Enes Makalic

12.2k total citations
64 papers, 1.2k citations indexed

About

Enes Makalic is a scholar working on Molecular Biology, Artificial Intelligence and Genetics. According to data from OpenAlex, Enes Makalic has authored 64 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Molecular Biology, 17 papers in Artificial Intelligence and 15 papers in Genetics. Recurrent topics in Enes Makalic's work include Epigenetics and DNA Methylation (18 papers), Genetic Associations and Epidemiology (11 papers) and Bayesian Methods and Mixture Models (8 papers). Enes Makalic is often cited by papers focused on Epigenetics and DNA Methylation (18 papers), Genetic Associations and Epidemiology (11 papers) and Bayesian Methods and Mixture Models (8 papers). Enes Makalic collaborates with scholars based in Australia, United Kingdom and France. Enes Makalic's co-authors include Daniel F. Schmidt, Melissa C. Southey, Graham G. Giles, John L. Hopper, Dallas R. English, Roger L. Milne, Pierre‐Antoine Dugué, Jihoon E. Joo, Ee Ming Wong and Daniel D. Buchanan and has published in prestigious journals such as PLoS ONE, American Journal of Clinical Nutrition and JNCI Journal of the National Cancer Institute.

In The Last Decade

Enes Makalic

60 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Enes Makalic Australia 18 590 154 149 143 138 64 1.2k
Daniel F. Schmidt Australia 17 462 0.8× 139 0.9× 125 0.8× 87 0.6× 129 0.9× 56 981
Eric F. Lock United States 16 710 1.2× 67 0.4× 104 0.7× 164 1.1× 99 0.7× 70 1.4k
Ståle Nygård Norway 28 784 1.3× 47 0.3× 166 1.1× 221 1.5× 200 1.4× 80 2.2k
Andreas Schuppert Germany 21 668 1.1× 30 0.2× 79 0.5× 112 0.8× 101 0.7× 82 1.6k
Prabhakar Chalise United States 18 423 0.7× 37 0.2× 109 0.7× 116 0.8× 129 0.9× 68 958
Yifan Huang United States 23 309 0.5× 149 1.0× 102 0.7× 51 0.4× 266 1.9× 75 1.2k
Sheri D. Schully United States 23 294 0.5× 47 0.3× 427 2.9× 221 1.5× 193 1.4× 55 1.4k
Christian M. Shaffer United States 22 457 0.8× 60 0.4× 328 2.2× 75 0.5× 79 0.6× 52 1.4k
Huann‐Sheng Chen United States 20 469 0.8× 43 0.3× 303 2.0× 67 0.5× 259 1.9× 51 1.4k

Countries citing papers authored by Enes Makalic

Since Specialization
Citations

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

Fields of papers citing papers by Enes Makalic

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Enes Makalic

This figure shows the co-authorship network connecting the top 25 collaborators of Enes Makalic. A scholar is included among the top collaborators of Enes Makalic 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 Enes Makalic. Enes Makalic 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.
Goudey, Benjamin, et al.. (2025). A survey on deep learning for polygenic risk scores. Briefings in Bioinformatics. 26(4). 1 indexed citations
2.
Zhang, Y., Amalia Karahalios, Aung Ko Win, et al.. (2025). A prediction model for metachronous colorectal cancer: development and validation. JNCI Journal of the National Cancer Institute. 117(10). 2082–2088.
3.
Li, Shuai, Gillian S. Dite, Robert J. MacInnis, et al.. (2024). Causation and familial confounding as explanations for the associations of polygenic risk scores with breast cancer: Evidence from innovative ICE FALCON and ICE CRISTAL analyses. Genetic Epidemiology. 48(8). 401–413. 3 indexed citations
4.
Kaestli, Mirjam, et al.. (2024). Outcomes of possible and probable rheumatic fever: A cohort study using northern Australian register data, 2013–2019. PLOS Global Public Health. 4(1). e0002064–e0002064. 2 indexed citations
5.
Zarean, Elaheh, Shuai Li, Enes Makalic, et al.. (2024). Evaluation of agreement between common clustering strategies for DNA methylation-based subtyping of breast tumours. Epigenomics. 17(2). 105–114. 2 indexed citations
6.
Zhang, Y., Aung Ko Win, Enes Makalic, et al.. (2024). Associations between pathological features and risk of metachronous colorectal cancer. International Journal of Cancer. 155(6). 1023–1032. 2 indexed citations
7.
Lai, John, Daniel F. Schmidt, Robert J. MacInnis, et al.. (2023). Using DEPendency of Association on the Number of Top Hits (DEPTH) as a Complementary Tool to Identify Novel Colorectal Cancer Susceptibility Loci. Cancer Epidemiology Biomarkers & Prevention. 32(9). 1153–1159.
8.
Dite, Gillian S., Tuong L. Nguyen, Robert J. MacInnis, et al.. (2023). Genetic and Environmental Causes of Variation in an Automated Breast Cancer Risk Factor Based on Mammographic Textures. Cancer Epidemiology Biomarkers & Prevention. 33(2). 306–313. 1 indexed citations
9.
Dugué, Pierre‐Antoine, Chenglong Yu, Allison Hodge, et al.. (2023). Methylation scores for smoking, alcohol consumption and body mass index and risk of seven types of cancer. International Journal of Cancer. 153(3). 489–498. 7 indexed citations
10.
Makalic, Enes, Lloyd Allison, & David L. Dowe. (2022). MML Inference of Single-layer Neural Networks. Figshare. 636–642.
11.
Dugué, Pierre‐Antoine, Julie K. Bassett, Ee Ming Wong, et al.. (2020). Biological Aging Measures Based on Blood DNA Methylation and Risk of Cancer: A Prospective Study. JNCI Cancer Spectrum. 5(1). 47 indexed citations
12.
Dugué, Pierre‐Antoine, Chol-Hee Jung, Jihoon E. Joo, et al.. (2019). Smoking and blood DNA methylation: an epigenome-wide association study and assessment of reversibility. Epigenetics. 15(4). 358–368. 55 indexed citations
13.
Makalic, Enes, et al.. (2018). An open-source, integrated pedigree data management and visualization tool for genetic epidemiology. International Journal of Epidemiology. 47(4). 1034–1039. 4 indexed citations
14.
Schmidt, Daniel F., et al.. (2017). Bayesian Sparse Global-Local Shrinkage Regression for Grouped Variables. arXiv (Cornell University). 1 indexed citations
15.
Dugué, Pierre‐Antoine, Julie K. Bassett, Jihoon E. Joo, et al.. (2017). DNA methylation‐based biological aging and cancer risk and survival: Pooled analysis of seven prospective studies. International Journal of Cancer. 142(8). 1611–1619. 137 indexed citations
16.
MacInnis, Robert J., Daniel F. Schmidt, Enes Makalic, et al.. (2016). Use of a Novel Nonparametric Version of DEPTH to Identify Genomic Regions Associated with Prostate Cancer Risk. Cancer Epidemiology Biomarkers & Prevention. 25(12). 1619–1624. 3 indexed citations
17.
Hopper, John L., Tuong L. Nguyen, Jennifer Stone, et al.. (2016). Childhood body mass index and adult mammographic density measures that predict breast cancer risk. Breast Cancer Research and Treatment. 156(1). 163–170. 21 indexed citations
18.
Dite, Gillian S., Enes Makalic, Daniel F. Schmidt, et al.. (2012). Tumour morphology of early-onset breast cancers predicts breast cancer risk for first-degree relatives: the Australian Breast Cancer Family Registry. Breast Cancer Research. 14(4). R122–R122. 6 indexed citations
19.
Park, Daniel J., Fabrice Odefrey, Fleur Hammet, et al.. (2011). FAN1 variants identified in multiple-case early-onset breast cancer families via exome sequencing: no evidence for association with risk for breast cancer. Breast Cancer Research and Treatment. 130(3). 1043–1049. 12 indexed citations
20.
Makalic, Enes, Ingrid Zukerman, & Michael Niemann. (2008). A spoken language interpretation component for a robot dialogue system. 195–198. 1 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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