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Tomislav Šmuc

Researcher at Laureate International Universities

Publications -  105
Citations -  7524

Tomislav Šmuc is an academic researcher from Laureate International Universities. The author has contributed to research in topics: Support vector machine & Cluster analysis. The author has an hindex of 23, co-authored 103 publications receiving 5909 citations.

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REVIGO Summarizes and Visualizes Long Lists of Gene Ontology Terms

TL;DR: REVIGO is a Web server that summarizes long, unintelligible lists of GO terms by finding a representative subset of the terms using a simple clustering algorithm that relies on semantic similarity measures.
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A large-scale evaluation of computational protein function prediction

Predrag Radivojac, +107 more
- 01 Mar 2013 - 
TL;DR: Today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets, and there is considerable need for improvement of currently available tools.
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The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

Naihui Zhou, +188 more
- 19 Nov 2019 - 
TL;DR: The third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed, concluded that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not.
Posted ContentDOI

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

Naihui Zhou, +181 more
- 29 May 2019 - 
TL;DR: It is reported that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bioontologies, working together to improve functional annotation, computational function prediction, and the ability to manage big data in the era of large experimental screens.
Journal ArticleDOI

Identification of Patient Zero in Static and Temporal Networks: Robustness and Limitations

TL;DR: The statistical inference problem of detecting the source of epidemics from a snapshot of spreading on an arbitrary network structure is studied and the detectability limits for the susceptible-infected-recovered model, which primarily depend on the spreading process characteristics are demonstrated.