Neural networks for Anatomical Therapeutic Chemical (ATC)

• URL: http://arxiv.org/abs/2101.11713v1
• Date: Fri, 22 Jan 2021 19:49:47 GMT
• Title: Neural networks for Anatomical Therapeutic Chemical (ATC)
• Authors: Loris Nanni, Alessandra Lumini and Sheryl Brahnam
• Abstract summary: 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.
• Score: 83.73971067918333
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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

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