William S. Ulrich

1.1k total citations
8 papers, 417 citations indexed

About

William S. Ulrich is a scholar working on Genetics, Molecular Biology and Cognitive Neuroscience. According to data from OpenAlex, William S. Ulrich has authored 8 papers receiving a total of 417 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Genetics, 7 papers in Molecular Biology and 2 papers in Cognitive Neuroscience. Recurrent topics in William S. Ulrich's work include Genetic Associations and Epidemiology (7 papers), Epigenetics and DNA Methylation (4 papers) and Bioinformatics and Genomic Networks (3 papers). William S. Ulrich is often cited by papers focused on Genetic Associations and Epidemiology (7 papers), Epigenetics and DNA Methylation (4 papers) and Bioinformatics and Genomic Networks (3 papers). William S. Ulrich collaborates with scholars based in United States, Italy and Switzerland. William S. Ulrich's co-authors include Daniel R. Weinberger, Thomas M. Hyde, Joel E. Kleinman, Leonardo Collado‐Torres, Andrew E. Jaffe, Richard E. Straub, Amy Deep‐Soboslay, Qiang Chen, Ran Tao and Brady J. Maher and has published in prestigious journals such as Nature Communications, Neuron and Nature Neuroscience.

In The Last Decade

William S. Ulrich

8 papers receiving 415 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
William S. Ulrich United States 6 272 202 58 54 45 8 417
Rahul Bharadwaj United States 11 374 1.4× 195 1.0× 61 1.1× 59 1.1× 51 1.1× 20 527
Annie Kathuria United States 10 284 1.0× 98 0.5× 87 1.5× 44 0.8× 66 1.5× 13 432
Daisuke Ikawa Japan 9 192 0.7× 102 0.5× 39 0.7× 24 0.4× 59 1.3× 13 377
Colm Ó'Dúshláine United States 11 274 1.0× 384 1.9× 27 0.5× 41 0.8× 52 1.2× 13 563
David H. Kavanagh United Kingdom 8 205 0.8× 179 0.9× 61 1.1× 30 0.6× 78 1.7× 9 362
Miho Nakajima Japan 6 219 0.8× 85 0.4× 52 0.9× 21 0.4× 83 1.8× 9 408
Safa Al‐Saraj Australia 7 244 0.9× 116 0.6× 52 0.9× 40 0.7× 20 0.4× 8 391
Jianmin Yuan China 12 207 0.8× 114 0.6× 58 1.0× 36 0.7× 41 0.9× 28 375
Erin Newburn United States 6 235 0.9× 84 0.4× 121 2.1× 31 0.6× 48 1.1× 8 367
Alessia Fiorentino United Kingdom 9 200 0.7× 89 0.4× 58 1.0× 20 0.4× 21 0.5× 15 335

Countries citing papers authored by William S. Ulrich

Since Specialization
Citations

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

Fields of papers citing papers by William S. Ulrich

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by William S. Ulrich. 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 William S. Ulrich. The network helps show where William S. Ulrich may publish in the future.

Co-authorship network of co-authors of William S. Ulrich

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

All Works

8 of 8 papers shown
1.
Pertea, Geo, Laura D’Ignazio, Leonardo Collado‐Torres, et al.. (2024). Sex affects transcriptional associations with schizophrenia across the dorsolateral prefrontal cortex, hippocampus, and caudate nucleus. Nature Communications. 15(1). 3980–3980. 5 indexed citations
2.
Chen, Qiang, Giulio Pergola, Aaron L. Goldman, et al.. (2021). G-MIND: An end-to-end multimodal imaging-genetics framework for biomarker identification and disease classification. CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro). 6 indexed citations
3.
Mandell, Kira A. Perzel, Nicholas J. Eagles, Richard Wilton, et al.. (2021). Genome-wide sequencing-based identification of methylation quantitative trait loci and their role in schizophrenia risk. Nature Communications. 12(1). 5251–5251. 35 indexed citations
4.
Chen, Qiang, Giulio Pergola, Aaron L. Goldman, et al.. (2021). A generative-discriminative framework that integrates imaging, genetic, and diagnosis into coupled low dimensional space. NeuroImage. 238. 118200–118200. 7 indexed citations
5.
Zink, Caroline F., Peter B. Barker, Akira Sawa, et al.. (2020). Association of Missense Mutation in FOLH1 With Decreased NAAG Levels and Impaired Working Memory Circuitry and Cognition. American Journal of Psychiatry. 177(12). 1129–1139. 28 indexed citations
6.
Collado‐Torres, Leonardo, Emily E. Burke, Amy Peterson, et al.. (2019). Regional Heterogeneity in Gene Expression, Regulation, and Coherence in the Frontal Cortex and Hippocampus across Development and Schizophrenia. Neuron. 103(2). 203–216.e8. 124 indexed citations
7.
Chen, Qiang, Aaron L. Goldman, William S. Ulrich, et al.. (2019). A generative-predictive framework to capture altered brain activity in fMRI and its association with genetic risk: application to Schizophrenia. 31. 77–77. 1 indexed citations
8.
Jaffe, Andrew E., Richard E. Straub, Joo Heon Shin, et al.. (2018). Developmental and genetic regulation of the human cortex transcriptome illuminate schizophrenia pathogenesis. Nature Neuroscience. 21(8). 1117–1125. 211 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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