Malik Yousef

4.9k total citations · 1 hit paper
101 papers, 3.0k citations indexed

About

Malik Yousef is a scholar working on Molecular Biology, Cancer Research and Artificial Intelligence. According to data from OpenAlex, Malik Yousef has authored 101 papers receiving a total of 3.0k indexed citations (citations by other indexed papers that have themselves been cited), including 68 papers in Molecular Biology, 30 papers in Cancer Research and 17 papers in Artificial Intelligence. Recurrent topics in Malik Yousef's work include MicroRNA in disease regulation (29 papers), Machine Learning in Bioinformatics (22 papers) and Gene expression and cancer classification (20 papers). Malik Yousef is often cited by papers focused on MicroRNA in disease regulation (29 papers), Machine Learning in Bioinformatics (22 papers) and Gene expression and cancer classification (20 papers). Malik Yousef collaborates with scholars based in Israel, Türkiye and United States. Malik Yousef's co-authors include Larry M. Manevitz, Louise C. Showe, Jens Allmer, Michael K. Showe, Burcu Bakır-Güngör, Segun Jung, Andrew V. Kossenkov, Rolf Renne, Paul M. Lieberman and Fang Lü and has published in prestigious journals such as SHILAP Revista de lepidopterología, Blood and Bioinformatics.

In The Last Decade

Malik Yousef

91 papers receiving 2.9k citations

Hit Papers

One-class svms for document classification 2002 2026 2010 2018 2002 250 500 750
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.

  1. One-class svms for document classification
    2002 843 citations Larry M. Manevitz, Malik Yousef Journal of Machine Learning Research profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Malik Yousef Israel 25 1.3k 911 717 212 198 101 3.0k
Ka‐Chun Wong Hong Kong 36 1.6k 1.2× 1.5k 1.6× 378 0.5× 217 1.0× 181 0.9× 220 4.1k
Rosalba Giugno Italy 31 1.5k 1.1× 508 0.6× 639 0.9× 522 2.5× 230 1.2× 108 2.8k
Liang Jin China 27 625 0.5× 512 0.6× 228 0.3× 121 0.6× 228 1.2× 154 3.0k
Chee Keong Kwoh Singapore 37 2.3k 1.7× 930 1.0× 256 0.4× 380 1.8× 92 0.5× 182 5.2k
Alfredo Ferro Italy 30 1.4k 1.0× 395 0.4× 784 1.1× 267 1.3× 96 0.5× 109 2.3k
Joshua W. K. Ho Australia 36 2.6k 1.9× 357 0.4× 452 0.6× 60 0.3× 108 0.5× 141 4.4k
Wenbin Liu China 27 828 0.6× 536 0.6× 375 0.5× 197 0.9× 146 0.7× 147 2.4k
Giancarlo Mauri Italy 33 2.5k 1.9× 1.0k 1.1× 382 0.5× 495 2.3× 140 0.7× 287 4.7k
Simon Kasif United States 39 5.9k 4.4× 1.1k 1.2× 384 0.5× 238 1.1× 174 0.9× 124 8.2k

Countries citing papers authored by Malik Yousef

Since Specialization
Citations

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

Fields of papers citing papers by Malik Yousef

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Malik Yousef

This figure shows the co-authorship network connecting the top 25 collaborators of Malik Yousef. A scholar is included among the top collaborators of Malik Yousef 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 Malik Yousef. Malik Yousef 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
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Bakır-Güngör, Burcu, et al.. (2025). Engineering novel features for diabetes complication prediction using synthetic electronic health records. Frontiers in Genetics. 16. 1451290–1451290.
3.
Yousef, Malik, et al.. (2025). Low Back Pain Among Health Sciences Undergraduates: Results Obtained from a Machine-Learning Analysis. Journal of Clinical Medicine. 14(6). 2046–2046.
4.
Bakır-Güngör, Burcu, et al.. (2024). CCPred: Global and population-specific colorectal cancer prediction and metagenomic biomarker identification at different molecular levels using machine learning techniques. Computers in Biology and Medicine. 182. 109098–109098. 4 indexed citations
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Ezzat, Hend A., Hanan Elhaes, Medhat Ibrahim, et al.. (2024). Molecular modeling analyses of functionalized cellulose. Scientific Reports. 14(1). 27698–27698. 6 indexed citations
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Yousef, Malik, et al.. (2023). AMP-GSM: Prediction of Antimicrobial Peptides via a Grouping–Scoring–Modeling Approach. Applied Sciences. 13(8). 5106–5106. 10 indexed citations
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Khatib, Mohammad, et al.. (2023). Compliance with COVID-19 Regulations among Palestinian Citizens of Israel in the Context of Social Norms and Gender Roles. Sustainability. 15(14). 11354–11354. 1 indexed citations
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Demirci, Müşerref Duygu Saçar, Malik Yousef, & Jens Allmer. (2019). Computational Prediction of Functional MicroRNA–mRNA Interactions. Methods in molecular biology. 1912. 175–196. 23 indexed citations
13.
Sobetzko, Patrick, et al.. (2017). Sequence-based information-theoretic features for gene essentiality prediction. BMC Bioinformatics. 18(1). 473–473. 34 indexed citations
14.
Yousef, Malik, et al.. (2010). A comparison study between one-class and two-class machine learning for MicroRNA target detection. Journal of Biomedical Science and Engineering. 3(3). 247–252. 19 indexed citations
15.
Showe, Michael K., Anil Vachani, Andrew V. Kossenkov, et al.. (2009). Gene Expression Profiles in Peripheral Blood Mononuclear Cells Can Distinguish Patients with Non–Small Cell Lung Cancer from Patients with Nonmalignant Lung Disease. Cancer Research. 69(24). 9202–9210. 125 indexed citations
16.
Raymond, Andrea, Aidan S. Hancock, Malik Yousef, et al.. (2009). Circulating Monocytes in HIV-1-Infected Viremic Subjects Exhibit an Antiapoptosis Gene Signature and Virus- and Host-Mediated Apoptosis Resistance. The Journal of Immunology. 182(7). 4459–4470. 76 indexed citations
17.
Yousef, Malik, Segun Jung, Louise C. Showe, & Michael K. Showe. (2007). Recursive Cluster Elimination (RCE) for classification and feature selection from gene expression data. BMC Bioinformatics. 8(1). 144–144. 83 indexed citations
18.
Manevitz, Larry M. & Malik Yousef. (2004). A web navigation system based on a neural network user-model trained with only positive web documents. Web Intelligence and Agent Systems An International Journal. 2(2). 137–144. 2 indexed citations
19.
Manevitz, Larry M. & Malik Yousef. (2002). One-class svms for document classification. Journal of Machine Learning Research. 2(2). 139–154. 843 indexed citations breakdown →
20.
Manevitz, Larry M. & Malik Yousef. (2000). Document Classification on Neural Networks Using Only Positive Examples. International ACM SIGIR Conference on Research and Development in Information Retrieval. 34. 304–306. 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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