Emily Pace

528 total citations
9 papers, 352 citations indexed

About

Emily Pace is a scholar working on Molecular Biology, Oncology and Computational Theory and Mathematics. According to data from OpenAlex, Emily Pace has authored 9 papers receiving a total of 352 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Molecular Biology, 6 papers in Oncology and 3 papers in Computational Theory and Mathematics. Recurrent topics in Emily Pace's work include HER2/EGFR in Cancer Research (4 papers), Computational Drug Discovery Methods (3 papers) and Monoclonal and Polyclonal Antibodies Research (3 papers). Emily Pace is often cited by papers focused on HER2/EGFR in Cancer Research (4 papers), Computational Drug Discovery Methods (3 papers) and Monoclonal and Polyclonal Antibodies Research (3 papers). Emily Pace collaborates with scholars based in United States, Switzerland and Germany. Emily Pace's co-authors include Ulrik B. Nielsen, Matthew Onsum, Birgit Schoeberl, Lin Nie, Jeffrey A. Engelman, Olga Burenkova, Danan Li, Katherine Crosby, Kwok‐Kin Wong and Zandra E. Walton and has published in prestigious journals such as Journal of Clinical Oncology, Blood and Cancer Research.

In The Last Decade

Emily Pace

8 papers receiving 346 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Emily Pace United States	5	222	210	142	62	41	9	352
Sergio Iadevaia United States	9	204 0.9×	330 1.6×	140 1.0×	44 0.7×	53 1.3×	19	496
Matthew McCoy United States	12	217 1.0×	163 0.8×	80 0.6×	40 0.6×	20 0.5×	32	439
Hayley E. Francies United Kingdom	10	178 0.8×	246 1.2×	29 0.2×	52 0.8×	48 1.2×	17	436
Elisa Baldelli United States	13	212 1.0×	339 1.6×	43 0.3×	226 3.6×	14 0.3×	34	545
Cammie R. Sutton United States	6	289 1.3×	251 1.2×	164 1.2×	87 1.4×	8 0.2×	9	421
Aida Shahrabi Netherlands	6	239 1.1×	308 1.5×	30 0.2×	32 0.5×	42 1.0×	7	463
Andrei Y. Volgin United States	5	123 0.6×	166 0.8×	84 0.6×	143 2.3×	37 0.9×	7	357
Kimberly Aardalen United States	7	165 0.7×	183 0.9×	38 0.3×	87 1.4×	40 1.0×	7	283
Ricarda M. Hoffmann United Kingdom	5	176 0.8×	108 0.5×	115 0.8×	23 0.4×	18 0.4×	6	257
Nara De Matos Granja-Ingram United States	3	404 1.8×	364 1.7×	215 1.5×	94 1.5×	16 0.4×	3	613

Countries citing papers authored by Emily Pace

Since Specialization

Citations

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

Fields of papers citing papers by Emily Pace

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Emily Pace

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

All Works

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9 of 9 papers shown

Pierce, Daniel W., Emily Pace, Hongbin Wang, et al.. (2021). Synergistic Combination Activity of the Novel GSPT1 Degrader CC-90009 in Acute Myeloid Leukemia Models. Blood. 138(Supplement 1). 3330–3330. 7 indexed citations

Hass, Helge, Kristina Masson, Violette Paragas, et al.. (2017). Predicting ligand-dependent tumors from multi-dimensional signaling features. npj Systems Biology and Applications. 3(1). 27–27. 31 indexed citations

Pace, Emily, Sharlene Adams, Michael Curley, et al.. (2015). Effect of MM-141 on gemcitabine and nab-paclitaxel potentiation in preclinical models of pancreatic cancer through induction of IGF-1R and ErbB3 degradation.. Journal of Clinical Oncology. 33(3_suppl). 289–289. 4 indexed citations

Adams, Sharlene, Jason Baum, Victoria Rimkunas, et al.. (2013). Abstract C169: MM-141, a bispecific antibody inhibitor of PI3K/AKT/mTOR, attenuates tumor growth and potentiates everolimus in mouse models of anti-hormonal therapy-resistant ER/PR+ breast cancer.. Molecular Cancer Therapeutics. 12(11_Supplement). C169–C169. 1 indexed citations

Euw, Erika M. von, Emily Pace, Diana H. Chai, et al.. (2013). Abstract 2077A: MM141, a novel bispecific antibody co-inhibitor of IGF-1R and ErbB3, inhibits the proliferation of melanoma cells.. Cancer Research. 73(8_Supplement). 2077A–2077A. 2 indexed citations

Kirouac, Daniel C., Johanna Lahdenranta, Ryan Overland, et al.. (2013). Computational Modeling of ERBB2 -Amplified Breast Cancer Identifies Combined ErbB2/3 Blockade as Superior to the Combination of MEK and AKT Inhibitors. Science Signaling. 6(288). ra68–ra68. 85 indexed citations

Niepel, Mario, Marc Hafner, Emily Pace, Birgit Schoeberl, & Peter K. Sorger. (2012). Abstract PR5: The receptor tyrosine kinase layer of breast cancer cell lines is predictive of the response to therapeutic drugs. Cancer Research. 72(13_Supplement). PR5–PR5.

Schoeberl, Birgit, Anthony C. Faber, Danan Li, et al.. (2010). An ErbB3 Antibody, MM-121, Is Active in Cancers with Ligand-Dependent Activation. Cancer Research. 70(6). 2485–2494. 215 indexed citations

Schoeberl, Birgit, et al.. (2006). A Data-Driven Computational Model of the ErbB Receptor Signaling Network. PubMed. 8. 53–54. 7 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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