Features’ compendium for machine learning in NGS data Analysis
Current studies on the cancer genome, majorly involve use of next generation sequencing (NGS) technologies followed by data analysis pipelines. Many of these pipelines comprise of tools using machine learning algorithms especially for downstream analysis. Features are important components of machine learning systems and inclusion of informative features improves the accuracy of the machine learning algorithms. The algorithms used inNGS analysis leads to the generation of huge feature space.Sometimes, this high dimensionality leads to slower analysis time and lesser accuracy due to inherentbias of the model and/or redundancy of fewfeatures. With growth and interest in NGS studies, there has been a rapid development of new NGS analysis tools and improvement in the performance of the previous ones byincluding new features and excludingthe redundant ones. To enable these development, there is a dire need for standardizing this plethora of features available from literature.
Current work presents a compendium of features that have been used in the literature for machine learning in NGS data pipeline and analysis. The features have beenfurther classified, assuming each stage of NGS data processing as individual category. The simple classification is a) Pre-processing features (b) Sequencing technology specific features (c) Downstream featuresor features for biological interpretation and analysis. This categorization will facilitate the use of correct features in a simplified manner.
The work will facilitate a uniform model for NGS tools development that utilize machine learning approaches for study of cancer data.A model for feature database and management based on this standardization is also proposed.
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