Judul: Deep Learning Optimation for Drug-Target Interaction with Graphic Processing Unit
Abstrak: Data drug-target interaction (DTI) yang semakin meningkat secara eksponensial menimbulkan permasalahan baru, yakni lamanya waktu komputasi. Besarnya data DTI tersebut menyebabkan lamanya waktu pelatihan model prediksi DTI untuk menentukan senyawa-senyawa potensial berkhasiat obat. Lamanya pengolahan data ini dapat berdampak pada waktu penelitian drug discovery yang lebih lama juga, karena tahap in silico biasa dilakukan di awal rangkaian penelitian, sehingga keterlambatan tahap ini membuat tahapan lainnya mengalami penundaan. Penelitian ini bertujuan untuk melihat seberapa baik performa GPU saat digunakan untuk permasalahan deep learning dalam rangka melakukan prediksi drug-target interaction. Data penelitian menggunakan lima dataset gold standard yakni enzyme, ion receptor, G-protein coupled receptor, dan nuclear receptor, serta dataset COVID-19. Metode yang digunakan adalah utilisasi algoritme deep learning secara paralel menggunakan GPU. Model yang dibangkitkan terdiri dari dua jenis, yakni yang menggunakan Central Processing Unit (CPU), dan menggunakan GPU. Kedua jenis model ini dibuat agar performa GPU dalam mengolah data dapat dibandingkan secara langsung dengan pengolahan data tanpa GPU. Setelah model dibuat, dilakukan hyperparameter tuning untuk menentukan hyperparameter yang optimal bagi masing-masing model. Performa model dihitung menggunakan parameter akurasi, recall, precision, F-measure, dan speed-up. Hasil dari penelitian ini menunjukkan bahwa penggunaan GPU dalam model deep learning dapat mempercepat proses pelatihan hingga 100 kali lipat. Selain itu, proses hyperparameter tuning juga sangat terbantu dengan adanya GPU karena dapat mempersingkat proses dari hingga 55 kali lebih cepat. Performa model deep learning yang dihasilkan dengan model CPU pun tidak jauh berbeda bila dibandingkan dengan model GPU dilihat dari nilai akurasi, recall, precision, dan F-measure yang tidak jauh berbeda. Saat dicobakan dengan dataset COVID-19, model menunjukkan performa yang baik dengan akurasi 76%, F-measure 74%, dan nilai speed-up 179. Data tersebut menunjukkan bahwa potensi GPU sangat besar apabila diintegrasikan dengan model deep learning. Penerapan lebih lanjut dapat digunakan dalam komputer server dan high performance computer untuk mengolah data bioinformatika yang lebih besar dan lebih kompleks., The exponentially increasing drug-target interaction (DTI) data raised a new problem, namely the length of computation time. The amount of DTI data that needs to be processed caused longer training time needed to do DTI prediction on potential drug compounds. The length of this computation time can have an impact on a longer drug discovery research time as well, because the in silico stage is usually carried out at the beginning of the research series, so the delay in this stage will cause other stages to be delayed. This study aims to see how well the GPU performs when used for deep learning problems in order to predict drug-target interactions. The research data uses five gold standard dataset which is enzyme, ion channel, G-protein coupled receptor, and nuclear receptor, as well as COVID-19 dataset. The method used is the utilization of deep learning algorithms in parallel using the GPU. The generated model consists of two types, namely those that use the Central Processing Unit (CPU), and those that use GPU. These two types of models are designed so that GPU performance in processing data can be compared directly with data processing without a GPU. After the model is created, hyperparameter tuning is performed to
Keyword: bioinformatika, deep learning, drug-target interaction, GPU, HPC

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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