A meta-learning framework using representation learning to predict drug-drug interaction

Deepika, S.; Geetha, T. V.

doi:10.1016/j.jbi.2018.06.015

Cited by 45 publications

(20 citation statements)

References 35 publications

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“…Zhang et al (2017) built a prediction model based on various characteristics of drugs and known data about DDI according to neighbor-recommendation, random walk, and matrix disturbance approaches, which use flexible and diverse frameworks to combine different models with different ensemble rules. Deepika and Geetha (2018) predicted DDI through positive-unlabeled (PU) learning (Elkan and Keith, 2008) and meta-learning (Lemke et al, 2015), and proposed a learning framework for semi-supervised classifiers based on SVM. The PU-based classifier was used to generate metaknowledge from the network, and the meta-classifier was designed to predict the probability of DDI from the generated meta-knowledge.…”

Section: Ensemble-based Approachmentioning

confidence: 99%

A Review of Approaches for Predicting Drug–Drug Interactions Based on Machine Learning

Han

Cao²,

Wang

et al. 2022

Front. Pharmacol.

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Drug–drug interactions play a vital role in drug research. However, they may also cause adverse reactions in patients, with serious consequences. Manual detection of drug–drug interactions is time-consuming and expensive, so it is urgent to use computer methods to solve the problem. There are two ways for computers to identify drug interactions: one is to identify known drug interactions, and the other is to predict unknown drug interactions. In this paper, we review the research progress of machine learning in predicting unknown drug interactions. Among these methods, the literature-based method is special because it combines the extraction method of DDI and the prediction method of DDI. We first introduce the common databases, then briefly describe each method, and summarize the advantages and disadvantages of some prediction models. Finally, we discuss the challenges and prospects of machine learning methods in predicting drug interactions. This review aims to provide useful guidance for interested researchers to further promote bioinformatics algorithms to predict DDI.

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Section: Ensemble-based Approachmentioning

confidence: 99%

A Review of Approaches for Predicting Drug–Drug Interactions Based on Machine Learning

Han

Cao²,

Wang

et al. 2022

Front. Pharmacol.

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“…In this, SVM, Node2vec and a PU learning algorithm are utilised. Meta‐classifier predicts DDI more significantly than base classifiers [21].…”

Section: Related Workmentioning

confidence: 99%

Efficient prediction of drug–drug interaction using deep learning models

Shukla

Sharma

et al. 2020

IET syst. biol.

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“…Cui et al [23] proposed a supervised model guided by specific prediction tasks to facilitate representations of medical codes, and it is effective to work with small EHR datasets. Deepika and Geetha [24] used a semi-supervised learning framework which contains representation learning of drugs to predict the drug interactions. However, these studies are all concept level, which means that the representations are learned to represent medical codes rather than patient representations.…”

Section: Related Workmentioning

confidence: 99%

Representation learning for clinical time series prediction tasks in electronic health records

Ruan

Lei

Zhou

et al. 2019

BMC Med Inform Decis Mak

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BackgroundElectronic health records (EHRs) provide possibilities to improve patient care and facilitate clinical research. However, there are many challenges faced by the applications of EHRs, such as temporality, high dimensionality, sparseness, noise, random error and systematic bias. In particular, temporal information is difficult to effectively use by traditional machine learning methods while the sequential information of EHRs is very useful.MethodIn this paper, we propose a general-purpose patient representation learning approach to summarize sequential EHRs. Specifically, a recurrent neural network based denoising autoencoder (RNN-DAE) is employed to encode inhospital records of each patient into a low dimensional dense vector.ResultsBased on EHR data collected from Shuguang Hospital affiliated to Shanghai University of Traditional Chinese Medicine, we experimentally evaluate our proposed RNN-DAE method on both mortality prediction task and comorbidity prediction task. Extensive experimental results show that our proposed RNN-DAE method outperforms existing methods. In addition, we apply the “Deep Feature” represented by our proposed RNN-DAE method to track similar patients with t-SNE, which also achieves some interesting observations.ConclusionWe propose an effective unsupervised RNN-DAE method to summarize patient sequential information in EHR data. Our proposed RNN-DAE method is useful on both mortality prediction task and comorbidity prediction task.

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A meta-learning framework using representation learning to predict drug-drug interaction

Cited by 45 publications

References 35 publications

A Review of Approaches for Predicting Drug–Drug Interactions Based on Machine Learning

A Review of Approaches for Predicting Drug–Drug Interactions Based on Machine Learning

Efficient prediction of drug–drug interaction using deep learning models

Representation learning for clinical time series prediction tasks in electronic health records

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