Machine Learning Interface to RNA-Seq data #

Introduction: #

In the field of artificial intelligence, Machine Learning (ML) is one of the major component to automate the most of the tedious task with the known knowledge. ML, majorly influenced in the field of bioinformatics and molecular biology for decision making from data. In context of the next generation sequencing technologies, there are various type of technology i.e. Illumina, 454 and Pacbio etc., and multiple type of sequencing methods i.e. (DNA-Seq, RNA-Seq, Chip-Seq, etc.,) at different stages of central dogma i.e (transcription and translation). Since 2004, the RNA-Seq were predominantly used by the versatile research applications. Importantly the RNA-Seq data analysis field were most optimized towards qualitative and quantitative parameters to obtain the knowledge from the multiple data sets.

MLSeq: #

The MLSeq Package developed for the biological researchers, which can easily incorporate the different data formats and multiple machines i.e Support vector machine (SVM), Random Forest (RF) and classification and regression tress (CART) to automate the RNA-Seq problems.

Functions: #

1) DESeqDataSetFromMatrix()

2) DESeq()

3) Classify()

4) predictClassify()

References: #

1) MLSeq package: Machine Learning Interface to RNA-Seq Data (https://www.bioconductor.org/packages/3.3/bioc/html/MLSeq.html).