Zobrazeno 1 - 10
of 82
pro vyhledávání: '"Paul D. W. Kirk"'
Publikováno v:
BMC Bioinformatics, Vol 24, Iss 1, Pp 1-31 (2023)
Abstract In this paper we propose PIICM, a probabilistic framework for dose–response prediction in high-throughput drug combination datasets. PIICM utilizes a permutation invariant version of the intrinsic co-regionalization model for multi-output
Externí odkaz:
https://doaj.org/article/626816bf9203485cb456661060a083f8
Autor:
Oliver M. Crook, Colin T. R. Davies, Lisa M. Breckels, Josie A. Christopher, Laurent Gatto, Paul D. W. Kirk, Kathryn S. Lilley
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-21 (2022)
Changes in protein subcellular localization can be determined using mass spectrometry. Here, the authors present a statistical approach to determine relocalising proteins from spatial proteomics experiments.
Externí odkaz:
https://doaj.org/article/2ab3daf7eb8d40e0ab4ad9e1fbd4cdd3
Publikováno v:
BMC Bioinformatics, Vol 23, Iss 1, Pp 1-21 (2022)
Abstract Background Cluster analysis is an integral part of precision medicine and systems biology, used to define groups of patients or biomolecules. Consensus clustering is an ensemble approach that is widely used in these areas, which combines the
Externí odkaz:
https://doaj.org/article/b0c24ad7c251418684e4ba8d91a1327f
Publikováno v:
Communications Biology, Vol 4, Iss 1, Pp 1-15 (2021)
Fang et al. develop a Bayesian data analysis approach that is better suited to the analysis of Thermal Proteome Profiling (TPP) data than existing data analysis approaches that have limitations with respect to deviations from the expected sigmoid dat
Externí odkaz:
https://doaj.org/article/7779bba951a04cb6ad7afed9774db400
Autor:
Sungsam Gong, Francesca Gaccioli, Justyna Dopierala, Ulla Sovio, Emma Cook, Pieter-Jan Volders, Lennart Martens, Paul D. W. Kirk, Sylvia Richardson, Gordon C. S. Smith, D. Stephen Charnock-Jones
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-17 (2021)
Placental dysfunction can have catastrophic or barely discernible effects ranging from miscarriage to apparently normal birth. Here the authors present a comprehensive analysis of the human placental transcriptome and identify circular RNAs and piRNA
Externí odkaz:
https://doaj.org/article/78ea657a313c40d78515e1e336473167
Autor:
Christopher N. Foley, James R. Staley, Philip G. Breen, Benjamin B. Sun, Paul D. W. Kirk, Stephen Burgess, Joanna M. M. Howson
Publikováno v:
Nature Communications, Vol 12, Iss 1, Pp 1-18 (2021)
Statistical colocalisation is a method to identify causal genes and shared genetic aetiology across traits. Here, the authors describe HyPrColoc, an efficient Bayesian divisive clustering algorithm which integrates summary statistics from genome-wide
Externí odkaz:
https://doaj.org/article/3c6045c88c554f35b4d4238ae36ad69a
Publikováno v:
Biology Open, Vol 4, Iss 11, Pp 1420-1426 (2015)
Within populations of cells, fate decisions are controlled by an indeterminate combination of cell-intrinsic and cell-extrinsic factors. In the case of stem cells, the stem cell niche is believed to maintain ‘stemness’ through communication and i
Externí odkaz:
https://doaj.org/article/b3b09e43983c4bb2bc59e47eeef65a82
Publikováno v:
PLoS Genetics, Vol 18, Iss 1, p e1009975 (2022)
Clustering genetic variants based on their associations with different traits can provide insight into their underlying biological mechanisms. Existing clustering approaches typically group variants based on the similarity of their association estima
Externí odkaz:
https://doaj.org/article/b817c2a6bf92477caa9509a6334583ed
Publikováno v:
The annals of applied statistics. 16(4)
Understanding sub-cellular protein localisation is an essential component in the analysis of context specific protein function. Recent advances in quantitative mass-spectrometry (MS) have led to high resolution mapping of thousands of proteins to sub
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1f9df3638499f6e668758ec1aaaf76c7
https://pubmed.ncbi.nlm.nih.gov/36507469
https://pubmed.ncbi.nlm.nih.gov/36507469
Publikováno v:
Biostatistics 24(1), 85-107 (2022). doi:10.1093/biostatistics/kxab023
Biostatistics
Karapanagiotis, S, Benedetto, U, Mukherjee, S, Kirk, P D W & Newcombe, P J 2021, ' Tailored Bayes : a risk modeling framework under unequal misclassification costs ', Biostatistics . https://doi.org/10.1093/biostatistics/kxab023
Biostatistics
Karapanagiotis, S, Benedetto, U, Mukherjee, S, Kirk, P D W & Newcombe, P J 2021, ' Tailored Bayes : a risk modeling framework under unequal misclassification costs ', Biostatistics . https://doi.org/10.1093/biostatistics/kxab023
Summary Risk prediction models are a crucial tool in healthcare. Risk prediction models with a binary outcome (i.e., binary classification models) are often constructed using methodology which assumes the costs of different classification errors are
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5d5414939b7e3c4c6596c530d30e58fb
https://www.repository.cam.ac.uk/handle/1810/326927
https://www.repository.cam.ac.uk/handle/1810/326927
