Comparison of machine learning and deep learning techniques in promoter prediction across diverse species

by   Nikita Bhandari, et al.

Gene promoters are the key DNA regulatory elements positioned around the transcription start sites and are responsible for regulating gene transcription process. Various alignment-based, signal-based and content-based approaches are reported for the prediction of promoters. However, since all promoter sequences do not show explicit features, the prediction performance of these techniques is poor. Therefore, many machine learning and deep learning models have been proposed for promoter prediction. In this work, we studied methods for vector encoding and promoter classification using genome sequences of three distinct higher eukaryotes viz. yeast (Saccharomyces cerevisiae), A. thaliana (plant) and human (Homo sapiens). We compared one-hot vector encoding method with frequency-based tokenization (FBT) for data pre-processing on 1-D Convolutional Neural Network (CNN) model. We found that FBT gives a shorter input dimension reducing the training time without affecting the sensitivity and specificity of classification. We employed the deep learning techniques, mainly CNN and recurrent neural network with Long Short Term Memory (LSTM) and random forest (RF) classifier for promoter classification at k-mer sizes of 2, 4 and 8. We found CNN to be superior in classification of promoters from non-promoter sequences (binary classification) as well as species-specific classification of promoter sequences (multiclass classification). In summary, the contribution of this work lies in the use of synthetic shuffled negative dataset and frequency-based tokenization for pre-processing. This study provides a comprehensive and generic framework for classification tasks in genomic applications and can be extended to various classification problems.



There are no comments yet.


page 1

page 2

page 3

page 4


An Empirical Analysis of Image-Based Learning Techniques for Malware Classification

In this paper, we consider malware classification using deep learning te...

Classification of Pedagogical content using conventional machine learning and deep learning model

The advent of the Internet and a large number of digital technologies ha...

Machine Learning based Prediction of Hierarchical Classification of Transposable Elements

Transposable Elements (TEs) or jumping genes are the DNA sequences that ...

A Novel Approach for Earthquake Early Warning System Design using Deep Learning Techniques

Earthquake signals are non-stationary in nature and thus in real-time, i...

Random Fragments Classification of Microbial Marker Clades with Multi-class SVM and N-Best Algorithm

Microbial clades modeling is a challenging problem in biology based on m...

byteSteady: Fast Classification Using Byte-Level n-Gram Embeddings

This article introduces byteSteady – a fast model for classification usi...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.