Aisha AlJanahi

1.2k total citations
8 papers, 286 citations indexed

About

Aisha AlJanahi is a scholar working on Molecular Biology, Ecology and Hematology. According to data from OpenAlex, Aisha AlJanahi has authored 8 papers receiving a total of 286 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 2 papers in Ecology and 2 papers in Hematology. Recurrent topics in Aisha AlJanahi's work include CRISPR and Genetic Engineering (3 papers), Acute Myeloid Leukemia Research (2 papers) and Bacteriophages and microbial interactions (2 papers). Aisha AlJanahi is often cited by papers focused on CRISPR and Genetic Engineering (3 papers), Acute Myeloid Leukemia Research (2 papers) and Bacteriophages and microbial interactions (2 papers). Aisha AlJanahi collaborates with scholars based in United States, South Korea and Belgium. Aisha AlJanahi's co-authors include Arifa S. Khan, Subhiksha Nandakumar, Norman Goodacre, Mike Mikailov, Cynthia E. Dunbar, Mark Danielsen, Minh Quang Nguyen, Hans Jürgen Solinski, Olivier M. Vandeputte and Robert L. Charlebois and has published in prestigious journals such as Blood, Cell stem cell and Cell Reports.

In The Last Decade

Aisha AlJanahi

8 papers receiving 284 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Aisha AlJanahi United States	5	161	70	48	46	34	8	286
Claire L. Hill United Kingdom	7	250 1.6×	42 0.6×	68 1.4×	75 1.6×	50 1.5×	7	443
Harri Jäälinoja Finland	6	159 1.0×	129 1.8×	41 0.9×	57 1.2×	21 0.6×	7	337
Tamar Kahan Israel	12	497 3.1×	30 0.4×	79 1.6×	28 0.6×	25 0.7×	14	663
Hans‐Dieter Liebig Austria	10	243 1.5×	95 1.4×	24 0.5×	49 1.1×	31 0.9×	12	344
Haiying Grunenwald United States	8	208 1.3×	48 0.7×	27 0.6×	19 0.4×	26 0.8×	10	290
Daiki Matsuda United States	12	361 2.2×	35 0.5×	191 4.0×	26 0.6×	34 1.0×	20	559
Jennifer F. Pinello United States	7	149 0.9×	24 0.3×	55 1.1×	20 0.4×	31 0.9×	8	265
Igor Stevanovski Australia	9	150 0.9×	58 0.8×	19 0.4×	128 2.8×	57 1.7×	17	318
Warren Emmett United Kingdom	12	485 3.0×	57 0.8×	58 1.2×	166 3.6×	51 1.5×	12	747
Alexandra B. Samal United States	12	215 1.3×	36 0.5×	21 0.4×	63 1.4×	41 1.2×	19	386

Countries citing papers authored by Aisha AlJanahi

Since Specialization

Citations

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

Fields of papers citing papers by Aisha AlJanahi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Aisha AlJanahi

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

All Works

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8 of 8 papers shown

Lee, Byung‐Chul, Chuanfeng Wu, Komudi Singh, et al.. (2024). Impact of CRISPR/HDR editing versus lentiviral transduction on long-term engraftment and clonal dynamics of HSPCs in rhesus macaques. Cell stem cell. 31(4). 455–466.e4. 8 indexed citations

Nickolls, Alec R., Marcin Szczot, Ruby M. Lam, et al.. (2020). Transcriptional Programming of Human Mechanosensory Neuron Subtypes from Pluripotent Stem Cells. Cell Reports. 30(3). 932–946.e7. 47 indexed citations

Shin, Tae–Hoon, Shirley Chen, Stefan Cordes, et al.. (2020). Macaque CRISPR/Cas9 Age-Related Clonal Hematopoiesis Model Demonstrates Expansion of TET2-Mutated Clones and Applicability for Testing Mitigation Approaches. Blood. 136(Supplement 1). 27–28. 1 indexed citations

Nickolls, Alec R., Marcin Szczot, Ruby M. Lam, et al.. (2019). Transcriptional Programming of Human Mechanosensory Neuron Subtypes. SSRN Electronic Journal. 1 indexed citations

AlJanahi, Aisha, Mark Danielsen, & Cynthia E. Dunbar. (2018). An Introduction to the Analysis of Single-Cell RNA-Sequencing Data. Molecular Therapy — Methods & Clinical Development. 10. 189–196. 73 indexed citations

Goodacre, Norman, Aisha AlJanahi, Subhiksha Nandakumar, Mike Mikailov, & Arifa S. Khan. (2018). A Reference Viral Database (RVDB) To Enhance Bioinformatics Analysis of High-Throughput Sequencing for Novel Virus Detection. mSphere. 3(2). 129 indexed citations

Yu, Kyung–Rok, Cynthia E. Dunbar, Marcus Alexandre Finzi Corat, et al.. (2017). A Non-Human Primate CRISPR/Cas9 Model of Clonal Hematopoiesis of Indeterminate Potential Demonstrates Expansion of TET2-Disrupted Clones. Blood. 130. 117–117. 1 indexed citations

Khan, Arifa S., Siemon H. S. Ng, Olivier M. Vandeputte, et al.. (2017). A Multicenter Study To Evaluate the Performance of High-Throughput Sequencing for Virus Detection. mSphere. 2(5). 26 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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