Melissa Cline

20.5k total citations
53 papers, 3.6k citations indexed

About

Melissa Cline is a scholar working on Molecular Biology, Genetics and Cancer Research. According to data from OpenAlex, Melissa Cline has authored 53 papers receiving a total of 3.6k indexed citations (citations by other indexed papers that have themselves been cited), including 45 papers in Molecular Biology, 7 papers in Genetics and 7 papers in Cancer Research. Recurrent topics in Melissa Cline's work include RNA and protein synthesis mechanisms (14 papers), Machine Learning in Bioinformatics (13 papers) and RNA Research and Splicing (13 papers). Melissa Cline is often cited by papers focused on RNA and protein synthesis mechanisms (14 papers), Machine Learning in Bioinformatics (13 papers) and RNA Research and Splicing (13 papers). Melissa Cline collaborates with scholars based in United States, Germany and Canada. Melissa Cline's co-authors include Manuel Ares, Kevin Karplus, Yael Mandel‐Gutfreund, David Haussler, Rachel Karchin, Tyson A. Clark, Mark Diekhans, Idit Kosti, Inbal Paz and Jingchun Zhu and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Nature Genetics.

In The Last Decade

Melissa Cline

52 papers receiving 3.5k citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Melissa Cline 3.1k 624 406 243 222 53 3.6k
Hongbo Xie 2.2k 0.7× 257 0.4× 205 0.5× 370 1.5× 329 1.5× 63 2.9k
William T. Arthur 2.3k 0.7× 301 0.5× 274 0.7× 162 0.7× 65 0.3× 26 3.3k
Beth Murray 2.2k 0.7× 206 0.3× 315 0.8× 156 0.6× 66 0.3× 5 2.8k
Caretha L. Creasy 2.8k 0.9× 317 0.5× 188 0.5× 150 0.6× 55 0.2× 41 3.4k
Dorothea Becker 2.4k 0.8× 408 0.7× 253 0.6× 204 0.8× 44 0.2× 70 3.5k
Florence Poy 1.9k 0.6× 322 0.5× 167 0.4× 224 0.9× 108 0.5× 28 2.8k
D. D. Wood 2.0k 0.6× 317 0.5× 234 0.6× 133 0.5× 75 0.3× 58 3.0k
Dalia Baršytė-Lovejoy 4.5k 1.4× 677 1.1× 206 0.5× 234 1.0× 38 0.2× 71 5.1k
Liang Hong 1.8k 0.6× 127 0.2× 240 0.6× 110 0.5× 73 0.3× 106 2.6k

Countries citing papers authored by Melissa Cline

Since Specialization
Citations

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

Fields of papers citing papers by Melissa Cline

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Melissa Cline

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

All Works

20 of 20 papers shown
1.
Thomas, Alun, et al.. (2025). When two plus four does not equal six: Combining computational and functional evidence to classify BRCA1 key domain missense substitutions. The American Journal of Human Genetics. 112(9). 2027–2042.
2.
Cline, Melissa, et al.. (2022). Modeling the impact of data sharing on variant classification. Journal of the American Medical Informatics Association. 30(3). 466–474. 4 indexed citations
3.
Parsons, Michael T., Charles Markello, Yusuke Iwasaki, et al.. (2022). Federated analysis of BRCA1 and BRCA2 variation in a Japanese cohort. Cell Genomics. 2(3). 100109–100109. 3 indexed citations
4.
Thorogood, Adrian, Heidi L. Rehm, Peter Goodhand, et al.. (2021). International federation of genomic medicine databases using GA4GH standards. Cell Genomics. 1(2). 100032–100032. 22 indexed citations
5.
Paz, Inbal, Idit Kosti, Manuel Ares, Melissa Cline, & Yael Mandel‐Gutfreund. (2014). RBPmap: a web server for mapping binding sites of RNA-binding proteins. Nucleic Acids Research. 42(W1). W361–W367. 385 indexed citations
6.
Goldman, Mary J., Brian Craft, Teresa Swatloski, et al.. (2014). The UCSC Cancer Genomics Browser: update 2015. Nucleic Acids Research. 43(D1). D812–D817. 252 indexed citations
7.
Hall, Megan P., Roland Nagel, W. Samuel Fagg, et al.. (2013). Quaking and PTB control overlapping splicing regulatory networks during muscle cell differentiation. RNA. 19(5). 627–638. 125 indexed citations
8.
Cline, Melissa, Brian Craft, Teresa Swatloski, et al.. (2013). Exploring TCGA Pan-Cancer Data at the UCSC Cancer Genomics Browser. Scientific Reports. 3(1). 2652–2652. 213 indexed citations
9.
Pistoni, Mariaelena, Lily Shiue, Melissa Cline, et al.. (2013). Rbfox1 Downregulation and Altered Calpain 3 Splicing by FRG1 in a Mouse Model of Facioscapulohumeral Muscular Dystrophy (FSHD). PLoS Genetics. 9(1). e1003186–e1003186. 32 indexed citations
10.
Goldman, Mary J., Brian Craft, Teresa Swatloski, et al.. (2012). The UCSC Cancer Genomics Browser: update 2013. Nucleic Acids Research. 41(D1). D949–D954. 129 indexed citations
11.
Salomonis, Nathan, Christopher R. Schlieve, Laura Pereira, et al.. (2010). Alternative splicing regulates mouse embryonic stem cell pluripotency and differentiation. Proceedings of the National Academy of Sciences. 107(23). 10514–10519. 175 indexed citations
12.
Sugnet, Charles W., Tyson A. Clark, Georgeann S. O’Brien, et al.. (2006). Unusual Intron Conservation near Tissue-Regulated Exons Found by Splicing Microarrays. PLoS Computational Biology. 2(1). e4–e4. 158 indexed citations
13.
Ule, Jernej, Aljaž Ule, Joanna L. Spencer-Segal, et al.. (2005). Nova regulates brain-specific splicing to shape the synapse. Nature Genetics. 37(8). 844–852. 390 indexed citations
14.
Cline, Melissa, John E. Blume, Simon Cawley, et al.. (2005). ANOSVA: a statistical method for detecting splice variation from expression data. Computer applications in the biosciences. 21(Suppl 1). i107–i115. 42 indexed citations
15.
Karchin, Rachel, Melissa Cline, & Kevin Karplus. (2004). Evaluation of local structure alphabets based on residue burial. Proteins Structure Function and Bioinformatics. 55(3). 508–518. 54 indexed citations
16.
Karchin, Rachel, Melissa Cline, Yael Mandel‐Gutfreund, & Kevin Karplus. (2003). Hidden Markov models that use predicted local structure for fold recognition: Alphabets of backbone geometry. Proteins Structure Function and Bioinformatics. 51(4). 504–514. 159 indexed citations
17.
Wang, Hui, Earl Hubbell, Gangwu Mei, et al.. (2003). Gene structure-based splice variant deconvolution using a microarry platform. Bioinformatics. 19(suppl_1). i315–i322. 82 indexed citations
18.
Cline, Melissa, et al.. (2002). Information‐theoretic dissection of pairwise contact potentials. Proteins Structure Function and Bioinformatics. 49(1). 7–14. 44 indexed citations
19.
Karplus, Kevin, Rachel Karchin, Christian Barrett, et al.. (2001). What is the value added by human intervention in protein structure prediction?. Proteins Structure Function and Bioinformatics. 45(S5). 86–91. 102 indexed citations
20.
Karplus, Kevin, Kimmen Sjölander, Christian Barrett, et al.. (1997). Predicting protein structure using hidden Markov models. Proteins Structure Function and Bioinformatics. 29(S1). 134–139. 5 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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