# Neural networks for Anatomical Therapeutic Chemical (ATC) classification

arXiv: https://arxiv.org/abs/2101.11713 · HTML: https://mlsanity.com/paper/2101.11713/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 29 references that are in the map.
- First posted on arXiv 22 Jan 2021.

## Builds on (most cited first, 4 of 4)

- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [fastai: A Layered API for Deep Learning](https://mlsanity.com/paper/2002.04688/) (2020)
- [Group Preserving Label Embedding for Multi-Label Classification](https://mlsanity.com/paper/1812.09910/) (2018)
- [MLC Toolbox: A MATLAB/OCTAVE Library for Multi-Label Classification](https://mlsanity.com/paper/1704.02592/) (2017)

## Built on by (most cited first, 1 of 1)

- [Gated recurrent units and temporal convolutional network for multilabel classification](https://mlsanity.com/paper/2110.04414/) (2021)

## Abstract (arXiv)

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