Due to the advent of Next Generation Sequencing and multiple innovative experimental
techniques there is an exponential increase in biological data. It is necessary to capture …

Background In the age of information superhighway, big data play a significant role in
information processing, extractions, retrieving and management. In computational biology …

Classification which is a popular supervised machine learning method has many
applications in computational biology, where data samples are automatically categorized …

Computational synthesize of the metabolic pathway is take low cost while comparing with
the direct trial and error laboratory process. In real world data, more or less all datasets …

Recently, feature selection task has gained more attention in classification of problems. This
task aims to find the most important features in a large search space of potential solutions …

Background Medical and biological data are commonly with small sample size, missing
values, and most importantly, imbalanced class distribution. In this study we propose a …

In machine learning, the data imbalance imposes challenges to perform data analytics in
almost all areas of real-world research. The raw primary data often suffers from the skewed …

The training machine learning algorithm from an imbalanced data set is an inherently
challenging task. It becomes more demanding with limited samples but with a massive …

Predictive analytics incorporate various statistical techniques from predictive modelling,
machine learning and data mining to analyse large database for future prediction. Data …

For over two decades, detecting rare events has been a challenging task among
researchers in the data mining and machine learning domain. Real-life problems inspire …