https://github.com/rvinas/predicting-subcellular-location

Predicting the subcellular location of eukaryotic proteins with support vector machines
https://github.com/rvinas/predicting-subcellular-location

amino-acid-sequence amino-acids bioinformatics biopython proteomics random-forest svm

Last synced: 6 days ago
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Predicting the subcellular location of eukaryotic proteins with support vector machines

• Host: GitHub
• URL: https://github.com/rvinas/predicting-subcellular-location
• Owner: rvinas
• Created: 2018-03-03T21:23:50.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2018-03-23T13:24:00.000Z (almost 7 years ago)
• Last Synced: 2024-11-01T09:43:45.172Z (about 2 months ago)
• Topics: amino-acid-sequence, amino-acids, bioinformatics, biopython, proteomics, random-forest, svm
• Language: Jupyter Notebook
• Homepage:
• Size: 5.14 MB
• Stars: 0
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Predicting Subcellular Location

## Overview

Within the last few years the complete sequence has been determined for over 3000 genomes. Predicting the function of a protein has proved to be a difficult task where no clear homology to proteins of known function exists. Knowing the subcellular location of such proteins might be a crucial feature to determine their function. This work presents an approach for predicting the subcellular location (nuclear, mitochondrial, cytosolic or secreted) of non-homologous proteins. This method extracts several N-terminal and global features, and performs classification using a Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel.

This work corresponds to the coursework of the module COMPGI10 Bioinformatics, University College London.

Please find further information at: http://www0.cs.ucl.ac.uk/staff/D.Jones/coursework/

## Prerequisites

- Python 3.5

- Numpy 1.14.0

- Pandas 0.22.0

- Scikit-learn 0.19.1

- Biopython 1.70

- Matplotlib 2.1.2

- Seaborn 0.8.1

## Installation

Run `sudo pip install -r requirements.txt`

## Structure

- data/

  - cyto.fasta: Amino acid sequences from cytosol in FASTA format

  - mito.fasta: Amino acid sequences from mitochondria in FASTA format

  - nucleus.fasta: Amino acid sequences from nucleus in FASTA format

  - secreted.fasta: Secreted amino acid sequences in FASTA format

  - blind.fasta: 20 unlabeled amino acid sequences in FASTA format

- src/

  - data_pipeline.py: Parses FASTA data and creates features from the amino acid sequences

  - utils.py: Subcellular location prediction utilities

  - eda.ipynb: Exploratory Data Analysis

  - svm.py: Trains a Support Vector Machine to perform subcellular location prediction

  - rf.py: Trains a Random Forest to perform subcellular location prediction

  - nn.py: Trains a feed-forward network to perform subcellular location prediction