English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Diagnosis and risk stratification of cancer and many other diseases requirethe detection of genomic breakpoints as a prerequisite of calling copy numberalterations (CNA). This, however, is still challenging and requirestime-consuming manual curation. As deep-learning methods outperformed classicalstate-of-the-art algorithms in various domains and have also been successfullyapplied to life science problems including medicine and biology, we herepropose Deep SNP, a novel Deep Neural Network to learn from genomic data.Specifically, we used a manually curated dataset from 12 genomic singlenucleotide polymorphism array (SNPa) profiles as truth-set and aimed atpredicting the presence or absence of genomic breakpoints, an indicator ofstructural chromosomal variations, in windows of 40,000 probes. We compare ourresults with well-known neural network models as well as Rawcopy though thistool is designed to predict breakpoints and in addition genomic segments withhigh sensitivity. We show, that Deep SNP is capable of successfully predictingthe presence or absence of a breakpoint in large genomic windows andoutperforms state-of-the-art neural network models. Qualitative examplessuggest that integration of a localization unit may enable breakpoint detectionand prediction of genomic segments, even if the breakpoint coordinates were notprovided for network training. These results warrant further evaluation ofDeepSNP for breakpoint localization and subsequent calling of genomic segments.

Deep SNP: An End-to-end Deep Neural Network with Attention-based Localization for Break-point Detection in SNP Array Genomic data