scholarly journals Splice Site Prediction Using Artificial Neural Networks

2009 ◽  
pp. 102-113 ◽  
Author(s):  
∅ystein Johansen ◽  
Tom Ryen ◽  
Trygve Eftes∅l ◽  
Thomas Kjosmoen ◽  
Peter Ruoff
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Splice Site ◽  
Splice Site Prediction ◽  
Site Prediction ◽  
Artificial Neural

scholarly journals Splice2Deep: An ensemble of deep convolutional neural networks for improved splice site prediction in genomic DNA

Gene X ◽  
2020 ◽  
Vol 5 ◽  
pp. 100035 ◽  
Author(s):  
Somayah Albaradei ◽  
Arturo Magana-Mora ◽  
Maha Thafar ◽  
Mahmut Uludag ◽  
Vladimir B. Bajic ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Splice Site ◽  
Genomic Dna ◽  
Deep Convolutional Neural Networks ◽  
Splice Site Prediction ◽  
Site Prediction

EDeepSSP: Explainable deep neural networks for exact splice sites prediction

2020 ◽  
Vol 18 (04) ◽  
pp. 2050024
Author(s):  
Santhosh Amilpur ◽  
Raju Bhukya
Keyword(s):  
Neural Networks ◽  
Splice Site ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Splice Sites ◽  
Human Donor ◽  
Splice Site Prediction ◽  
Site Prediction ◽  
Automatic Feature Extraction ◽  
Precision Recall Curve

Splice site prediction is crucial for understanding underlying gene regulation, gene function for better genome annotation. Many computational methods exist for recognizing the splice sites. Although most of the methods achieve a competent performance, their interpretability remains challenging. Moreover, all traditional machine learning methods manually extract features, which is tedious job. To address these challenges, we propose a deep learning-based approach (EDeepSSP) that employs convolutional neural networks (CNNs) architecture for automatic feature extraction and effectively predicts splice sites. Our model, EDeepSSP, divulges the opaque nature of CNN by extracting significant motifs and explains why these motifs are vital for predicting splice sites. In this study, experiments have been conducted on six benchmark acceptors and donor datasets of humans, cress, and fly. The results show that EDeepSSP has outperformed many state-of-the-art approaches. EDeepSSP achieves the highest area under the receiver operating characteristic curve (AUC_ROC) and area under the precision-recall curve (AUC_PR) of 99.32% and 99.26% on human donor datasets, respectively. We also analyze various filter activities, feature activations, and extracted significant motifs responsible for the splice site prediction. Further, we validate the learned motifs of our model against known motifs of JASPAR splice site database.

scholarly journals SpliceRover: interpretable convolutional neural networks for improved splice site prediction

2018 ◽  
Vol 34 (24) ◽  
pp. 4180-4188 ◽  
Author(s):  
Jasper Zuallaert ◽  
Fréderic Godin ◽  
Mijung Kim ◽  
Arne Soete ◽  
Yvan Saeys ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Splice Site ◽  
Splice Site Prediction ◽  
Site Prediction

Determination of S-N curves with the application of artificial neural networks

1999 ◽  
Vol 22 (8) ◽  
pp. 723-728 ◽  
Author(s):  
Artymiak ◽  
Bukowski ◽  
Feliks ◽  
Narberhaus ◽  
Zenner
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Artificial Neural
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