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ABERRANT SPLICING DETECTION USING CONVOLUTIONAL NEURAL NETWORKS (CNNS)
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Title:
ABERRANT SPLICING DETECTION USING CONVOLUTIONAL NEURAL NETWORKS (CNNS)
Author:
JEREMY FRANCIS MCRAE
;
KISHOR JAGANATHAN
;
SOFIA KYRIAZOPOULOU PANAGIOTOPOULOU
;
FARH KAI-HOW
Subjects:
CALCULATING
;
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
;
COMPUTING
;
COUNTING
;
INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS
;
PHYSICS
Description:
To provide a method, a system, and a computer readable storage medium which train a convolutional neural network work base (CNN) sorter by training data by using inclination updating technology of a reverse propagation base of making output of the sorter match gradually with a corresponding ground truth sign.SOLUTION: A convolutional neural network-based classifier comprises groups of residual blocks. Each group of the residual blocks is parameterized by the number of convolution filters in the residual blocks, a convolution window size of the residual blocks, and an Atrous convolution rate of the residual blocks. Size of a convolution window is different between groups of the residual blocks, and the Atrous convolution rate is different between the groups of the residual blocks. Training data includes benign training examples and pathogenic training examples of translated sequence pairs generated from benign variants and pathogenic variants.SELECTED DRAWING: Figure 31 【課題】分類器の出力を対応するグラウンドトゥルース標識と徐々にマッチさせる逆伝搬ベースの勾配更新技術を使用して、訓練データで畳み込みニューラルネットワークベース(CNN)分類器を訓練する方法、システム及びコンピュータ可読記憶媒体を提供する。【解決手段】畳み込みニューラルネットワークベース分類器は、残差ブロックのグループを含む。残差ブロックの各グループは、残差ブロック内の畳み込みフィルタの数、残差ブロックの畳み込みウィンドウサイズ及び残差ブロックのAtrous畳み込みレートによってパラメータ化される。畳み込みウィンドウのサイズは、残差ブロックのグループの間で異なり、Atrous畳み込みレートは、残差ブロックのグループの間で異なる。訓練データは、良性バリアント及び病原性バリアントから生成された翻訳済み配列対の良性訓練例並びに病原性訓練例を含む。【選択図】図31
Creation Date:
2021
Language:
English;Japanese
Source:
esp@cenet
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