Ryan Delahanty

2.7k total citations
17 papers, 1.1k citations indexed

About

Ryan Delahanty is a scholar working on Genetics, Molecular Biology and Cognitive Neuroscience. According to data from OpenAlex, Ryan Delahanty has authored 17 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Genetics, 5 papers in Molecular Biology and 4 papers in Cognitive Neuroscience. Recurrent topics in Ryan Delahanty's work include Genetic Associations and Epidemiology (4 papers), Genomic variations and chromosomal abnormalities (4 papers) and Autism Spectrum Disorder Research (4 papers). Ryan Delahanty is often cited by papers focused on Genetic Associations and Epidemiology (4 papers), Genomic variations and chromosomal abnormalities (4 papers) and Autism Spectrum Disorder Research (4 papers). Ryan Delahanty collaborates with scholars based in United States, China and Taiwan. Ryan Delahanty's co-authors include James S. Sutcliffe, Spencer S. Jones, Jacob L. McCauley, Susan E. Folstein, Chun Li, Lan Jiang, Qiao Han, Harish C. Prasad, Randy Blakely and Robert Sherwin and has published in prestigious journals such as PLoS ONE, JNCI Journal of the National Cancer Institute and Cancer Research.

In The Last Decade

Ryan Delahanty

17 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
Ryan Delahanty United States 15 476 423 346 190 153 17 1.1k
Maggie Chow United States 12 345 0.7× 435 1.0× 433 1.3× 70 0.4× 82 0.5× 26 1.0k
Tricia A. Thornton‐Wells United States 21 309 0.6× 174 0.4× 506 1.5× 55 0.3× 74 0.5× 41 1.1k
Patrick Sleiman United States 24 768 1.6× 312 0.7× 632 1.8× 131 0.7× 100 0.7× 94 2.4k
Lisa J. Strug Canada 26 606 1.3× 310 0.7× 523 1.5× 100 0.5× 185 1.2× 81 2.2k
Dimitri J. Stavropoulos Canada 23 1.2k 2.5× 139 0.3× 731 2.1× 66 0.3× 55 0.4× 66 1.7k
Jessica Ezzell Hunter United States 19 745 1.6× 464 1.1× 491 1.4× 30 0.2× 173 1.1× 57 1.3k
David Margulies United States 15 407 0.9× 159 0.4× 370 1.1× 41 0.2× 68 0.4× 31 1.2k
Christina M. Hultman Sweden 16 967 2.0× 135 0.3× 628 1.8× 71 0.4× 82 0.5× 26 1.7k
Angela Tam Canada 16 129 0.3× 570 1.3× 641 1.9× 43 0.2× 58 0.4× 33 1.6k
Kin Y. Mok United Kingdom 19 349 0.7× 87 0.2× 449 1.3× 160 0.8× 171 1.1× 38 1.7k

Countries citing papers authored by Ryan Delahanty

Since Specialization
Citations

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

Fields of papers citing papers by Ryan Delahanty

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ryan Delahanty

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

All Works

17 of 17 papers shown
1.
Delahanty, Ryan, et al.. (2019). Development and Evaluation of a Machine Learning Model for the Early Identification of Patients at Risk for Sepsis. Annals of Emergency Medicine. 73(4). 334–344. 144 indexed citations
2.
Delahanty, Ryan, David Kaufman, & Spencer S. Jones. (2018). Development and Evaluation of an Automated Machine Learning Algorithm for In-Hospital Mortality Risk Adjustment Among Critical Care Patients*. Critical Care Medicine. 46(6). e481–e488. 74 indexed citations
3.
Delahanty, Ryan, Yanfeng Zhang, Terry Jo Bichell, et al.. (2016). Beyond Epilepsy and Autism: Disruption of GABRB3 Causes Ocular Hypopigmentation. Cell Reports. 17(12). 3115–3124. 14 indexed citations
4.
Zhang, Yanfeng, Ryan Delahanty, Xingyi Guo, Wei Zheng, & Jirong Long. (2015). Integrative genomic analysis reveals functional diversification of APOBEC gene family in breast cancer. Human Genomics. 9(1). 18 indexed citations
5.
Villegas, Raquel, Ryan Delahanty, Scott M. Williams, et al.. (2015). Genetic Variation and Insulin Resistance in Middle‐Aged Chinese Men. Annals of Human Genetics. 79(5). 357–365. 2 indexed citations
6.
Ma, Xiangyu, Qiuyin Cai, Ryan Delahanty, et al.. (2013). No Association between Ovarian Cancer Susceptibility Variants and Breast Cancer Risk among Chinese Women. Cancer Epidemiology Biomarkers & Prevention. 22(3). 467–469. 1 indexed citations
7.
Long, Jirong, Ryan Delahanty, Guoliang Li, et al.. (2013). A Common Deletion in the APOBEC3 Genes and Breast Cancer Risk. JNCI Journal of the National Cancer Institute. 105(8). 573–579. 103 indexed citations
8.
Shen, Chong, Ryan Delahanty, Yu‐Tang Gao, et al.. (2013). Evaluating GWAS-Identified SNPs for Age at Natural Menopause among Chinese Women. PLoS ONE. 8(3). e58766–e58766. 21 indexed citations
9.
Delahanty, Ryan, Alicia Beeghly‐Fadiel, Jirong Long, et al.. (2013). Evaluation of GWAS-identified genetic variants for age at menarche among Chinese women. Human Reproduction. 28(4). 1135–1143. 23 indexed citations
10.
Shu, Xiao Ou, Jirong Long, Wei Lu, et al.. (2012). Novel Genetic Markers of Breast Cancer Survival Identified by a Genome-Wide Association Study. Cancer Research. 72(5). 1182–1189. 45 indexed citations
11.
Villegas, Raquel, Ryan Delahanty, Yu‐Tang Gao, et al.. (2012). Joint Effect of Genetic and Lifestyle Risk Factors on Type 2 Diabetes Risk among Chinese Men and Women. PLoS ONE. 7(11). e49464–e49464. 15 indexed citations
12.
Dorjgochoo, Tsogzolmaa, Ryan Delahanty, Wei Lu, et al.. (2011). Common Genetic Variants in the Vitamin D Pathway Including Genome-Wide Associated Variants Are Not Associated with Breast Cancer Risk among Chinese Women. Cancer Epidemiology Biomarkers & Prevention. 20(10). 2313–2316. 30 indexed citations
13.
Delahanty, Ryan, Jing‐Qiong Kang, Camille W. Brune, et al.. (2009). Maternal transmission of a rare GABRB3 signal peptide variant is associated with autism. Molecular Psychiatry. 16(1). 86–96. 84 indexed citations
14.
Cross, Sarah J., Soo‐Jeong Kim, Lauren A. Weiss, et al.. (2007). Molecular Genetics of the Platelet Serotonin System in First-Degree Relatives of Patients with Autism. Neuropsychopharmacology. 33(2). 353–360. 50 indexed citations
15.
Weiss, Lauren A., Gülüm Kosova, Ryan Delahanty, et al.. (2006). Variation in ITGB3 is associated with whole-blood serotonin level and autism susceptibility. European Journal of Human Genetics. 14(8). 923–931. 70 indexed citations
16.
Sutcliffe, James S., Ryan Delahanty, Harish C. Prasad, et al.. (2005). Allelic Heterogeneity at the Serotonin Transporter Locus (SLC6A4) Confers Susceptibility to Autism and Rigid-Compulsive Behaviors. The American Journal of Human Genetics. 77(2). 265–279. 299 indexed citations
17.
McCauley, Jacob L., Lana M. Olson, Ryan Delahanty, et al.. (2004). A linkage disequilibrium map of the 1‐Mb 15q12 GABAA receptor subunit cluster and association to autism. American Journal of Medical Genetics Part B Neuropsychiatric Genetics. 131B(1). 51–59. 118 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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