Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Madalina Ciortan"'
Autor:
Madalina Ciortan, Matthieu Defrance
Publikováno v:
BMC Bioinformatics, Vol 22, Iss 1, Pp 1-27 (2021)
Abstract Background Single-cell RNA sequencing (scRNA-seq) has emerged has a main strategy to study transcriptional activity at the cellular level. Clustering analysis is routinely performed on scRNA-seq data to explore, recognize or discover underly
Externí odkaz:
https://doaj.org/article/d6c02bdbb33d43a18a8c57ae0dd8d3ae
Publikováno v:
Data, Vol 6, Iss 6, p 61 (2021)
We propose a framework using contrastive learning as a pre-training task to perform image classification in the presence of noisy labels. Recent strategies, such as pseudo-labeling, sample selection with Gaussian Mixture models, and weighted supervis
Externí odkaz:
https://doaj.org/article/892cbc470ca046ac9ae6ae5cfa05ecf1
Autor:
Quentin Rivière, Massimiliano Corso, Madalina Ciortan, Grégoire Noël, Nathalie Verbruggen, Matthieu Defrance
Publikováno v:
Plant and Cell Physiology. 63:1457-1473
The identification of transcription factor (TF) target genes is central in biology. A popular approach is based on the location by pattern matching of potential cis-regulatory elements (CREs). During the last few years, tools integrating next-generat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a4defd2beefc427f79f36185f7b491ab
https://doi.org/10.1093/pcp/pcac095
https://doi.org/10.1093/pcp/pcac095
Autor:
madalina ciortan, Leo bouscarrat
Publikováno v:
IEEE Potentials. 40:31-35
First, we would like to introduce ourselves. We are two young engineers, Leo and Madalina, in our early 20s and 30s, respectively, and we both made the same strange choice: we started a Ph.D. degree in data science while working in the private sector
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::067c0b3b301dfe8276361b9700733d83
https://doi.org/10.1109/mpot.2021.3071115
https://doi.org/10.1109/mpot.2021.3071115
Autor:
Madalina Ciortan, Matthieu Defrance
Single-cell RNA sequencing (scRNA-seq) produces transcriptomic profiling for individual cells. Due to the lack of cell-class annotations, scRNA-seq is routinely analyzed with unsupervised clustering methods. Because these methods are typically limite
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f992949f7b802c56a0cbf4b23a441734
https://doi.org/10.1101/2021.11.15.468416
https://doi.org/10.1101/2021.11.15.468416
Autor:
Madalina Ciortan, Matthieu Defrance
Publikováno v:
The 12th International Conference on Computational Systems-Biology and Bioinformatics.
Subspace clustering identifies multiple feature subspaces embedded in a dataset together with the underlying sample clusters. When applied to omic data, subspace clustering is a challenging task, as additional problems have to be addressed: the curse
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c916c5addbfb89cf3f13c5fd19555e34
https://doi.org/10.1145/3486713.3486742
https://doi.org/10.1145/3486713.3486742
Autor:
Madalina Ciortan, Matthieu Defrance
Publikováno v:
Bioinformatics (Oxford, England).
Motivation Single-cell RNA sequencing (scRNA-seq) provides transcriptomic profiling for individual cells, allowing researchers to study the heterogeneity of tissues, recognize rare cell identities and discover new cellular subtypes. Clustering analys
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dbb82e9cf411f847ca3123e8632b8443
https://pubmed.ncbi.nlm.nih.gov/34850828
https://pubmed.ncbi.nlm.nih.gov/34850828
Publikováno v:
Data, Vol 6, Iss 61, p 61 (2021)
Data
Volume 6
Issue 6
Data
Volume 6
Issue 6
We propose a framework using contrastive learning as a pre-training task to perform image classification in the presence of noisy labels. Recent strategies such as pseudo-labeling, sample selection with Gaussian Mixture models, weighted supervised co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::66245112fc99539e4ace770308b16228
http://arxiv.org/abs/2104.09563
http://arxiv.org/abs/2104.09563