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Žiga Avsec

Showing results (1-10 of 16) with videos related to

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Plos Computational Biology|May 10, 2021
Predicting mean ribosome load for 5'UTR of any length using deep learningAlexander Karollus, Žiga Avsec, Julien Gagneur
Bioinformatics (Oxford, England)|November 21, 2017
Modeling positional effects of regulatory sequences with spline transformations increases prediction accuracy of deep neural networksŽiga Avsec, Mohammadamin Barekatain, Jun Cheng, et al.
Nature Reviews. Genetics|April 12, 2019
Deep learning: new computational modelling techniques for genomicsGökcen Eraslan, Žiga Avsec, Julien Gagneur, et al.
RNA (New York, N.Y.)|August 13, 2017
<i>Cis</i>-regulatory elements explain most of the mRNA stability variation across genes in yeastJun Cheng, Kerstin C Maier, Žiga Avsec, et al.
Human Mutation|May 10, 2019
CAGI 5 splicing challenge: Improved exon skipping and intron retention predictions with MMSpliceJun Cheng, Muhammed Hasan Çelik, Thi Yen Duong Nguyen, et al.
Genome Biology|March 3, 2019
MMSplice: modular modeling improves the predictions of genetic variant effects on splicingJun Cheng, Thi Yen Duong Nguyen, Kamil J Cygan, et al.
American Journal of Human Genetics|December 4, 2018
OUTRIDER: A Statistical Method for Detecting Aberrantly Expressed Genes in RNA Sequencing DataFelix Brechtmann, Christian Mertes, Agnė Matusevičiūtė, et al.
Nature Genetics|February 19, 2021
Base-resolution models of transcription-factor binding reveal soft motif syntaxŽiga Avsec, Melanie Weilert, Avanti Shrikumar, et al.
Nature Methods|October 5, 2021
Effective gene expression prediction from sequence by integrating long-range interactionsŽiga Avsec, Vikram Agarwal, Daniel Visentin, et al.
Nature Biotechnology|May 30, 2019
The Kipoi repository accelerates community exchange and reuse of predictive models for genomicsŽiga Avsec, Roman Kreuzhuber, Johnny Israeli, et al.
Pageof 2

Showing results (1-10 of 16) with videos related to

Sort By:
Pageof 2
Plos Computational Biology|May 10, 2021
Predicting mean ribosome load for 5'UTR of any length using deep learningAlexander Karollus, Žiga Avsec, Julien Gagneur
Bioinformatics (Oxford, England)|November 21, 2017
Modeling positional effects of regulatory sequences with spline transformations increases prediction accuracy of deep neural networksŽiga Avsec, Mohammadamin Barekatain, Jun Cheng, et al.
Nature Reviews. Genetics|April 12, 2019
Deep learning: new computational modelling techniques for genomicsGökcen Eraslan, Žiga Avsec, Julien Gagneur, et al.
RNA (New York, N.Y.)|August 13, 2017
<i>Cis</i>-regulatory elements explain most of the mRNA stability variation across genes in yeastJun Cheng, Kerstin C Maier, Žiga Avsec, et al.
Human Mutation|May 10, 2019
CAGI 5 splicing challenge: Improved exon skipping and intron retention predictions with MMSpliceJun Cheng, Muhammed Hasan Çelik, Thi Yen Duong Nguyen, et al.
Genome Biology|March 3, 2019
MMSplice: modular modeling improves the predictions of genetic variant effects on splicingJun Cheng, Thi Yen Duong Nguyen, Kamil J Cygan, et al.
American Journal of Human Genetics|December 4, 2018
OUTRIDER: A Statistical Method for Detecting Aberrantly Expressed Genes in RNA Sequencing DataFelix Brechtmann, Christian Mertes, Agnė Matusevičiūtė, et al.
Nature Genetics|February 19, 2021
Base-resolution models of transcription-factor binding reveal soft motif syntaxŽiga Avsec, Melanie Weilert, Avanti Shrikumar, et al.
Nature Methods|October 5, 2021
Effective gene expression prediction from sequence by integrating long-range interactionsŽiga Avsec, Vikram Agarwal, Daniel Visentin, et al.
Nature Biotechnology|May 30, 2019
The Kipoi repository accelerates community exchange and reuse of predictive models for genomicsŽiga Avsec, Roman Kreuzhuber, Johnny Israeli, et al.
Pageof 2