Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Laetitia Papaxanthos"'
Autor:
Simon Höllerer, Laetitia Papaxanthos, Anja Cathrin Gumpinger, Katrin Fischer, Christian Beisel, Karsten Borgwardt, Yaakov Benenson, Markus Jeschek
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-15 (2020)
Current methods to generate sequence-function data at large scale are either technically complex or limited to specific applications. Here the authors introduce DNA-based phenotypic recording to overcome these limitations and enable deep learning for
Externí odkaz:
https://doaj.org/article/5cdab11544394bd78e7fc3880bbaa8e2
Autor:
Anja C Gumpinger, Karsten M. Borgwardt, Christian Beisel, Katrin Fischer, Laetitia Papaxanthos, Markus Jeschek, Yaakov Benenson, Simon Hoellerer
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-15 (2020)
Nature Communications
Nature Communications, 11 (1)
Nature Communications
Nature Communications, 11 (1)
Predicting effects of gene regulatory elements (GREs) is a longstanding challenge in biology. Machine learning may address this, but requires large datasets linking GREs to their quantitative function. However, experimental methods to generate such d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::924dd0ac979d603ba9dd553fdd6f2237
https://doaj.org/article/5cdab11544394bd78e7fc3880bbaa8e2
https://doaj.org/article/5cdab11544394bd78e7fc3880bbaa8e2
Single-cell omics technologies produce large quantities of data describing the genomic, transcriptomic or epigenomic profiles of many individual cells in parallel. In order to infer biological knowledge and develop predictive models from these data,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b644fba1f72d9331126068d75aa4b07d
https://doi.org/10.1101/2021.02.04.429763
https://doi.org/10.1101/2021.02.04.429763
Autor:
Dean A. Bodenham, Damian Roqueiro, Felipe Llinares-López, Karsten M. Borgwardt, Laetitia Papaxanthos
Publikováno v:
Bioinformatics, 35 (15)
Bioinformatics
Bioinformatics
Combinatorial association mapping aims to assess the statistical association of higher-order interactions of genetic markers with a phenotype of interest. This article presents combinatorial association mapping (CASMAP), a software package that lever
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::efb5466df32fa4f9e0d87ad2934b073a
https://doi.org/10.1093/bioinformatics/bty1020
https://doi.org/10.1093/bioinformatics/bty1020
Publikováno v:
ACL (1)
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019)
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019)
We consider the task of inferring is-a relationships from large text corpora. For this purpose, we propose a new method combining hyperbolic embeddings and Hearst patterns. This approach allows us to set appropriate constraints for inferring concept
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1073cc73be8d74e64512274f06a146f9
https://doi.org/10.18653/v1/p19-1313
https://doi.org/10.18653/v1/p19-1313
Autor:
Felipe Llinares-López, Damian Roqueiro, Dean A. Bodenham, Laetitia Papaxanthos, Karsten M. Borgwardt, COPDGene Investigators
Publikováno v:
Bioinformatics
Bioinformatics, 33 (12)
Bioinformatics, 33 (12)
Motivation Genetic heterogeneity is the phenomenon that distinct genetic variants may give rise to the same phenotype. The recently introduced algorithm Fast Automatic Interval Search (FAIS) enables the genome-wide search of candidate regions for gen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a67d48adb54823e2c705b8a5cb9987d1
Publikováno v:
KDD
KDD '15: The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
KDD '15: The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
We present a novel algorithm for significant pattern mining, Westfall-Young light. The target patterns are statistically significantly enriched in one of two classes of objects. Our method corrects for multiple hypothesis testing and correlations bet
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c110feb4db0d50c39826beb9e42216f7