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Evaluation of cell-free DNA approaches for multi-cancer early detection.
Cancer Cell
December 2022
Francis Crick Institute, London, NW1 1AT, UK; UCL Cancer Institute, CRUK Lung Cancer Centre of Excellence, London, WC1E 6DD, UK.
In the Circulating Cell-free Genome Atlas (NCT02889978) substudy 1, we evaluate several approaches for a circulating cell-free DNA (cfDNA)-based multi-cancer early detection (MCED) test by defining clinical limit of detection (LOD) based on circulating tumor allele fraction (cTAF), enabling performance comparisons. Among 10 machine-learning classifiers trained on the same samples and independently validated, when evaluated at 98% specificity, those using whole-genome (WG) methylation, single nucleotide variants with paired white blood cell background removal, and combined scores from classifiers evaluated in this study show the highest cancer signal detection sensitivities. Compared with clinical stage and tumor type, cTAF is a more significant predictor of classifier performance and may more closely reflect tumor biology.
View Article and Find Full Text PDFSensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA.
Ann Oncol
June 2020
US Oncology Research, US Oncology, The Woodlands, USA. Electronic address:
Background: Early cancer detection could identify tumors at a time when outcomes are superior and treatment is less morbid. This prospective case-control sub-study (from NCT02889978 and NCT03085888) assessed the performance of targeted methylation analysis of circulating cell-free DNA (cfDNA) to detect and localize multiple cancer types across all stages at high specificity.
Participants And Methods: The 6689 participants [2482 cancer (>50 cancer types), 4207 non-cancer] were divided into training and validation sets.
Data Shared Lasso: A Novel Tool to Discover Uplift.
Comput Stat Data Anal
September 2016
Department of Statistics, Stanford University, Stanford, CA.
A model is presented for the supervised learning problem where the observations come from a fixed number of pre-specified groups, and the regression coefficients may vary sparsely between groups. The model spans the continuum between individual models for each group and one model for all groups. The resulting algorithm is designed with a high dimensional framework in mind.
View Article and Find Full Text PDFGenome-wide characteristics of mutations in autism.
NPJ Genom Med
August 2016
The Centre for Applied Genomics, Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada; Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada; McLaughlin Centre, University of Toronto, Toronto, Ontario, Canada.
mutations (DNMs) are important in Autism Spectrum Disorder (ASD), but so far analyses have mainly been on the ~1.5% of the genome encoding genes. Here, we performed whole genome sequencing (WGS) of 200 ASD parent-child trios and characterized germline and somatic DNMs.
View Article and Find Full Text PDFCollaborative regression.
Biostatistics
April 2015
Department of Statistics, Stanford University, Stanford, CA 94305, USADepartment of Health Research & Policy, Stanford University, Stanford, CA 94305, USA.
We consider the scenario where one observes an outcome variable and sets of features from multiple assays, all measured on the same set of samples. One approach that has been proposed for dealing with these type of data is "sparse multiple canonical correlation analysis" (sparse mCCA). All of the current sparse mCCA techniques are biconvex and thus have no guarantees about reaching a global optimum.
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