Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Tijana Milenković"'
Publikováno v:
BMC Bioinformatics, Vol 22, Iss 1, Pp 1-26 (2021)
Abstract Background This study focuses on the task of supervised prediction of aging-related genes from -omics data. Unlike gene expression methods for this task that capture aging-specific information but ignore interactions between genes (i.e., the
Externí odkaz:
https://doaj.org/article/a4fbac9781194573a4c72316a1b28f48
Data-driven biological network alignment that uses topological, sequence, and functional information
Autor:
Shawn Gu, Tijana Milenković
Publikováno v:
BMC Bioinformatics, Vol 22, Iss 1, Pp 1-24 (2021)
Abstract Background Network alignment (NA) can transfer functional knowledge between species’ conserved biological network regions. Traditional NA assumes that it is topological similarity (isomorphic-like matching) between network regions that cor
Externí odkaz:
https://doaj.org/article/f635ac9f23064be1bfecc74a08ef42a2
Autor:
Shikang Liu, David Hachen, Omar Lizardo, Christian Poellabauer, Aaron Striegel, Tijana Milenković
Publikováno v:
Applied Network Science, Vol 3, Iss 1, Pp 1-26 (2018)
Abstract Understanding the relationship between individuals’ social networks and health could help devise public health interventions for reducing incidence of unhealthy behaviors or increasing prevalence of healthy ones. In this context, we explor
Externí odkaz:
https://doaj.org/article/93be77b1563b44eea54c0c46a1699852
Publikováno v:
Royal Society Open Science, Vol 7, Iss 6 (2020)
Experimental determination of protein function is resource-consuming. As an alternative, computational prediction of protein function has received attention. In this context, protein structural classification (PSC) can help, by allowing for determini
Externí odkaz:
https://doaj.org/article/66dd788a06d841858dc74dc6458454f1
Autor:
Shawn Gu, Tijana Milenković
Publikováno v:
PLoS ONE, Vol 15, Iss 7, p e0234978 (2020)
In this study, we deal with the problem of biological network alignment (NA), which aims to find a node mapping between species' molecular networks that uncovers similar network regions, thus allowing for the transfer of functional knowledge between
Externí odkaz:
https://doaj.org/article/905049f8f9fb446e8b1c604d59872ebd
Analysis of computational codon usage models and their association with translationally slow codons.
Autor:
Gabriel Wright, Anabel Rodriguez, Jun Li, Patricia L Clark, Tijana Milenković, Scott J Emrich
Publikováno v:
PLoS ONE, Vol 15, Iss 4, p e0232003 (2020)
Improved computational modeling of protein translation rates, including better prediction of where translational slowdowns along an mRNA sequence may occur, is critical for understanding co-translational folding. Because codons within a synonymous co
Externí odkaz:
https://doaj.org/article/4a07a1fc614144eda11ceb5db7cba588
Autor:
Joseph Crawford, Tijana Milenković
Publikováno v:
PLoS ONE, Vol 13, Iss 5, p e0195993 (2018)
Network clustering is a very popular topic in the network science field. Its goal is to divide (partition) the network into groups (clusters or communities) of "topologically related" nodes, where the resulting topology-based clusters are expected to
Externí odkaz:
https://doaj.org/article/e7fbc41edc534512bc3ba4e47aec3fb4
Publikováno v:
Cancer Informatics, Vol 2010, Iss 9, Pp 121-137 (2010)
Important biological information is encoded in the topology of biological networks. Comparative analyses of biological networks are proving to be valuable, as they can lead to transfer of knowledge between species and give deeper insights into biolog
Externí odkaz:
https://doaj.org/article/ffb3e2f216094c7c9d38dcf89ef51b2a
Autor:
Nataša Pržulj, Tijana Milenković
Publikováno v:
Cancer Informatics, Vol 6, Pp 257-273 (2008)
Motivation: Proteins are essential macromolecules of life and thus understanding their function is of great importance. The number of functionally unclassified proteins is large even for simple and well studied organisms such as baker’s yeast. Met
Externí odkaz:
https://doaj.org/article/0336fbb51635463fabcccacadac68b58
Publikováno v:
PLoS ONE, Vol 9, Iss 3, p e90073 (2014)
Protein interaction networks (PINs) are often used to "learn" new biological function from their topology. Since current PINs are noisy, their computational de-noising via link prediction (LP) could improve the learning accuracy. LP uses the existing
Externí odkaz:
https://doaj.org/article/58dfd8fdee8747f69354e650b9749c63