Efficient Prediction of DNA-Binding Proteins Using Machine Learning

07/11/2012
by   Sokyna Qatawneh, et al.
0

DNA-binding proteins are a class of proteins which have a specific or general affinity to DNA and include three important components: transcription factors; nucleases, and histones. DNA-binding proteins also perform important roles in many types of cellular activities. In this paper we describe machine learning systems for the prediction of DNA- binding proteins where a Support Vector Machine and a Cascade Correlation Neural Network are optimized and then compared to determine the learning algorithm that achieves the best prediction performance. The information used for classification is derived from characteristics that include overall charge, patch size and amino acids composition. In total 121 DNA- binding proteins and 238 non-binding proteins are used to build and evaluate the system. For SVM using the ANOVA Kernel with Jack-knife evaluation, an accuracy of 86.7 sensitivity and 85.3 dataset with Jack knife evaluation we report an accuracy of 75.4 values of specificity and sensitivity achieved were 72.3 respectively.

READ FULL TEXT
research
04/04/2020

DNA Methylation Data to Predict Suicidal and Non-Suicidal Deaths: A Machine Learning Approach

The objective of this study is to predict suicidal and non-suicidal deat...
research
03/27/2018

Analyzing DNA Hybridization via machine learning

In DNA computing, it is impossible to decide whether a specific hybridiz...
research
02/27/2016

Towards Neural Knowledge DNA

In this paper, we propose the Neural Knowledge DNA, a framework that tai...
research
11/01/2020

Comparing Machine Learning Algorithms with or without Feature Extraction for DNA Classification

The classification of DNA sequences is a key research area in bioinforma...
research
10/25/2018

Structure Learning of Deep Networks via DNA Computing Algorithm

Convolutional Neural Network (CNN) has gained state-of-the-art results i...
research
11/12/2019

Comparing pattern sensitivity of a convolutional neural network with an ideal observer and support vector machine

We investigate the performance of a convolutional neural network (CNN) a...
research
11/26/2018

Interlacing Personal and Reference Genomes for Machine Learning Disease-Variant Detection

DNA sequencing to identify genetic variants is becoming increasingly val...

Please sign up or login with your details

Forgot password? Click here to reset