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Article Dans Une Revue Expert Systems with Applications Année : 2023

DeConDFFuse: Predicting drug-drug interaction using joint deep convolutional transform learning and decision forest fusion framework

Résumé

In Drug-Drug-Interaction (DDI), the task is to predict the (adverse) effect of administering two drugs simultaneously. Currently, the techniques proposed in this direction are generally based on either shallow learning paradigms like Random Decision Forest (RDF), Logistic Regression (LR), Support Vector Machines (SVM), etc., or deep Convolutional Neural Networks (CNNs). However, specific works combine traditional machine learning (ML) algorithms such as RDF, LR, SVM, and deep learning paradigms such as CNNs in a piecemeal fashion which might not be optimal. Hence, the present work proposes a framework that presents a joint end-to-end solution. We propose a Siamese-like architecture with two processing channels' networks based on deep convolutional transform learning. Common fused representations as well as channel-wise representations are learnt, in addition with the transform across them. The final representation is passed to a decision forest to give final predictions. The proposed method is thus a supervised end-to-end multi-channel fusion framework that (i) learns unique and interpretable filters in contrast with CNNs, and (ii) jointly learns and optimizes decision forest in contrast with state-of-the-art piecemeal approach. We apply this technique to identify DDIs among 1059 drugs from the DrugBank database showing superiority of our method compared to the state-of-the-art(s).
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Dates et versions

hal-04089994 , version 1 (05-05-2023)

Identifiants

Citer

Pooja Gupta, Angshul Majumdar, Emilie Chouzenoux, Giovanni Chierchia. DeConDFFuse: Predicting drug-drug interaction using joint deep convolutional transform learning and decision forest fusion framework. Expert Systems with Applications, 2023, 227, pp.120238:1-22. ⟨10.1016/j.eswa.2023.120238⟩. ⟨hal-04089994⟩
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