Publications

Dissertations

 Ehsaneddin Asgari
UC Berkeley, 2019

Journal Papers

   
 
Asgari, E., Poerner, N., McHardy, A., & Mofrad, M.
DeepPrime2Sec: Deep Learning for Protein Secondary Structure Prediction from the Primary Sequences.
bioRxiv, 705426 (2019).
Under Review.
 
   
Khaledi A*, Weimann A*, Schniederjans M§, Asgari E§, Kuo T, Gabriel O, Kola A, Gastmeier P, Hogardt M, Jonas D, Mofrad MRK, Bremges A, McHardy AC, Hussler S.
Fighting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics.

EMBO Molecular Medicine 2020 e10264 .
        Zhou, N., Jiang, Y., Bergquist, T. R., Lee, A. J., Kacsoh, B. Z., Crocker, A. W., … & Davis, L. 

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

Genome Biology 20, 244 (2019) doi:10.1186/s13059-019-1835-8.

 

DiMotif

ProtVecX

    
Asgari, E, McHardy A. C., and Mofrad M. R. K.
Probabilistic variable-length segmentation of protein sequences for discriminative motif mining (DiMotif) and sequence embedding (ProtVecX).
Scientific reports 9.1 (2019): 3577.

DiTaxa

  
Asgari, E, Münch P. C., Lesker T.R., McHardy A. C., and Mofrad M. R. K.
 Nucleotide-pair encoding of 16S rRNA sequences for host phenotype and biomarker detection.
Bioinformatics (2018) bty954, https://doi.org/10.1093/bioinformatics/bty954.
   
Asgari E, Garakani K, McHardy AC and Mofrad M. R. K,
Bioinformatics, Volume 34, Issue 13, 1 July 2018, Pages i32–i42, https://doi.org/10.1093/bioinformatics/bty296
ISMB 2018 Special Issue: microbial informatics track — acceptance rate fo 0.13

BioVec ProtVec GeneVec

   Asgari, E, and  RK Mofrad, MRK.

Continuous distributed representation of biological sequences for deep proteomics and genomics.

PloS ONE 10.11 (2015): e0141287.

Peer-reviewed Conference/Workshop Papers
E. Asgari, F. Braune, B. Roth, C. Ringlstetter, M. R.K. Mofrad,

UniSent: Universal Adaptable Sentiment Lexica for 1000+ Languages.

In Proceedings of  Language Resources and Evaluation Conference (LREC) 2020 , Marseille, France, May 2020.
 Highlighted by MIT Tech. Review Magazine

 

       E. Asgari, H. Schütze ,

Past, Present, Future: A Computational Investigation of the Typology of Tense in 1000 Languages.

In Proceedings of the Empirical Methods on Natural Language Processing (EMNLP),

Copenhagen, Denmark, September 2017. 
BEST PAPER AWARD

Sponsored by:

Google

       E. Asgari, M.R.K. Mofrad,

Comparing Fifty Natural Languages and Twelve Genetic Languages Using Word Embedding Language Divergence (WELD) as a Quantitative Measure of Language Distance,

In Proceedings of the NAACL-HLT Workshop on Multilingual and Cross-lingual Methods in NLP,

San Diego, CA, 2016.
Selected among best submissions

Google

       E. Asgari, M.R.K. Mofrad,

Word Vectors of Biological Sequences and Their Applications in Bioinformatics,

International Machine Learning Conference (ICML) Workshop on Computational Biology, New York City, NY, 2016.
        E. Asgari, S. Nasiriany, M.R.K. Mofrad,

Text Analysis and Automatic Triage of Posts in a Mental Health Forum,

In Proceedings of the NAACL-HLT Workshop on Computational Linguistics and Clinical Psychology, San Diego, CA, 2016.

Book chapters
         E. Asgari and M. R. K Mofrad
Deep Genomics and Proteomics: Language Model-Based Embedding of Biological Sequences and Their Applications in Bioinformatics
Leveraging Biomedical and Healthcare Data (pp. 167-181) 2019. Academic Press.
         H. Adel and E. Asgari and H. Schütze
Overview of Character-Based Models for Natural Language Processing
Lecture Notes in Computer Science book series (LNCS, volume 10761) 2018, 3–16.