DREAMS: deep read-level error model for sequencing data applied to
Por um escritor misterioso
Last updated 07 julho 2024
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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.
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DREAMS: Deep Read-level Error Model for Sequencing data applied to
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DREAMS: Deep Read-level Error Model for Sequencing data applied to
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The Science of Mind Reading
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Analysis of error profiles in deep next-generation sequencing data
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The Best GPUs for Deep Learning in 2023 — An In-depth Analysis
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Activation Functions in Neural Networks: 15 examples
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Life, Free Full-Text
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Benchmarking of computational error-correction methods for next
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DREAMS: Deep Read-level Error Model for Sequencing data applied to
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AI-Generated Art: From Text to Images & Beyond [Examples]
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DREAMS: Deep Read-level Error Model for Sequencing data applied to
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Stop Stealing Dreams. if you don't underestimate me, I…
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DREAMS: deep read-level error model for sequencing data applied to
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Diagnostics, Free Full-Text
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