Feature extraction using Spectral Clustering for Gene Function Prediction using Hierarchical Multi-label Classification

TL;DR

This paper introduces a novel computational method combining spectral clustering and hierarchical multi-label classification to improve gene function prediction, reducing costs and time compared to traditional experimental approaches.

Contribution

It presents a new in silico approach that extracts features from gene co-expression networks and considers hierarchical gene functions for more accurate annotation.

Findings

Spectral clustering effectively extracts features from gene co-expression networks.

Hierarchical multi-label classification improves prediction consistency.

The approach reduces time and costs in gene annotation processes.

Abstract

Gene annotation addresses the problem of predicting unknown associations between gene and functions (e.g., biological processes) of a specific organism. Despite recent advances, the cost and time demanded by annotation procedures that rely largely on in vivo biological experiments remain prohibitively high. This paper presents a novel in silico approach for to the annotation problem that combines cluster analysis and hierarchical multi-label classification (HMC). The approach uses spectral clustering to extract new features from the gene co-expression network (GCN) and enrich the prediction task. HMC is used to build multiple estimators that consider the hierarchical structure of gene functions. The proposed approach is applied to a case study on Zea mays, one of the most dominant and productive crops in the world. The results illustrate how in silico approaches are key to reduce the time and costs of gene annotation. More specifically, they highlight the importance of: (i) building new features that represent the structure of gene relationships in GCNs to annotate genes; and (ii) taking into account the structure of biological processes to obtain consistent predictions.