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Related Experiment Video

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A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Published on: May 22, 2018

Predicting Post-Translational Modifications from Local Sequence Fragments Using Machine Learning Algorithms: Overview

Marcin Tatjewski^1,2, Marcin Kierczak³, Dariusz Plewczynski⁴

¹Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland.

Methods in Molecular Biology (Clifton, N.J.)

|October 28, 2016

Summary

This summary is machine-generated.

This study outlines essential steps for building post-translational modification (PTM) prediction tools using machine learning. It addresses key challenges like imbalanced data and homology in cross-validation for improved PTM prediction accuracy.

Keywords:

Class imbalance Cross-validation Feature extraction Feature selection Phosphorylation

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Area of Science:

Computational Biology
Bioinformatics
Machine Learning

Background:

Predicting post-translational modifications (PTMs) is crucial for understanding protein function.
Machine learning (ML) offers powerful tools for PTM prediction from protein sequences.
Existing methods often face challenges with data imbalance and homology considerations.

Purpose of the Study:

To provide a comprehensive guide for developing PTM prediction models.
To discuss and offer solutions for advanced challenges in PTM prediction.
To aid researchers in building and refining PTM prediction tools.

Main Methods:

Data gathering, feature extraction, and machine learning classifier selection for PTM prediction.
Addressing the class imbalance problem in training data for PTM prediction.
Implementing a novel 'folds-over-clusters' algorithm for homology-aware cross-validation.
Exploring efficient methods for incorporating new feature sources.

Main Results:

A foundational framework for constructing PTM predictors is detailed.
Statistical insights into the class imbalance issue in PTM prediction are provided.
The 'folds-over-clusters' algorithm is presented as a solution for homology in cross-validation.
Strategies for feature expansion in PTM prediction models are discussed.

Conclusions:

The presented methodologies offer practical guidance for PTM prediction research.
Addressing data imbalance and homology is critical for robust PTM prediction.
These techniques benefit both novice and experienced researchers in the PTM prediction field.