DPP IV Inhibitors Activities Prediction as An Anti-Diabetic Agent using Particle Swarm Optimization-Support Vector Machine Method

Septiawan, Reza Rendian; Prakoso, None Bambang Hadi; Kurniawan, Isman

doi:10.29207/resti.v6i6.4470

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…The SVM method is to carry out sentiment analysis using the Support Vector Machine method for the training and testing process [24] and is used to facilitate classification tasks with minimized computational effort whether the data includes positive or negative sentiments [25]. SVM has a goal to build the best hyperplane that can divide a given sample set into two distinct classes [26].…”

Section: Methodsmentioning

confidence: 99%

Comparing the Performance of Data Mining Algorithms in Predicting Sentiments on Twitter

Rusydi Umar,

Sunardi,

Nuriyah

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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On the social networking site Twitter, users can post tweets, videos, and images. It can, however, also be disruptive and difficult. In order to categorize material and improve searchability, hashtags are crucial. This study focuses on examining the opinions of Twitter users who participate in trending topics. The algorithms K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are employed for sentiment analysis. The dataset comprises of tweet information on popular subjects that was collected using the Twitter API and saved in Excel format. SVM and K-NN are used for data preparation, weighting, and sentiment analysis. With 105 data points, the study provides insights into user sentiment. SVM identified 99% of positive and 1% of negative replies with accuracy of 80%. KNN successfully identified 90% of positive and 10% of negative responses, with an accuracy rate of 71.4%. According to the results, SVM performs better when analyzing the sentiment of hashtag users on Twitter.

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Section: Methodsmentioning

confidence: 99%

Comparing the Performance of Data Mining Algorithms in Predicting Sentiments on Twitter

Rusydi Umar,

Sunardi,

Nuriyah

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Predictive Modeling and Drug Repurposing for Type-II Diabetes

Wankhade,

Sharma,

Wani

et al. 2024

ACS Med. Chem. Lett.

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Diabetes mellitus (DM) is a global health concern, and dipeptidyl peptidase-4 (DPP-4) is a key therapeutic target. The study used three machine learning and deep learning models to predict potential DPP-4 inhibitors using a curated data set of 6,750 compounds. The models included support vector machine (SVM), random forest (RF), naive Bayes (NB), and multitask deep neural network (MTDNN). The MTDNN model demonstrated strong predictive performance, achieving 98.62% train accuracy and 98.42% test accuracy for predicting DPP-4 inhibitors and a correlation coefficient of 0.979 for training and 0.977 for the test data set, with low training and test errors while predicting corresponding IC 50 values. The MTDNN model predicted potential inhibitors using an external data set of FDA-approved drugs, identifying 100 compounds. Among these, five compounds stood out with promising molecular docking and dynamic profiles, suggesting their potential as repurposed drugs for targeting DPP-4 and offering hope for the future of diabetes treatment.

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DPP IV Inhibitors Activities Prediction as An Anti-Diabetic Agent using Particle Swarm Optimization-Support Vector Machine Method

Cited by 2 publications

References 27 publications

Comparing the Performance of Data Mining Algorithms in Predicting Sentiments on Twitter

Comparing the Performance of Data Mining Algorithms in Predicting Sentiments on Twitter

Predictive Modeling and Drug Repurposing for Type-II Diabetes

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