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Träfflista för sökning "WFRF:(Cortés Ciriano Isidro) "

Sökning: WFRF:(Cortés Ciriano Isidro)

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1.
  • Sieverling, Lina, et al. (författare)
  • Genomic footprints of activated telomere maintenance mechanisms in cancer
  • 2020
  • Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 11
  • Tidskriftsartikel (refereegranskat)abstract
    • Cancers require telomere maintenance mechanisms for unlimited replicative potential. They achieve this through TERT activation or alternative telomere lengthening associated with ATRX or DAXX loss. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we dissect whole-genome sequencing data of over 2500 matched tumor-control samples from 36 different tumor types aggregated within the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium to characterize the genomic footprints of these mechanisms. While the telomere content of tumors with ATRX or DAXX mutations (ATRX/DAXXtrunc) is increased, tumors with TERT modifications show a moderate decrease of telomere content. One quarter of all tumor samples contain somatic integrations of telomeric sequences into non-telomeric DNA. This fraction is increased to 80% prevalence in ATRX/DAXXtrunc tumors, which carry an aberrant telomere variant repeat (TVR) distribution as another genomic marker. The latter feature includes enrichment or depletion of the previously undescribed singleton TVRs TTCGGG and TTTGGG, respectively. Our systematic analysis provides new insight into the recurrent genomic alterations associated with telomere maintenance mechanisms in cancer.
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2.
  • Svensson, Fredrik, et al. (författare)
  • Conformal Regression for Quantitative Structure-Activity Relationship Modeling-Quantifying Prediction Uncertainty
  • 2018
  • Ingår i: Journal of Chemical Information and Modeling. - Washington DC : American Chemical Society (ACS). - 1549-9596 .- 1549-960X. ; 58:5, s. 1132-1140
  • Tidskriftsartikel (refereegranskat)abstract
    • Making predictions with an associated confidence is highly desirable as it facilitates decision making and resource prioritization. Conformal regression is a machine learning framework that allows the user to define the required confidence and delivers predictions that are guaranteed to be correct to the selected extent. In this study, we apply conformal regression to model molecular properties and bioactivity values and investigate different ways to scale the outputted prediction intervals to create as efficient (i.e. narrow) regressors as possible. Different algorithms to estimate the prediction uncertainty were used to normalize the prediction ranges and the different approaches were evaluated on 29 publicly available datasets. Our results show that the most efficient conformal regressors are obtained when using the natural exponential of the ensemble standard deviation from the underlying random forest to scale the prediction intervals. This approach afforded an average prediction range of 1.65 pIC50 units at the 80 % confidence level when applied to bioactivity modeling. The choice of nonconformity function has a pronounced impact on the average prediction range with a difference of close to one log unit in bioactivity between the tightest and widest prediction range. Overall, conformal regression is a robust approach to generate bioactivity predictions with associated confidence.
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