Samantha Beik

5.9k total citations
4 papers, 66 citations indexed

About

Samantha Beik is a scholar working on Molecular Biology, Oncology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Samantha Beik has authored 4 papers receiving a total of 66 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Molecular Biology, 1 paper in Oncology and 1 paper in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Samantha Beik's work include Gene Regulatory Network Analysis (2 papers), Cancer Genomics and Diagnostics (1 paper) and Machine Learning in Bioinformatics (1 paper). Samantha Beik is often cited by papers focused on Gene Regulatory Network Analysis (2 papers), Cancer Genomics and Diagnostics (1 paper) and Machine Learning in Bioinformatics (1 paper). Samantha Beik collaborates with scholars based in United States and Austria. Samantha Beik's co-authors include Ynes Helou, Leonard A. Harris, Vinh T. Nguyen, Arthur R. Salomon, Lizandra Jimenez, Patricia Midori Murobushi Ozawa, Alissa M. Weaver, Carlos F. Lopez, Vito Quaranta and Julien Sage and has published in prestigious journals such as PLoS ONE, PLoS Computational Biology and Current Opinion in Systems Biology.

In The Last Decade

Samantha Beik

4 papers receiving 66 citations

Peers

Countries citing papers authored by Samantha Beik

Since Specialization

Citations

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

Fields of papers citing papers by Samantha Beik

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Samantha Beik

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

All Works

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4 of 4 papers shown

#	Work	Indexed citations
1	Unified tumor growth mechanisms from multimodel inference and dataset integration 2023 ·PLoS Computational Biology ·Samantha Beik, Leonard A. Harris, (unknown), Julien Sage, Vito Quaranta, Carlos F. Lopez	7
2	Unsupervised logic-based mechanism inference for network-driven biological processes 2021 ·PLoS Computational Biology ·(unknown), Lukas Einkemmer, Samantha Beik, (unknown), Leonard A. Harris, Carlos F. Lopez	4
3	Modeling heterogeneous tumor growth dynamics and cell–cell interactions at single-cell and cell-population resolution 2019 ·Current Opinion in Systems Biology ·Leonard A. Harris, Samantha Beik, Patricia Midori Murobushi Ozawa, Lizandra Jimenez, Alissa M. Weaver	27
4	ERK Positive Feedback Regulates a Widespread Network of Tyrosine Phosphorylation Sites across Canonical T Cell Signaling and Actin Cytoskeletal Proteins in Jurkat T Cells 2013 ·PLoS ONE ·Ynes Helou, Vinh T. Nguyen, Samantha Beik, Arthur R. Salomon	28

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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