Habiba Abdelhalim

1.4k total citations · 1 hit paper
13 papers, 683 citations indexed

About

Habiba Abdelhalim is a scholar working on Molecular Biology, Genetics and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Habiba Abdelhalim has authored 13 papers receiving a total of 683 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 6 papers in Genetics and 2 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Habiba Abdelhalim's work include Gene expression and cancer classification (5 papers), Genomics and Rare Diseases (3 papers) and Genetic Associations and Epidemiology (3 papers). Habiba Abdelhalim is often cited by papers focused on Gene expression and cancer classification (5 papers), Genomics and Rare Diseases (3 papers) and Genetic Associations and Epidemiology (3 papers). Habiba Abdelhalim collaborates with scholars based in United States, Netherlands and Finland. Habiba Abdelhalim's co-authors include Zeeshan Ahmed, Saman Zeeshan, William DeGroat, Dinesh Mendhe, Kush Patel, Bruce T. Liang, Suvi Linna-Kuosmanen, Yuichiro Arima, N A Sheth and Daniel H. Fine and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and Scientific Reports.

In The Last Decade

Habiba Abdelhalim

13 papers receiving 668 citations

Hit Papers

align trajectories

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.

Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicine

2024 457 citations William DeGroat, Habiba Abdelhalim et al. Scientific Reports profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Habiba Abdelhalim United States	7	123	82	71	61	60	13	683
Dinesh Mendhe United States	8	75 0.6×	101 1.2×	77 1.1×	64 1.0×	69 1.1×	20	777
Tony Kam‐Thong Switzerland	6	173 1.4×	111 1.4×	62 0.9×	21 0.3×	52 0.9×	10	688
Tingli Wang China	17	107 0.9×	30 0.4×	21 0.3×	37 0.6×	58 1.0×	62	764
Qiong Wei China	15	160 1.3×	74 0.9×	58 0.8×	155 2.5×	155 2.6×	34	903
Yinghong Zhang China	17	235 1.9×	28 0.3×	74 1.0×	35 0.6×	35 0.6×	99	833
Weihong Wang China	19	133 1.1×	66 0.8×	156 2.2×	99 1.6×	59 1.0×	111	1.6k
Martin Binder Germany	5	69 0.6×	190 2.3×	34 0.5×	23 0.4×	58 1.0×	10	693
Qiang Yao China	15	214 1.7×	43 0.5×	161 2.3×	44 0.7×	59 1.0×	100	1.4k
Riccardo De Bin Norway	15	169 1.4×	92 1.1×	16 0.2×	122 2.0×	43 0.7×	34	660
Yaozhong Zhang China	17	198 1.6×	106 1.3×	182 2.6×	107 1.8×	64 1.1×	70	817

Countries citing papers authored by Habiba Abdelhalim

Since Specialization

Citations

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

Fields of papers citing papers by Habiba Abdelhalim

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Habiba Abdelhalim

This figure shows the co-authorship network connecting the top 25 collaborators of Habiba Abdelhalim. A scholar is included among the top collaborators of Habiba Abdelhalim 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 Habiba Abdelhalim. Habiba Abdelhalim is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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13 of 13 papers shown

DeGroat, William, Habiba Abdelhalim, Kush Patel, et al.. (2024). Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicine. Scientific Reports. 14(1). 1–1. 457 indexed citations breakdown →

DeGroat, William, Habiba Abdelhalim, N A Sheth, et al.. (2024). Multimodal AI/ML for discovering novel biomarkers and predicting disease using multi-omics profiles of patients with cardiovascular diseases. Scientific Reports. 14(1). 26503–26503. 26 indexed citations

McGee, Z. A., Habiba Abdelhalim, William DeGroat, et al.. (2024). Computational approaches to investigate the relationship between periodontitis and cardiovascular diseases for precision medicine. Human Genomics. 18(1). 116–116. 2 indexed citations

Abdelhalim, Habiba, et al.. (2024). GWAS advancements to investigate disease associations and biological mechanisms. SHILAP Revista de lepidopterología. 4(3). 2 indexed citations

DeGroat, William, et al.. (2024). IntelliGenes: Interactive and user-friendly multimodal AI/ML application for biomarker discovery and predictive medicine. Biology Methods and Protocols. 9(1). bpae040–bpae040. 6 indexed citations

Abdelhalim, Habiba, et al.. (2023). Investigating genes associated with heart failure, atrial fibrillation, and other cardiovascular diseases, and predicting disease using machine learning techniques for translational research and precision medicine. Genomics. 115(2). 110584–110584. 25 indexed citations

Abdelhalim, Habiba, et al.. (2023). Hygieia: AI/ML pipeline integrating healthcare and genomics data to investigate genes associated with targeted disorders and predict disease. Software Impacts. 16. 100493–100493. 7 indexed citations

Abdelhalim, Habiba, et al.. (2023). Integrated ACMG-approved genes and ICD codes for the translational research and precision medicine. Database. 2023. 1 indexed citations

Abdelhalim, Habiba, et al.. (2023). Functional mutation, splice, distribution, and divergence analysis of impactful genes associated with heart failure and other cardiovascular diseases. Scientific Reports. 13(1). 16769–16769. 5 indexed citations

10.

DeGroat, William, et al.. (2023). IntelliGenes: a novel machine learning pipeline for biomarker discovery and predictive analysis using multi-genomic profiles. Bioinformatics. 39(12). 64 indexed citations

11.

Ahmed, Zeeshan, et al.. (2023). Investigating genes associated with cardiovascular disease among heart failure patients for translational research and precision medicine. SHILAP Revista de lepidopterología. 3(3). 4 indexed citations

12.

Abdelhalim, Habiba, et al.. (2023). Genomic approaches to identify and investigate genes associated with atrial fibrillation and heart failure susceptibility. Human Genomics. 17(1). 47–47. 22 indexed citations

13.

Abdelhalim, Habiba, et al.. (2022). Artificial intelligence and machine learning approaches using gene expression and variant data for personalized medicine. Briefings in Bioinformatics. 23(5). 62 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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