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Prediction of Side Effects Using Comprehensive Similarity Measures.

ABSTRACT: Identifying the potential side effects of drugs is crucial in clinical trials in the pharmaceutical industry. The existing side effect prediction methods mainly focus on the chemical and biological properties of drugs. This study proposes a method that uses diverse information such as drug-drug interactions from DrugBank, drug-drug interactions from network, single nucleotide polymorphisms, and side effect anatomical hierarchy as well as chemical structures, indications, and targets. The proposed method is based on the assumption that properties used in drug repositioning studies could be utilized to predict side effects because the phenotypic expression of a side effect is similar to that of the disease. The prediction results using the proposed method showed a 3.5% improvement in the area under the curve (AUC) over that obtained when only chemical, indication, and target features were used. The random forest model delivered outstanding results for all combinations of feature types. Finally, after identifying candidate side effects of drugs using the proposed method, the following four popular drugs were discussed: (1) dasatinib, (2) sitagliptin, (3) vorinostat, and (4) clonidine.

SUBMITTER: Seo S

PROVIDER: S-EPMC7064827 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Prediction of Side Effects Using Comprehensive Similarity Measures.

Seo Sukyung S Lee Taekeon T Kim Mi-Hyun MH Yoon Youngmi Y

BioMed research international 20200227

Identifying the potential side effects of drugs is crucial in clinical trials in the pharmaceutical industry. The existing side effect prediction methods mainly focus on the chemical and biological properties of drugs. This study proposes a method that uses diverse information such as drug-drug interactions from DrugBank, drug-drug interactions from network, single nucleotide polymorphisms, and side effect anatomical hierarchy as well as chemical structures, indications, and targets. The propose ...[more]

PMID: 32190647

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Project description:BACKGROUND:In silico prediction of potential drug side-effects is of crucial importance for drug development, since wet experimental identification of drug side-effects is expensive and time-consuming. Existing computational methods mainly focus on leveraging validated drug side-effect relations for the prediction. The performance is severely impeded by the lack of reliable negative training data. Thus, a method to select reliable negative samples becomes vital in the performance improvement. METHODS:Most of the existing computational prediction methods are essentially based on the assumption that similar drugs are inclined to share the same side-effects, which has given rise to remarkable performance. It is also rational to assume an inverse proposition that dissimilar drugs are less likely to share the same side-effects. Based on this inverse similarity hypothesis, we proposed a novel method to select highly-reliable negative samples for side-effect prediction. The first step of our method is to build a drug similarity integration framework to measure the similarity between drugs from different perspectives. This step integrates drug chemical structures, drug target proteins, drug substituents, and drug therapeutic information as features into a unified framework. Then, a similarity score between each candidate negative drug and validated positive drugs is calculated using the similarity integration framework. Those candidate negative drugs with lower similarity scores are preferentially selected as negative samples. Finally, both the validated positive drugs and the selected highly-reliable negative samples are used for predictions. RESULTS:The performance of the proposed method was evaluated on simulative side-effect prediction of 917 DrugBank drugs, comparing with four machine-learning algorithms. Extensive experiments show that the drug similarity integration framework has superior capability in capturing drug features, achieving much better performance than those based on a single type of drug property. Besides, the four machine-learning algorithms achieved significant improvement in macro-averaging F1-score (e.g., SVM from 0.655 to 0.898), macro-averaging precision (e.g., RBF from 0.592 to 0.828) and macro-averaging recall (e.g., KNN from 0.651 to 0.772) complimentarily attributed to the highly-reliable negative samples selected by the proposed method. CONCLUSIONS:The results suggest that the inverse similarity hypothesis and the integration of different drug properties are valuable for side-effect prediction. The selection of highly-reliable negative samples can also make significant contributions to the performance improvement.

Project description:Memory conformity may develop when people are confronted with distinct memories reported by others in social situations and knowingly/unknowingly adhere to these exogenous memories. Earlier research on memory conformity suggests that (1) subjects were more likely to conform to confederate with high confidence; (2) subjects with low confidence on their memory accuracy were more likely to conform, and; (3) this subjective confidence could be adjusted by social manipulations. Nonetheless, it remains unclear how the confidence levels of ours and others may interact and produce a combined effect on our degree of conformity. More importantly, is memory conformity, defined by a complete adoption of the opposite side, the result of a gradual accumulation of subtler changes at the confidence level, i.e., a buildup of confidence conformity? Here, we followed participant's confidence transformation quantitatively over three confederate sessions in a memory test. After studying a set of human motion videos, participants had to answer simultaneously whether a target or lure video had appeared before by indicating their side (i.e., Yes/No) and their associated confidence rating. Participants were allowed to adjust their responses as they were being shown randomly-generated confederates' answers and confidence values. Results show that participants indeed demonstrated confidence conformity. Interestingly, they tended to become committed to their side early on and gain confidence gradually over subsequent sessions. This polarizing behaviour may be explained by two kinds of preferences: (1) Participant's confidence enhancement towards same-sided confederates was greater in magnitude compared to the decrement towards an opposite-sided confederate; and (2) Participants had the most effective confidence boost when the same-sided confederates shared similar, but not considerably different, confidence level to theirs. In other words, humans exhibit side- and similarity-biases during confidence conformity.

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Prediction of Side Effects Using Comprehensive Similarity Measures.

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Prediction of Side Effects Using Comprehensive Similarity Measures.

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