Lisa McWilliams

730 total citations
10 papers, 188 citations indexed

About

Lisa McWilliams is a scholar working on Molecular Biology, Computational Theory and Mathematics and Biophysics. According to data from OpenAlex, Lisa McWilliams has authored 10 papers receiving a total of 188 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 3 papers in Computational Theory and Mathematics and 3 papers in Biophysics. Recurrent topics in Lisa McWilliams's work include Computational Drug Discovery Methods (3 papers), Cell Image Analysis Techniques (3 papers) and Amino Acid Enzymes and Metabolism (1 paper). Lisa McWilliams is often cited by papers focused on Computational Drug Discovery Methods (3 papers), Cell Image Analysis Techniques (3 papers) and Amino Acid Enzymes and Metabolism (1 paper). Lisa McWilliams collaborates with scholars based in United Kingdom, United States and Sweden. Lisa McWilliams's co-authors include Asha S. Nayar, Lindsey Leach, Sarah M. McLeod, Shin‐ichiro Narita, Alita A. Miller, Thomas J. Dougherty, Keith Ferguson, Hajime Tokuda, Dean G. Brown and David C. Murray and has published in prestigious journals such as Journal of Biological Chemistry, Journal of Bacteriology and Neuro-Oncology.

In The Last Decade

Lisa McWilliams

10 papers receiving 181 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Lisa McWilliams United Kingdom	6	101	45	37	32	29	10	188
Anh Miu Canada	5	165 1.6×	30 0.7×	31 0.8×	46 1.4×	33 1.1×	6	223
Shreyas Kaptan Germany	10	247 2.4×	42 0.9×	69 1.9×	22 0.7×	24 0.8×	13	362
Jeremy R. Hershfield United States	10	169 1.7×	39 0.9×	47 1.3×	10 0.3×	25 0.9×	14	332
Kennosuke Wada Japan	10	266 2.6×	75 1.7×	61 1.6×	8 0.3×	12 0.4×	26	425
Zeyu Zhu United States	10	189 1.9×	43 1.0×	58 1.6×	4 0.1×	18 0.6×	19	327
Qinghua Luo China	11	211 2.1×	82 1.8×	120 3.2×	7 0.2×	11 0.4×	25	395
Luise Eckhardt-Strelau Germany	7	303 3.0×	33 0.7×	15 0.4×	15 0.5×	26 0.9×	7	368
Teresa Páramo United Kingdom	7	179 1.8×	10 0.2×	17 0.5×	16 0.5×	36 1.2×	10	323
Meine Ramakers Belgium	13	431 4.3×	9 0.2×	45 1.2×	64 2.0×	19 0.7×	17	569
Ludovic Carlier France	11	211 2.1×	9 0.2×	20 0.5×	11 0.3×	21 0.7×	26	393

Countries citing papers authored by Lisa McWilliams

Since Specialization

Citations

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

Fields of papers citing papers by Lisa McWilliams

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lisa McWilliams

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

All Works

Sort: Min cites: Since: Top N: Style:

10 of 10 papers shown

O’Halloran, Peter, et al.. (2022). Exercise Interventions for Women with Ovarian Cancer: A Realist Review. Healthcare. 10(4). 720–720. 7 indexed citations

Staniszewska, Anna D., Domenic Pilger, Giuditta Illuzzi, et al.. (2022). DDDR-01. AZD9574 IS A NOVEL, BRAIN PENETRANT PARP-1 SELECTIVE INHIBITOR WITH ACTIVITY IN AN INTRACRANIAL XENOGRAFT MODEL OF TRIPLE NEGATIVE BREAST CARCINOMA WITH HOMOLOGOUS RECOMBINATION REPAIR DEFICIENCY. Neuro-Oncology. 24(Supplement_7). vii98–vii98. 2 indexed citations

McWilliams, Lisa, et al.. (2021). A Novel High-Throughput FLIPR Tetra–Based Method for Capturing Highly Confluent Kinetic Data for Structure–Kinetic Relationship Guided Early Drug Discovery. SLAS DISCOVERY. 26(5). 684–697. 5 indexed citations

Plant, Helen, et al.. (2018). Techniques to Enable 1536-Well Phenotypic Screening. Methods in molecular biology. 1787. 263–278. 1 indexed citations

Plant, Helen, Lisa McWilliams, David C. Murray, et al.. (2017). Enabling 1536-Well High-Throughput Cell-Based Screening through the Application of Novel Centrifugal Plate Washing. SLAS DISCOVERY. 22(6). 732–742. 14 indexed citations

Conway, Leslie, Ross A. Cardarelli, Yvonne E. Moore, et al.. (2017). N-Ethylmaleimide increases KCC2 cotransporter activity by modulating transporter phosphorylation. Journal of Biological Chemistry. 292(52). 21253–21263. 30 indexed citations

Murray, David C., Lisa McWilliams, & Mark Wigglesworth. (2016). High-Throughput Cell Toxicity Assays. Methods in molecular biology. 1439. 245–262. 4 indexed citations

Mervin, Lewis, Qing Cao, Ian P. Barrett, et al.. (2016). Understanding Cytotoxicity and Cytostaticity in a High-Throughput Screening Collection. ACS Chemical Biology. 11(11). 3007–3023. 32 indexed citations

Bardelle, Catherine, et al.. (2015). Validation of Miniaturized One-Step Reverse Transcription qPCR Assays for High-Throughput Screening and Comparison to a Reporter Gene Methodology. Assay and Drug Development Technologies. 13(2). 94–101. 3 indexed citations

10.

Nayar, Asha S., Thomas J. Dougherty, Keith Ferguson, et al.. (2015). Novel Antibacterial Targets and Compounds Revealed by a High-Throughput Cell Wall Reporter Assay. Journal of Bacteriology. 197(10). 1726–1734. 90 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.

Explore authors with similar magnitude of impact