HPSLPred: An Ensemble Multi-label Classifier for Human Protein Subcellular Location Prediction with Imbalanced Source

TL;DR

HPSLPred is an ensemble multi-label classifier designed to predict protein subcellular locations, effectively handling multi-label and imbalanced data challenges in biological datasets.

Contribution

The paper introduces HPSLPred, a novel ensemble classifier that addresses multi-label and imbalanced data issues in protein localization prediction.

Findings

Successfully predicts multiple subcellular locations of proteins.

Handles imbalanced datasets effectively.

Provides a user-friendly webserver for researchers.

Abstract

Predicting the subcellular localization of proteins is an important and challenging problem. Traditional experimental approaches are often expensive and time-consuming. Consequently, a growing number of research efforts employ a series of machine learning approaches to predict the subcellular location of proteins. There are two main challenges among the state-of-the-art prediction methods. First, most of the existing techniques are designed to deal with multi-class rather than multi-label classification, which ignores connections between multiple labels. In reality, multiple locations of particular proteins implies that there are vital and unique biological significances that deserve special focus and cannot be ignored. Second, techniques for handling imbalanced data in multi-label classification problems are necessary, but never employed. For solving these two issues, we have developed an ensemble multi-label classifier called HPSLPred, which can be applied for multi-label classification with an imbalanced protein source. For convenience, a user-friendly webserver has been established at http://server.malab.cn/HPSLPred.