Judul: Prediction of Compound-Protein Interaction for Drug Repurposing Anti COVID-19 Using Convolutional Neural Network Method
Abstrak: COVID-19 menyebabkan masalah kesehatan seperti demam, batuk kering, gangguan pernapasan, dan bahkan kematian. Penemuan obat secara tradisional memerlukan banyak sumber daya, sehingga pendekatan komputasional menjadi salah satu pendekatan yang efisien untuk screening senyawa potensial melalui prediksi interaksi senyawa-protein. Model deep learning yang digunakan pada penelitian ini adalah Convolutional Neural Network (CNN). Hasil pemodelan CNN dibandingkan dengan model Support Vector Machine dan Naive Bayes dengan representasi fitur protein Amino Acid Composition (AAC) dan Dipeptide Composition (DC). Selain itu, juga diamati pengaruh penggunaan seleksi fitur pada model. Selanjutnya, kinerja dari metode untuk memprediksi interaksi senyawa dan protein diukur dengan menggunakan akurasi, precision, recall, F-measure, dan AUROC. Hasil penelitian menunjukkan bahwa pemodelan dengan representasi fitur protein DC lebih baik dibandingkan dengan AAC. Pemodelan interaksi senyawa-protein menggunakan PubChem fingerprint sebagai representasi senyawa dan DC sebagai representasi protein pada CNN dengan seleksi fitur ANOVA menghasilkan kinerja terbaik dengan nilai akurasi sebesar 0.9475, recall 0.9687, precision 0.9679, F-measure 0.9683, dan AUROC 0.9751., COVID-19 is a disease that causes health problems. Traditional drug discovery requires many resources. Thus, the computational approach is one of the approaches that can be employed to screen potential compounds through the prediction of compound-protein interactions. The deep learning model used in this study is Convolutional Neural Network (CNN). The results of the CNN model were compared to Support Vector Machine (SVM) and Naive Bayes (NB) with representations of proteins using Amino Acid Composition (AAC) and Dipeptide Composition (DC). We also examined the effect of the feature selection approach using ANOVA. The results were evaluated in terms of accuracy, precision, recall, F-measure, and AUROC. Results showed that modeling with a representation of DC protein features was better than AAC. Prediction of compound-protein interaction modeling using PubChem fingerprint as a compound representation and DC as protein representation on CNN using ANOVA feature selection resulted in the best performance with an accuracy value of 0.9475, recall 0.9687, precision 0.9679, F-measure 0.9683, and AUROC 0.9751.
Keyword: convolutional neural network, COVID-19, drug repurposing, drug target interaction

Judul: Prediction of Drug Target Interaction for Drug Repurposing Anti COVID-19 Using Deep Neural Network
Abstrak: Coronavirus Disease 2019 (COVID-19) merupakan penyakit menular saluran pernapasan yang merusak sistem kesehatan di berbagai negara. Hingga saat ini belum ditemukan obat anti COVID-19 yang efektif. Drug repurposing digunakan untuk mengatasi penyakit baru selain penyakit awal obat tersebut ditujukan. Interaksi senyawa obat dan protein penyakit merupakan dasar dari drug repurposing. Pada penelitian ini dilakukan prediksi interaksi senyawa-protein COVID-19 menggunakan metode klasifikasi Deep Neural Network. Selanjutnya, dilakukan perbandingan kinerja hasil prediksi terhadap algoritme Random Forest dan K-Nearest Neighbor dengan menggunakan representasi fitur protein Amino Acid Composition dan Dipeptide Composition. Selain itu juga dibandingkan kinerja model prediksi pada dataset dengan menggunakan seleksi fitur ANOVA. Perbandingan kinerja model prediksi diukur dengan menggunakan AUROC, accuracy, precision, recall, dan F-measure. Hasil penelitian menunjukkan bahwa penggunaan PubChem fingerprint untuk representasi senyawa dan Dipeptide Composition untuk representasi protein menghasilkan nilai metrik yang paling baik dengan accuracy (0.9548), precision (0.8736), recall (0.8551), F-measure (0.8642) dan AUROC (0.9787) pada model Deep Neural Network dan seleksi fitur ANOVA., Coronavirus Disease 2019 (COVID-19) is an infectious respiratory disease that is damaging health systems in various countries. So far, no effective anti-Covid-19 drug has been found. Drug repurposing is used to treat new diseases other than the initial disease the drug was intended for. The interaction of drug compounds and disease proteins (DTI) is the basis of drug repurposing. In this study, predictions of the COVID-19 protein-compound interactions were carried out using the Deep Neural Network classification method. Furthermore, a comparison of the performance of the prediction results against the Random Forest and K-Nearest Neighbor algorithms was carried out using Amino Acid Composition and Dipeptide Composition protein features. The performance of the prediction model is also compared using the ANOVA feature selection. The comparison of prediction is measured using AUROC, accuracy, precision, recall, and F-measure. The results use the PubChem fingerprint for compound representation, and Dipeptide Composition for protein is the best metric value ​​with accuracy (0.9548), precision (0.8736), recall (0.8551), F-measure (0.8642), and AUROC (0.9787) at Deep Neural Network model and ANOVA.
Keyword: COVID-19, deep neural network, dipeptide composition, drug repurposing

Judul: Etno-ornitologi Burung Kancilan Flores (Pachycephala nudigula nudigula Hartert 1897) pada Masyarakat Lio di Taman Nasional Kelimutu
Abstrak: Bare-throathed whistler is one of several wild birds in Kelimutu National Park (TNKL) that is believed to have correlation with Lio Community‟s belief. The estimation of Bare-throated whistler‟s population in Kelimutu National Park was about 186.41-1668.07 individual by the density about 0.0389-0.3481 ind/ha with the sex ratio 6.2:1 that spreaded on area with height of 1100 – 1600 masl. The characteristic of Bare-throated whistler‟s habitat is a natural forest with various canopy level, high plant species diversity and high density. The habitat characteristics related to the availability of food in the form of insects, cover, safety and a nesting site. Generally the most of Lio community knew the existence of Bare-throated whistler directly or from their parents‟ stories. The perceptions of Lio Community to the Bare-throated whistler‟s existence is generally support the conservation efforts. The perceptions construct the Lio Community‟s atitude that tend to be conservative to the existence of Bare-throated whistler. The attitude shown by their behavior that not cathing the Bare-throated whistler from the TNKL‟s area or the outside. The attitude have an important role to the existence of Bare-throated whistler in Kelimutu National Park‟s area.
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