Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Marinka Žitnik"'
Publikováno v:
BioData Mining, Vol 10, Iss 1, Pp 1-16 (2017)
Abstract Background Matrix factorization is a well established pattern discovery tool that has seen numerous applications in biomedical data analytics, such as gene expression co-clustering, patient stratification, and gene-disease association mining
Externí odkaz:
https://doaj.org/article/1d3af326feb2487f9bbf5409188124f1
Publikováno v:
PLoS Computational Biology, Vol 11, Iss 10, p e1004552 (2015)
Data integration procedures combine heterogeneous data sets into predictive models, but they are limited to data explicitly related to the target object type, such as genes. Collage is a new data fusion approach to gene prioritization. It considers d
Externí odkaz:
https://doaj.org/article/3db88e6e2baf476282ff43473e8cb746
Publikováno v:
Bioinformatics
Motivation: RNA binding proteins (RBPs) play important roles in post-transcriptional control of gene expression, including splicing, transport, polyadenylation and RNA stability. To model protein–RNA interactions by considering all available source
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9aee59d3341e5189fbf77202ab6dca4a
https://doi.org/10.1093/bioinformatics/btw003
https://doi.org/10.1093/bioinformatics/btw003
Publikováno v:
Scientific Data, Vol 10, Iss 1, Pp 1-18 (2023)
Abstract As explanations are increasingly used to understand the behavior of graph neural networks (GNNs), evaluating the quality and reliability of GNN explanations is crucial. However, assessing the quality of GNN explanations is challenging as exi
Externí odkaz:
https://doaj.org/article/993af0bc7ead4318aa8097ae16a3be21
Autor:
Marinka Žitnik, Blaž Zupan
Publikováno v:
Journal of Computational Biology. 22:595-608
Epistatic miniarray profile (E-MAP) is a popular large-scale genetic interaction discovery platform. E-MAPs benefit from quantitative output, which makes it possible to detect subtle interactions with greater precision. However, due to the limits of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0131dc7339b48e3112038c30a97d4f01
https://doi.org/10.1089/cmb.2014.0158
https://doi.org/10.1089/cmb.2014.0158
Autor:
Yasemin Altun, Kamalika Das, Taneli Mielikäinen, Donato Malerba, Jerzy Stefanowski, Jesse Read, Marinka Žitnik, Michelangelo Ceci, Sašo Džeroski
The three volume proceedings LNAI 10534 – 10536 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2017, held in Skopje, Macedonia, in September 2017. The total of 101
Publikováno v:
Scientific Data, Vol 10, Iss 1, Pp 1-16 (2023)
Measurement(s) knowledge graph • Relation Code • textual entity Technology Type(s) machine learning • computational modeling technique
Externí odkaz:
https://doaj.org/article/a173e11db88c48ca96c21a86ff2deee1
Autor:
Marinka Žitnik, Blaž Zupan
Publikováno v:
Bioinformatics
Motivation: Epistasis analysis is an essential tool of classical genetics for inferring the order of function of genes in a common pathway. Typically, it considers single and double mutant phenotypes and for a pair of genes observes whether a change
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bf30d46a3637a9e0e1d5b38609cce318
https://doi.org/10.1093/bioinformatics/btu287
https://doi.org/10.1093/bioinformatics/btu287
Autor:
Marinka Žitnik, Blaž Zupan
Publikováno v:
Systems Biomedicine. 2:16-22
Traditional studies of liver toxicity involve screening compounds through in vivo and in vitro tests. They need to distinguish between compounds that represent little or no health concern and those with the greatest likelihood to cause adverse effect
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7629b970a8ab55fded06b523609c4fe5
https://doi.org/10.4161/sysb.29072
https://doi.org/10.4161/sysb.29072
Autor:
Hossein Honarvar, PhD, Chirag Agarwal, PhD, Sulaiman Somani, MD, Akhil Vaid, MD, Joshua Lampert, MD, Tingyi Wanyan, PhD, Vivek Y. Reddy, MD, Girish N. Nadkarni, MD, Riccardo Miotto, PhD, Marinka Zitnik, PhD, Fei Wang, PhD, Benjamin S. Glicksberg, PhD
Publikováno v:
Cardiovascular Digital Health Journal, Vol 3, Iss 5, Pp 220-231 (2022)
Background: Electrocardiogram (ECG) deep learning (DL) has promise to improve the outcomes of patients with cardiovascular abnormalities. In ECG DL, researchers often use convolutional neural networks (CNNs) and traditionally use the full duration of
Externí odkaz:
https://doaj.org/article/7106b3c922994d84a741ddc460870603