Alicja Tadych

1.5k total citations
12 papers, 695 citations indexed

About

Alicja Tadych is a scholar working on Molecular Biology, Genetics and Aging. According to data from OpenAlex, Alicja Tadych has authored 12 papers receiving a total of 695 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 2 papers in Genetics and 2 papers in Aging. Recurrent topics in Alicja Tadych's work include Bioinformatics and Genomic Networks (6 papers), Gene expression and cancer classification (4 papers) and Genomics and Chromatin Dynamics (2 papers). Alicja Tadych is often cited by papers focused on Bioinformatics and Genomic Networks (6 papers), Gene expression and cancer classification (4 papers) and Genomics and Chromatin Dynamics (2 papers). Alicja Tadych collaborates with scholars based in United States, Japan and Portugal. Alicja Tadych's co-authors include Olga G. Troyanskaya, Vicky Yao, Aaron K. Wong, Arjun Krishnan, Ran Zhang, Chandra L. Theesfeld, Natalia Volfovsky, Alex Lash, Alan Packer and Coleen T. Murphy and has published in prestigious journals such as Nucleic Acids Research, Nature Neuroscience and Immunity.

In The Last Decade

Alicja Tadych

10 papers receiving 692 citations

Peers

Alicja Tadych

MB

GENET

AGING

CN

ONCOL

Vicky Yao United States

Anahita Amiri United States

Cho-Yi Chen Taiwan

C. Kimberly Tsui United States

Kraig M. Theriault United States

Andrew Knights United Kingdom

Julia M. Schulze‐Hentrich Germany

Noah Davidsohn United States

Frank M. J. Jacobs Netherlands

Akiko Yanagiya Canada

Vicky Yao United States

Alicja Tadych

611 ×1.4

MB

205 ×1.2

GENET

180 ×1.5

AGING

118 ×1.0

CN

88 ×1.2

ONCOL

Citations per year, relative to Alicja Tadych Alicja Tadych (= 1×) peers Vicky Yao

Countries citing papers authored by Alicja Tadych

Since Specialization

Citations

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

Fields of papers citing papers by Alicja Tadych

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Alicja Tadych

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

All Works

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12 of 12 papers shown

1.

Wu, Tinghui, et al.. (2024). Widespread Impact of Natural Genetic Variations in CRISPR-Cas9 Outcomes. The CRISPR Journal. 7(5). 283–292.

2.

Thistlethwaite, William, Alicja Tadych, Frederique Ruf-Zamojski, et al.. (2024). Automated single-cell omics end-to-end framework with data-driven batch inference. Cell Systems. 15(10). 982–990.e5. 2 indexed citations

3.

Tadych, Alicja, et al.. (2024). Tapioca: a platform for predicting de novo protein–protein interactions in dynamic contexts. Nature Methods. 21(3). 488–500. 12 indexed citations

4.

Cofer, Evan M., João Raimundo, Alicja Tadych, et al.. (2021). Modeling transcriptional regulation of model species with deep learning. Genome Research. 31(6). 1097–1105. 7 indexed citations

5.

Dannenfelser, Ruth, Benjamin VanderSluis, Vicky Yao, et al.. (2020). Discriminatory Power of Combinatorial Antigen Recognition in Cancer T Cell Therapies. Cell Systems. 11(3). 215–228.e5. 69 indexed citations

6.

Zhou, Jian, Ignacio E. Schor, Vicky Yao, et al.. (2019). Accurate genome-wide predictions of spatio-temporal gene expression during embryonic development. PLoS Genetics. 15(9). e1008382–e1008382. 4 indexed citations

7.

Kaletsky, Rachel, Vicky Yao, April E. Williams, et al.. (2018). Transcriptome analysis of adult Caenorhabditis elegans cells reveals tissue-specific gene and isoform expression. PLoS Genetics. 14(8). e1007559–e1007559. 142 indexed citations

8.

Lee, Young-Suk, Aaron K. Wong, Alicja Tadych, et al.. (2018). Interpretation of an individual functional genomics experiment guided by massive public data. Nature Methods. 15(12). 1049–1052.

9.

Krishnan, Arjun, Ran Zhang, Vicky Yao, et al.. (2016). Genome-wide prediction and functional characterization of the genetic basis of autism spectrum disorder. Nature Neuroscience. 19(11). 1454–1462. 265 indexed citations

10.

Wong, Aaron K., Arjun Krishnan, Vicky Yao, Alicja Tadych, & Olga G. Troyanskaya. (2015). IMP 2.0: a multi-species functional genomics portal for integration, visualization and prediction of protein functions and networks. Nucleic Acids Research. 43(W1). W128–W133. 46 indexed citations

11.

Zhu, Qian, Aaron K. Wong, Arjun Krishnan, et al.. (2015). Targeted exploration and analysis of large cross-platform human transcriptomic compendia. Nature Methods. 12(3). 211–214. 110 indexed citations

12.

Zaslavsky, Elena, Miguel Fribourg, Christopher Y. Park, et al.. (2015). Interactive Big Data Resource to Elucidate Human Immune Pathways and Diseases. Immunity. 43(3). 605–614. 38 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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