Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Gabriele Partel"'
Autor:
Sergio Marco Salas, Xiao Yuan, Christer Sylven, Mats Nilsson, Carolina Wählby, Gabriele Partel
Publikováno v:
PLoS Computational Biology, Vol 18, Iss 8, p e1010366 (2022)
With the emergence of high throughput single cell techniques, the understanding of the molecular and cellular diversity of mammalian organs have rapidly increased. In order to understand the spatial organization of this diversity, single cell data is
Externí odkaz:
https://doaj.org/article/6dcd79603c5c473eb053649c29d706ec
Autor:
Gabriele Partel, Markus M. Hilscher, Giorgia Milli, Leslie Solorzano, Anna H. Klemm, Mats Nilsson, Carolina Wählby
Publikováno v:
BMC Biology, Vol 18, Iss 1, Pp 1-14 (2020)
Abstract Background Neuroanatomical compartments of the mouse brain are identified and outlined mainly based on manual annotations of samples using features related to tissue and cellular morphology, taking advantage of publicly available reference a
Externí odkaz:
https://doaj.org/article/d8c415f033614600b74b6a6e8c129f0a
Autor:
Noora Andersson, Tiia Kähkönen, Alejandra Cervera, Ville Rantanen, Gabriele Partel, Olli Carpén, Sampsa Hautaniemi, Sakari Hietanen, Rainer Lehtonen, Heidi Rausio, Johanna Hynninen, Giulia Paciello, Elisa Ficarra, Kaisa Huhtinen
Publikováno v:
Bioinformatics
Motivation Fusion genes are both useful cancer biomarkers and important drug targets. Finding relevant fusion genes is challenging due to genomic instability resulting in a high number of passenger events. To reveal and prioritize relevant gene fusio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fcbf9d4c5241537e7824ef4b7fffb208
http://hdl.handle.net/10138/338618
http://hdl.handle.net/10138/338618
With the emergence of high throughput single cell techniques, the understanding of cellular diversity in biologically complex processes has rapidly increased. The next step towards comprehension of e.g. key organs in the mammal development is to obta
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9514c28d41b13581ead89a46d613fbf0
https://doi.org/10.1101/2021.07.10.451822
https://doi.org/10.1101/2021.07.10.451822
Publikováno v:
Bioinformatics
Motivation Visual assessment of scanned tissue samples and associated molecular markers, such as gene expression, requires easy interactive inspection at multiple resolutions. This requires smart handling of image pyramids and efficient distribution
Autor:
Gabriele Partel, Carolina Wählby
Publikováno v:
ICPR
Image-based multiplexed in situ RNA detection makes it possible to map the spatial gene expression of hundreds to thousands of genes in parallel, and thus discern at the same time a large numbers of different cell types to better understand tissue de
Autor:
Carolina Wählby, Gabriele Partel
Investigation of spatial cellular composition of tissue architectures revealed by multiplexed in situ RNA detection often rely on inaccurate cell segmentation or prior biological knowledge from complementary single cell sequencing experiments. Here w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a626c938a927a8e80efd186c628db1ff
Autor:
Carolina Wählby, Gabriele Partel, Mats Nilsson, Leslie Solorzano, Anna H. Klemm, Giorgia Milli, Markus M. Hilscher
Publikováno v:
BMC Biology
BMC Biology, Vol 18, Iss 1, Pp 1-14 (2020)
BMC Biology, Vol 18, Iss 1, Pp 1-14 (2020)
Background Neuroanatomical compartments of the mouse brain are identified and outlined mainly based on manual annotations of samples using features related to tissue and cellular morphology, taking advantage of publicly available reference atlases. H
Publikováno v:
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)
ISBI
ISBI
Deep learning has proven to successfully learn variations in tissue and cell morphology. Training of such models typically relies on expensive manual annotations. Here we conjecture that spatially resolved gene expression, e.i., the transcriptome, ca
Autor:
Håkan Wieslander, Nicolas Pielawski, Carolina Wählby, Leslie Solorzano, Gabriele Partel, Philip J. Harrison, Kimmo Kartasalo, Ola Spjuth, Anindya Gupta, Ida-Maria Sintorn, Amit Suveer, Anna H. Klemm
Publikováno v:
Cytometry
Cytometry Part A
Cytometry Part A
Artificial intelligence, deep convolutional neural networks, and deep learning are all niche terms that are increasingly appearing in scientific presentations as well as in the general media. In this review, we focus on deep learning and how it is ap
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b616149118d669150ab833cf14a5d461
https://trepo.tuni.fi/handle/10024/117020
https://trepo.tuni.fi/handle/10024/117020