Neural networks for Anatomical Therapeutic Chemical (ATC) classification

TL;DR

This paper presents a novel multi-label classification approach for ATC drug classification using combined classifiers and BiLSTM features, outperforming existing methods in bioinformatics.

Contribution

It introduces a new ensemble method combining multiple classifiers trained on diverse feature sets, including BiLSTM-derived features, for improved ATC classification accuracy.

Findings

Outperforms state-of-the-art methods in ATC classification

Demonstrates the effectiveness of combining classifiers with BiLSTM features

Provides publicly available source code for reproducibility

Abstract

Motivation: Automatic Anatomical Therapeutic Chemical (ATC) classification is a critical and highly competitive area of research in bioinformatics because of its potential for expediting drug develop-ment and research. Predicting an unknown compound's therapeutic and chemical characteristics ac-cording to how these characteristics affect multiple organs/systems makes automatic ATC classifica-tion a challenging multi-label problem. Results: In this work, we propose combining multiple multi-label classifiers trained on distinct sets of features, including sets extracted from a Bidirectional Long Short-Term Memory Network (BiLSTM). Experiments demonstrate the power of this approach, which is shown to outperform the best methods reported in the literature, including the state-of-the-art developed by the fast.ai research group. Availability: All source code developed for this study is available at https://github.com/LorisNanni. Contact: loris.nanni@unipd.it