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Ehsaneddin Asgari

Researcher at University of California, Berkeley

Publications -  38
Citations -  1535

Ehsaneddin Asgari is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Language model & Natural language. The author has an hindex of 13, co-authored 38 publications receiving 945 citations. Previous affiliations of Ehsaneddin Asgari include École Polytechnique Fédérale de Lausanne & Lawrence Berkeley National Laboratory.

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Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics.

TL;DR: A new representation and feature extraction method for biological sequences that can be utilized in a wide array of bioinformatics investigations such as family classification, protein visualization, structure prediction, disordered protein identification, and protein-protein interaction prediction is introduced.
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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

Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics.

TL;DR: This study sequenced the genomes and transcriptomes of 414 drug‐resistant clinical Pseudomonas aeruginosa isolates and identified biomarkers of resistance to four commonly administered antimicrobial drugs, paving the way for the development of a molecular resistance profiling tool that reliably predicts antimicrobial susceptibility based on genomic and transcriptomic markers.
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MicroPheno: predicting environments and host phenotypes from 16S rRNA gene sequencing using a k-mer based representation of shallow sub-samples

TL;DR: A reference‐ and alignment‐free approach for predicting environments and host phenotypes from 16S rRNA gene sequencing based on k‐mer representations that benefits from a bootstrapping framework for investigating the sufficiency of shallow sub‐samples is described.