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Your search keyword '"ATC code"' showing total 4 results
Start Over Descriptor "ATC code" Topic machine learning
4 results on '"ATC code"'

1. A similarity-based method for prediction of drug side effects with heterogeneous information.

Author

Zhao, Xian, Chen, Lei, and Lu, Jing

Subjects
*DRUG side effects, *PREDICTION models, *RANDOM forest algorithms, *MACHINE learning, *DIFFERENTIAL equations
Abstract

Highlights • A novel binary classification scheme for prediction of drug side effect was built. • A prediction model for the binary classification was proposed. • The prediction model integrated several heterogeneous information of drugs. • Several comparisons were performed to indicate the effectiveness of the model. • Drug similarity in fingerprint is most related to drug side effects. Abstract Drugs can produce intended therapeutic effects to treat different diseases. However, they may also cause side effects at the same time. For an approved drug, it is best to detect all side effects it can produce. Otherwise, it may bring great risks for pharmaceuticals companies as well as be harmful to human body. It is urgent to design quick and reliable identification methods to detect the side effects for a given drug. In this study, a binary classification model was proposed to predict drug side effects. Different from most previous methods, our model termed the pair of drug and side effect as a sample and convert the original problem to a binary classification problem. Based on the similarity idea, each pair was represented by five features, each of which was derived from a type of drug property. The strong machine learning algorithm, random forest, was adopted as the prediction engine. The ten-fold cross-validation on five datasets with different negative samples indicated that the proposed model yielded a good performance of Matthews correlation coefficient around 0.550 and AUC around 0.8492. In addition, we also analyzed the contribution of each drug property for construction of the model. The results indicated that drug similarity in fingerprint was most related to the prediction of drug side effects and all drug properties gave less or more contributions. [ABSTRACT FROM AUTHOR]

Published
2018
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2. Drug repositioning: amachine-learning approach through data integration.

Author

Francesco Napolitano, Yan Zhao, Moreira, Vânia M., Tagliaferri, Roberto, Kere, Juha, D'Amato, Mauro, and Greco, Dario

Subjects
*DATA integration, *MACHINE learning, *SUPPORT vector machines, *GENOMICS, *ANTHELMINTICS, *ANTINEOPLASTIC agents, *GENE expression, *THERAPEUTICS
Abstract

Existing computational methods for drug repositioning either rely only on the gene expression response of cell lines after treatment, or on drug-to-disease relationships, merging several information levels. However, the noisy nature of the gene expression and the scarcity of genomic data for many diseases are important limitations to such approaches. Here we focused on a drug-centered approach by predicting the therapeutic class of FDA-approved compounds, not considering data concerning the diseases. We propose a novel computational approach to predict drug repositioning based on state-of-the-art machine-learning algorithms. We have integrated multiple layers of information: i) on the distances of the drugs based on how similar are their chemical structures, ii) on how close are their targets within the protein-protein interaction network, and iii) on how correlated are the gene expression patterns after treatment. Our classifier reaches high accuracy levels (78%), allowing us to re-interpret the top misclassifications as re-classifications, after rigorous statistical evaluation. Efficient drug repurposing has the potential to significantly impact the whole field of drug development. The results presented here can significantly accelerate the translation into the clinics of known compounds for novel therapeutic uses. [ABSTRACT FROM AUTHOR]

Published
2013
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4. Drug Repositioning: A Machine-Learning Approach through Data Integration

Author

Vânia M. Moreira, Mauro D'Amato, Francesco Napolitano, Roberto Tagliaferri, Yan Zhao, Juha Kere, and Dario Greco

Subjects
Computer science, SVM, ATC code, Library and Information Sciences, computer.software_genre, Machine learning, RS, 03 medical and health sciences, 0302 clinical medicine, Text mining, CMap, Interaction network, Connectivity map, Physical and Theoretical Chemistry, 030304 developmental biology, Anthelmintics, 0303 health sciences, business.industry, Drug repositioning, SMILES, Computer Graphics and Computer-Aided Design, Antineoplastic, Oxamniquine, Computer Science Applications, Support vector machine, Drug development, Mode of action, 030220 oncology & carcinogenesis, Integrative genomics, Niclosamide, Artificial intelligence, Data mining, business, computer, Classifier (UML), After treatment, Research Article, Data integration
Abstract

Existing computational methods for drug repositioning either rely only on the gene expression response of cell lines after treatment, or on drug-to-disease relationships, merging several information levels. However, the noisy nature of the gene expression and the scarcity of genomic data for many diseases are important limitations to such approaches. Here we focused on a drug-centered approach by predicting the therapeutic class of FDA-approved compounds, not considering data concerning the diseases. We propose a novel computational approach to predict drug repositioning based on state-of-the-art machine-learning algorithms. We have integrated multiple layers of information: i) on the distances of the drugs based on how similar are their chemical structures, ii) on how close are their targets within the protein-protein interaction network, and iii) on how correlated are the gene expression patterns after treatment. Our classifier reaches high accuracy levels (78%), allowing us to re-interpret the top misclassifications as re-classifications, after rigorous statistical evaluation. Efficient drug repurposing has the potential to significantly impact the whole field of drug development. The results presented here can significantly accelerate the translation into the clinics of known compounds for novel therapeutic uses.

Published
2013

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