indexed citations
11.
Singh, Madhu & Dileep Kumar Singh. (2013). Endosulfan induced alteration in bacterial protein profile and RNA yield of Klebsiella sp. M3, Achromobacter sp. M6, and Rhodococcus sp. M2. Journal of Hazardous Materials. 265. 233–241. 7 indexed citations
12.
Singh, Madhu & Dileep Kumar Singh. (2013). Biodegradation of Endosulfan in Broth Medium and in Soil Microcosm by Klebsiella sp. M3. Bulletin of Environmental Contamination and Toxicology. 92(2). 237–242. 19 indexed citations
13.
Kamal, Saurabh, et al.. (2012). Thyroid Associated Ophthalmopathy. Delhi Journal of Ophthalmology. 22(4). 249–255.
14.
Ruwali, Munindra, Madhu Singh, Mohan C. Pant, & Devendra Parmar. (2011). Polymorphism in glutathione S-transferases: Susceptibility and treatment outcome for head and neck cancer. Xenobiotica. 41(12). 1122–1130. 27 indexed citations
15.
Singh, Madhu, et al.. (2010). Unequal Moving to Being Equal: Impact of No Child Left Behind in the Mississippi Delta. The Journal of Negro Education. 79(1). 18–32.
16.
Singh, Madhu, et al.. (2010). A case of papillary growth from the areola. Journal of Cutaneous and Aesthetic Surgery. 3(2). 122–122.
17.
Singh, Madhu, Prudence A. Francis, & Michael Michael. (2010). Tamoxifen, cytochrome P450 genes and breast cancer clinical outcomes. The Breast. 20(2). 111–118. 68 indexed citations
18.
Singh, Madhu, Anwar Jamal Khan, Parag P. Shah, et al.. (2008). Polymorphism in environment responsive genes and association with Parkinson disease. Molecular and Cellular Biochemistry. 312(1-2). 131–138. 72 indexed citations
19.
Singh, Madhu, Parag P. Shah, Arvind Pratap Singh, et al.. (2007). Association of genetic polymorphisms in glutathione S-transferases and susceptibility to head and neck cancer. Mutation research. Fundamental and molecular mechanisms of mutagenesis. 638(1-2). 184–194. 76 indexed citations
20.
Singh, Madhu. (2002). Institutionalising learning lifelong : creating conducive environments for adult learning in the Asian context. 3 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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