Journal Article10.1007/S10989-019-09887-3
A Two-Level Computation Model Based on Deep Learning Algorithm for Identification of piRNA and Their Functions via Chou’s 5-Steps Rule
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TL;DR: A two level computational model based on deep neural network (DNN) that automatically extract informative features from RNA sequences using standard learning methods that performed better than the existing predictors with accuracy level 91.81% and 84.52% in the first level and in the second level respectively.
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Abstract: Piwi interacting RNA (piRNA) molecules belong to a largest class of small non coding RNA molecules which are originally discovered in animal germline cells and also occur across a variety of human somatic cells The piRNA molecules play a significant role in many gene functions such as protecting genomic integrity, gene expression regulation and restricting the functions of transposable elements The identification of piRNA molecules and their function types are significant for cancer cells diagnosis, drug developments and genes stability A number of traditional machine learning methods have been proposed for identification of piRNAs and their functions However, these methods are required a considerable amounts of human engineering and expertise to design an accurate identification model Hence, this paper proposes a two level computational model based on deep neural network (DNN) that automatically extract informative features from RNA sequences using standard learning methods Moreover, the proposed model employs di-nucleotide auto covariance (DAC) method along with six physiochemical properties to construct a feature vector The performance of the proposed model has been extensively evaluated through k-fold cross-validation tests Firstly, the performance of the proposed model is compared with commonly used classifier algorithms using benchmark dataset Secondly, its performance is compared with the existing state-of- the-art computational models The experimental results show that the proposed model performed better than the existing predictors with accuracy level 9181% and 8452% in the first level and in the second level respectively The source code along with dataset of the proposed model is freely available at https://githubcom/salman-khan-mrd/2L-piRNADNN
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AIPs-SnTCN: Predicting Anti-Inflammatory Peptides Using fastText and Transformer Encoder-Based Hybrid Word Embedding with Self-Normalized Temporal Convolutional Networks.
Ali Raza,Jamal Uddin,Abdullah Almuhaimeed,Shahid Akbar,Quan Zou,Ashfaq Ahmad +5 more
TL;DR: A highly discriminative prediction model called AIPs-SnTCN is developed to predict anti-inflammatory peptides accurately and outperformed existing models with an ∼19% higher accuracy and an ∼14% higher AUC value.
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Spark-Based Parallel Deep Neural Network Model for Classification of Large Scale RNAs into piRNAs and Non-piRNAs
TL;DR: A computational model based on parallel deep neural network for timely classification of large number of RNAs sequence into piRNAs and non-piRNAs, taking advantages of parallel and distributed computing platform is presented.
Identifying FL11 subtype by characterizing tumor immune microenvironment in prostate adenocarcinoma via Chou's 5-steps rule.
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