Jake Crawford

794 total citations
10 papers, 277 citations indexed

About

Jake Crawford is a scholar working on Molecular Biology, Cancer Research and Statistical and Nonlinear Physics. According to data from OpenAlex, Jake Crawford has authored 10 papers receiving a total of 277 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 3 papers in Cancer Research and 2 papers in Statistical and Nonlinear Physics. Recurrent topics in Jake Crawford's work include Bioinformatics and Genomic Networks (4 papers), Cancer Genomics and Diagnostics (3 papers) and Gene expression and cancer classification (3 papers). Jake Crawford is often cited by papers focused on Bioinformatics and Genomic Networks (4 papers), Cancer Genomics and Diagnostics (3 papers) and Gene expression and cancer classification (3 papers). Jake Crawford collaborates with scholars based in United States, United Kingdom and Switzerland. Jake Crawford's co-authors include J. Keith Joung, Nicolò Fusi, Benjamin P. Kleinstiver, Luong Hoang, John G. Doench, Michael M. Weinstein, Jennifer Listgarten, Alexander A. Sousa, Casey S. Greene and Maria Chikina and has published in prestigious journals such as Nature Communications, Bioinformatics and Genome biology.

In The Last Decade

Jake Crawford

10 papers receiving 276 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jake Crawford United States	6	245	37	34	24	20	10	277
Jifang Yan China	5	401 1.6×	49 1.3×	49 1.4×	31 1.3×	30 1.5×	6	426
Kirsten E Snijders United Kingdom	5	313 1.3×	55 1.5×	27 0.8×	13 0.5×	15 0.8×	7	360
Nahye Kim South Korea	7	237 1.0×	48 1.3×	27 0.8×	28 1.2×	12 0.6×	15	270
Peter Cameron Switzerland	5	264 1.1×	49 1.3×	47 1.4×	20 0.8×	25 1.3×	5	286
Oliver Pelz Germany	5	257 1.0×	27 0.7×	10 0.3×	34 1.4×	16 0.8×	6	297
Luong Hoang United States	3	218 0.9×	31 0.8×	34 1.0×	23 1.0×	19 0.9×	4	280
Aidan R. O’Brien Australia	8	298 1.2×	58 1.6×	41 1.2×	40 1.7×	13 0.7×	11	331
Benjamin Holmes United States	4	176 0.7×	24 0.6×	25 0.7×	26 1.1×	10 0.5×	5	186
Chase C. Suiter United States	4	273 1.1×	76 2.1×	15 0.4×	37 1.5×	12 0.6×	5	303

Countries citing papers authored by Jake Crawford

Since Specialization

Citations

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

Fields of papers citing papers by Jake Crawford

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jake Crawford

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

All Works

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10 of 10 papers shown

Crawford, Jake, Maria Chikina, & Casey S. Greene. (2024). Best holdout assessment is sufficient for cancer transcriptomic model selection. Patterns. 5(12). 101115–101115. 1 indexed citations

Crawford, Jake, Maria Chikina, & Casey S. Greene. (2024). Optimizer’s dilemma: optimization strongly influences model selection in transcriptomic prediction. Bioinformatics Advances. 4(1). vbae004–vbae004. 2 indexed citations

Govek, Kiya W., et al.. (2023). CAJAL enables analysis and integration of single-cell morphological data using metric geometry. Nature Communications. 14(1). 3672–3672. 11 indexed citations

Crawford, Jake, et al.. (2023). The effect of non-linear signal in classification problems using gene expression. PLoS Computational Biology. 19(3). e1010984–e1010984. 7 indexed citations

Crawford, Jake, Brock C. Christensen, Maria Chikina, & Casey S. Greene. (2022). Widespread redundancy in -omics profiles of cancer mutation states. Genome biology. 23(1). 137–137. 7 indexed citations

Lee, Alexandra, Dallas L. Mould, Jake Crawford, et al.. (2022). SOPHIE: Generative Neural Networks Separate Common and Specific Transcriptional Responses. Genomics Proteomics & Bioinformatics. 20(5). 912–927. 5 indexed citations

Tomasoni, Mattia, Sergio Gómez, Jake Crawford, et al.. (2020). MONET : a toolbox integrating top-performing methods for network modularization. Bioinformatics. 36(12). 3920–3921. 12 indexed citations

Choobdar, Sarvenaz, Mehmet Eren Ahsen, Jake Crawford, et al.. (2018). Open Community Challenge Reveals Molecular Network Modules with Key Roles in Diseases. SSRN Electronic Journal. 3 indexed citations

Listgarten, Jennifer, Michael M. Weinstein, Benjamin P. Kleinstiver, et al.. (2018). Prediction of off-target activities for the end-to-end design of CRISPR guide RNAs. Nature Biomedical Engineering. 2(1). 38–47. 228 indexed citations

10.

Crawford, Jake, et al.. (2017). Detangling PPI Networks to Uncover Functionally Meaningful Clusters. 752–753. 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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