Julia Lasserre

1.8k total citations · 1 hit paper
9 papers, 941 citations indexed

About

Julia Lasserre is a scholar working on Molecular Biology, Computer Vision and Pattern Recognition and Artificial Intelligence. According to data from OpenAlex, Julia Lasserre has authored 9 papers receiving a total of 941 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Molecular Biology, 2 papers in Computer Vision and Pattern Recognition and 2 papers in Artificial Intelligence. Recurrent topics in Julia Lasserre's work include Genomics and Chromatin Dynamics (4 papers), Epigenetics and DNA Methylation (3 papers) and Cancer-related gene regulation (2 papers). Julia Lasserre is often cited by papers focused on Genomics and Chromatin Dynamics (4 papers), Epigenetics and DNA Methylation (3 papers) and Cancer-related gene regulation (2 papers). Julia Lasserre collaborates with scholars based in Germany, United States and United Kingdom. Julia Lasserre's co-authors include Martin Vingron, Ho‐Ryun Chung, Kristian Vlahoviček, Rosa Karlić, C.M. Bishop, Thomas P. Minka, Annalisa Marsico, Ulf Andersson Ørom, Hai Hu and Matthew R. Huska and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Bioinformatics.

In The Last Decade

Julia Lasserre

9 papers receiving 919 citations

Hit Papers

Histone modification levels are predictive for gene expre... 2010 2026 2015 2020 2010 100 200 300 400 500
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Histone modification levels are predictive for gene expression
    2010 532 citations Rosa Karlić, Ho‐Ryun Chung et al. Proceedings of the National Academy of Sciences profile →

Peers

Julia Lasserre
Patrick E. Meyer Germany
Mikel Hernáez United States
Jiangwen Sun United States
Richard C. McEachin United States
Zichuan Liu China
Hagit Shatkay United States
Il‐Youp Kwak South Korea
Jinsong Cai China
Tao Meng China
Shiquan Sun China
Patrick E. Meyer Germany
Julia Lasserre
Citations per year, relative to Julia Lasserre Julia Lasserre (= 1×) peers Patrick E. Meyer

Countries citing papers authored by Julia Lasserre

Since Specialization
Citations

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

Fields of papers citing papers by Julia Lasserre

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Julia Lasserre

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

All Works

9 of 9 papers shown
1.
Lasserre, Julia, et al.. (2015). Reconstruction of gene networks using prior knowledge. BMC Systems Biology. 9(1). 84–84. 14 indexed citations
2.
Perner, Juliane, Julia Lasserre, Sarah Kinkley, Martin Vingron, & Ho‐Ryun Chung. (2014). Inference of interactions between chromatin modifiers and histone modifications: from ChIP-Seq data to chromatin-signaling. Nucleic Acids Research. 42(22). 13689–13695. 14 indexed citations
3.
Lasserre, Julia, Ho‐Ryun Chung, & Martin Vingron. (2013). Finding Associations among Histone Modifications Using Sparse Partial Correlation Networks. PLoS Computational Biology. 9(9). e1003168–e1003168. 23 indexed citations
4.
Marsico, Annalisa, Matthew R. Huska, Julia Lasserre, et al.. (2013). PROmiRNA: a new miRNA promoter recognition method uncovers the complex regulation of intronic miRNAs. Genome biology. 14(8). R84–R84. 91 indexed citations
5.
Göke, Jonathan, Marcel H. Schulz, Julia Lasserre, & Martin Vingron. (2012). Estimation of pairwise sequence similarity of mammalian enhancers with word neighbourhood counts. Bioinformatics. 28(5). 656–663. 36 indexed citations
6.
Lasserre, Julia, Steffen Arnold, Martin Vingron, Petra Reinke, & Carl Hinrichs. (2011). Predicting the outcome of renal transplantation. Journal of the American Medical Informatics Association. 19(2). 255–262. 32 indexed citations
7.
Karlić, Rosa, Ho‐Ryun Chung, Julia Lasserre, Kristian Vlahoviček, & Martin Vingron. (2010). Histone modification levels are predictive for gene expression. Proceedings of the National Academy of Sciences. 107(7). 2926–2931. 532 indexed citations breakdown →
8.
Lasserre, Julia, Anitha Kannan, & John Winn. (2007). Hybrid learning of large jigsaws. 1–8. 6 indexed citations
9.
Lasserre, Julia, C.M. Bishop, & Thomas P. Minka. (2006). Principled Hybrids of Generative and Discriminative Models. 1. 87–94. 193 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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