DREAMS: deep read-level error model for sequencing data applied to
Por um escritor misterioso
Descrição
Circulating tumor DNA detection using next-generation sequencing (NGS) data of plasma DNA is promising for cancer identification and characterization. However, the tumor signal in the blood is often low and difficult to distinguish from errors. We present DREAMS (Deep Read-level Modelling of Sequencing-errors) for estimating error rates of individual read positions. Using DREAMS, we develop statistical methods for variant calling (DREAMS-vc) and cancer detection (DREAMS-cc). For evaluation, we generate deep targeted NGS data of matching tumor and plasma DNA from 85 colorectal cancer patients. The DREAMS approach performs better than state-of-the-art methods for variant calling and cancer detection.
Using Nucleus and TensorFlow for DNA Sequencing Error Correction
DREAMS: deep read-level error model for sequencing data applied to
Estimation of sequencing error rates in short reads
The Full Guide to Embeddings in Machine Learning
Grammatical Error Correction Using Neural Networks
DREAMS: deep read-level error model for sequencing data applied to
PDF) DREAMS: deep read-level error model for sequencing data
Analysis of error profiles in deep next-generation sequencing data
PDF) DREAMS: deep read-level error model for sequencing data
Recent advances and applications of deep learning methods in
Reimagining our futures together: a new social contract for education
Deep learning - Wikipedia
Active Site Sequence Representations of Human Kinases Outperform
de
por adulto (o preço varia de acordo com o tamanho do grupo)