Browsing by subject "Machine Learning"
Now showing items 1-9 of 9
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Flexible Data Analysis Pipeline for High-Confidence Proteogenomics.
(2016-12)Proteogenomics leverages information derived from proteomic data to improve genome annotations. Of particular interest are "novel" peptides that provide direct evidence of protein expression for genomic regions not previously ... -
How to develop a meaningful radiomic signature for clinical use in oncologic patients.
(2020-05)During the last decade, there is an increasing usage of quantitative methods in Radiology in an effort to reduce the diagnostic variability associated with a subjective manner of radiological interpretation. Combined ... -
A Machine Learning Platform to Optimize the Translation of Personalized Network Models to the Clinic.
(2019-04)<h4>Purpose</h4>Dynamic network models predict clinical prognosis and inform therapeutic intervention by elucidating disease-driven aberrations at the systems level. However, the personalization of model predictions requires ... -
The MOBSTER R package for tumour subclonal deconvolution from bulk DNA whole-genome sequencing data.
(2020-11-17)<h4>Background</h4>The large-scale availability of whole-genome sequencing profiles from bulk DNA sequencing of cancer tissues is fueling the application of evolutionary theory to cancer. From a bulk biopsy, subclonal ... -
Noninvasive MRI Native T<sub>1</sub> Mapping Detects Response to <i>MYCN</i>-targeted Therapies in the Th-<i>MYCN</i> Model of Neuroblastoma.
(2020-08)Noninvasive early indicators of treatment response are crucial to the successful delivery of precision medicine in children with cancer. Neuroblastoma is a common solid tumor of young children that arises from anomalies ... -
Normal tissue complication probability (NTCP) modelling using spatial dose metrics and machine learning methods for severe acute oral mucositis resulting from head and neck radiotherapy.
(2016-07)<h4>Background and purpose</h4>Severe acute mucositis commonly results from head and neck (chemo)radiotherapy. A predictive model of mucositis could guide clinical decision-making and inform treatment planning. We aimed ... -
Subclonal reconstruction of tumors by using machine learning and population genetics.
(2020-09-02)Most cancer genomic data are generated from bulk samples composed of mixtures of cancer subpopulations, as well as normal cells. Subclonal reconstruction methods based on machine learning aim to separate those subpopulations ... -
Use of a Novel Nonparametric Version of DEPTH to Identify Genomic Regions Associated with Prostate Cancer Risk.
(2016-12)<h4>Background</h4>We have developed a genome-wide association study analysis method called DEPTH (DEPendency of association on the number of Top Hits) to identify genomic regions potentially associated with disease by ...
