Feature Extraction: A Fourier and Numerical Mapping Approach ---- To extract features using 5 numeric mappings

Due to the high number of genomic sequencing projects, the number of RNA transcripts increased significantly, creating a huge volume of data. Thus, new computational methods are needed for the analysis and information extraction from these data. In particular, when parts of a genome are transcribed into RNA molecules, some specific classes of RNA are produced, such as mRNA and ncRNA with different functions. In this way, long non-coding RNAs have emerged as key regulators of many biological processes. Therefore, machine learning approaches are being used to identify this enigmatic RNA class. Considering this, we present a Fourier transform-based features extraction approach with 5 numerical mapping techniques (Voss, Integer, Real, EIIP and Z-curve), in order to classify lncRNAs from plants. We investigate four classification algorithms like Naive Bayes, Random Forest, Support Vector Machine and AdaBoost. Moreover, the proposed approach was compared with 4 competing methods available in the literature (CPC2, CNCI, PLEK, and RNAplonc). The experimental results demonstrated high efficiency for the classification of lncRNAs, providing competitive performance.

Authors

• Robson Parmezan Bonidia, Fabrício Martins Lopes, Lucas Dias Hiera Sampaio and Danilo Sipoli Sanches.

• Correspondence: robservidor@gmail.com and danilosanches@utfpr.edu.br

Publication

Bonidia R.P., Sampaio L.D.H., Lopes F.M., Sanches D.S. (2019) Feature Extraction of Long Non-coding RNAs: A Fourier and Numerical Mapping Approach. In: Nyström I., Hernández Heredia Y., Milián Núñez V. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2019. Lecture Notes in Computer Science, vol 11896. Springer, Cham.

List of files

• Datasets: Fasta and csv files used in our research;

• Examples: Files of Example

• README: Documentation;

• featureExtraction.py Main File - Python.

Dependencies

• Python (>=3.5)
• NumPy
• SciPy
• Biopython
• Statistics

Installing our tool

$ git clone https://github.com/Bonidia/FourierFeatureExtraction.git FeatureExtractionFourier

Usange and Examples

Access folder: $ cd FeatureExtractionFourier
 
To run the tool (Example): $ python3.5 featureExtraction.py -i input -o output -l mRNA -r 2


Where:

-i = Input - Fasta format file, e.g., test.fasta

-o = output - CSV format file, e.g., test.csv

-l = Label - Dataset Label, e.g., lncRNA, mRNA, sncRNA

-r = representation/mappings, e.g., 1 = Binary, 2 = Z-curve, 3 = Real, 4 = Integer, 5 = EIIP.

About

If you use this code in a scientific publication, we would appreciate citations to the following paper:

Bonidia R.P., Sampaio L.D.H., Lopes F.M., Sanches D.S. (2019) Feature Extraction of Long Non-coding RNAs: A Fourier and Numerical Mapping Approach. In: Nyström I., Hernández Heredia Y., Milián Núñez V. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2019. Lecture Notes in Computer Science, vol 11896. Springer, Cham.