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The Structural Annotations of The Mir-122 Non-Coding RNA from The Tilapia Fish (Oreochromis niloticus) Arli Aditya Parikesit; Imron Imron; Rizky Nurdiansyah; David Agustriawan
HAYATI Journal of Biosciences Vol. 29 No. 2 (2022): March 2022
Publisher : Bogor Agricultural University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.4308/hjb.29.2.171-181

Tilapia (Oreochromis niloticus) is an important fisheries commodity. Scientific efforts have been done to increase its quality. One of them is staging a premium diet such as a fat-enriched diet. The transcriptomics approach is able to provide the signatures of the diet outcomes by observing the micro(mi)RNA signature in transcriptional regulation. Hence, it was found that the availability of mir-122 is essential in the regulation of a high-fat diet in tilapia. However, this transcriptomics signature is lacking structural annotations and the complete interaction annotations with its silencing(si)RNA. RNAcentral website was navigated for the latest annotation of mir-122 from tilapia and other species as a comparison. MEGA X was employed to comprehend the miRNA evolutionary repertoire. The RNA secondary structure prediction tools from the Vienna RNA package and the RNA tertiary structure prediction tools from simRNA and modeRNA are secured with default parameters. The HNADOCK tools were leveraged to observe the interaction between mir-122 and its siRNA. The post-processing was conducted with the Chimera visualization tool. The secondary and tertiary structure of the mir-122 and its siRNA could be elucidated, docked, and visualized. In this end, further effort to develop a comprehensive molecular breeding tool could be secured with the structural annotation information.

PELATIHAN BERSERI TEKNOLOGI INFORMASI DAN KOMUNIKASI DI SEKOLAH DASAR MEKARSARI JAKARTA [A WORKSHOP SERIES OF INFORMATION COMMUNICATION AND TECHNOLOGY AT MEKARSARI ELEMENTARY SCHOOL JAKARTA] David Agustriawan; Arli Aditya Parikesit; Rizky Nurdiansyah
Jurnal Sinergitas PKM & CSR Vol 4, No 1 (2019): October
Publisher : Universitas Pelita Harapan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Industry 4.0 era needs to introduce current information communication and technology (ICT) to the society starting from the elementary school. However, Mekarsari Elementary school does not have the facility nor curriculum to prepare the students to face the era. This corporate social responsibility (CSR) aimed to introduce the kids with the current development of hardware and software. The participants for the series of the workshop are the 15 4th and 5th grade students and two teachers from Mekarsari Elementary school. The intervention was devised by providing user-friendly teaching-learning materials with hands-on activities related to the current development of ICT. The type of study is the “direct philanthropic giving” because it aims at providing knowledge for free. As the result, the students are familiar with: the type of computer’s hardware and software; python programming; budgeting for their daily allowance using Microsoft Excel; and creating a short story and presentation in Microsoft Word and PowerPoint. Based on the survey, the students could comprehend and enthusiastic to complete the hands-on activities. This CSR suggests that each elementary school should have a curriculum and computer laboratory to prepare the youth to compete in industry 4.0 era.

Protein Annotation of Breast-cancer-related Proteins with Machine-learning Tools Parikesit, Arli Aditya; Agustriawan, David; Nurdiansyah, Rizky
Makara Journal of Science Vol. 24, No. 2
Publisher : UI Scholars Hub

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One of the primary contributors to the mortality of women is breast cancer. Several approaches are used to cure it, but recurrence occurs in 79% of the cases because the underlying mechanism of the protein molecules is not carefully ex-amined. The goal of this research was to use machine-learning tools is to elucidate conserved regions and to obtain functional annotations of breast-cancer-related proteins. The sequences of five breast-cancer-related proteins (BRCA2, BCAR1, BCAR3, BCAR4, and BRMS1) and their annotations were retrieved from the UniProt and TCGA databases, respectively. Conserved regions were extracted using CLUSTALX. We constructed a phylogenetic tree using the MEGA 7.0. SUPERFAMILY database to obtain fine-grained domain annotation. The tree revealed that the BRCA2 and BCAR4 protein sequences are located in a clade, which indicates that they have overlapping functions. Several protein domains were identified, including the SH2 and Ras GEF domains in BCAR3, the SH3 domain in BCAR1, and the BRCA2 helical domain, the nucleic-acid-binding protein, and tower domain. We found that no protein domains could be annotated for BCAR4 or BRMS1, which may indicate the presence of a disordered protein state. We suggest that each protein has distinct functionalities that are complementary in regulating the progression of breast cancer, although further study is necessary for confirmation. This protein-domain annotation project could be leveraged by the complete integration of mapping with respect to gene and disease ontology. This type of leverage is vital for obtaining biochemical insights regarding breast cancer.

Workshop And Training Of The Drug-Drug Interaction Database Using Indonesian Drug Brand Names Arli Aditya Parikesit; David Agustriawan; Margareta Deidre Valeska; Andreas Whisnu; Moch Firmansyah; Ike Veneqe; Audrey Amira Crystalia
Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Vol 5, No 3 (2022): September 2022
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurdimas.v5i3.1574

Indonesia International Institute for Life Sciences (i3L) and Lira Medika Hospital have developed a customized drug-drug interaction database, named as DDIBase. It has a comprehensive library for providing annotation-specific information toward the drug targets, mechanism, and interaction with other drugs. Hence, DDIBase is a new database with a brand-new user interface as well. In this regard, as DDIBase has currently reached beta stage of development, it is required to provide more comprehensive user feedback to improve its features. Thus, a workshop has been devised to explain the user guidelines, while a training has been conducted to provide system demo and user hand-on. The workshop and training attendees are faculty members of the i3L and Lira Medika Hospital Staffs. A survey has been developed to cater the user satisfaction toward the DDIBase. In this end, the survey has elicited good user satisfaction toward the developed user interface.

Prostate Cancer Screening for Specific Races Using Bioinformatics and Artificial Intelligence on Genomic Data Agustriawan, David
Ultimatics : Jurnal Teknik Informatika Vol 16 No 2 (2024): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v16i2.3735

Prostate cancer is one of a deathly cancer worldwide. The higher incidence and mortality rate shows that it is an urgent call for all of us to fight against it in our own way. This study develops an artificial intelligence system to screening prostate cancer from normal patients in a specific race. Gene expression and its phenotype dataset was downloaded from xenabrowser.net Data preprocessing and filtering based on a particular race, bioinformatics computational analysis to determine the features and machine learning algorithm such as decision tree and random forest are used to develop AI model. All the procedure and analysis was performed using python programming The result show that only White and Black African American that has a proper number of dataset while Asian and American Indian has a very lack dataset. Differentially expression gene (DEG) analysis was performed to both White and Black African American cancer and normal dataset as a reference. 143 and 1 DEG are found in White and Black African American race respectively. ENSG00000225937.1 (PCA3) is identified as the highest up-regulated gene expression in cancer in both White and Black African American race. The results of DEG analysis then become features to develop Artificial Intelligence (AI) classification system. AI model was developed using decision tree and random forest with GriDSearch parameters optimization and stratified 10-fold cross validation. Both Decision tree and random forest model yield 96% accuracy in training dataset and 93% and 91% accuracy in testing dataset for decision tree and random forest, respectively.

Development of Double-Tail Generative Adversarial Network with Adaptive Style Transfer for Anime Background Production with Makoto Shinkai's Stylization Purwanto, Agus; Kusrini, Kusrini; Utami, Ema; Agustriawan, David
Scientific Journal of Informatics Vol. 12 No. 1: February 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i1.20850

Purpose: Traditionally, 2D anime production involves the expertise of experienced animators and is labor-intensive and time-consuming. Generative adversarial networks (GANs) have been developed to create high-quality anime over the years. However, the developed GANs still have caveats, such as the presence of artifacts, high-frequency noise, color and semantic structure mismatches, blurring, and texture issues. Additionally, research on AI-generated anime images with a particular style is still lacking. Thus, this study aimed to develop double-tail generative adversarial network (DTGAN) with adaptive style transfer to generate quality anime background images aligning with Makoto Shinkai's anime style. Methods: A dataset of real world and anime images was collected and preprocessed. The training was run, and an inference process was done to generate background images with the anime style of Makoto Shinkai using DTGAN with adaptive style transfer. Evaluations of the images produced were performed using visual comparison and quantitative analysis using Fréchet Inception Distance (FID) and peak signal-to-noise ratio (PSNR). Result: Compared to other methods, the images generated by DTGAN with adaptive style transfer had the lowest FID and highest PSNR values of.38.7 and 19.4 dB, respectively. Visual comparison of the images against other methods and real anime image of Makoto Shinkai demonstrated that images from DTGAN had the best quality that matched Makoto's style, as observed from color, background preservation, photorealistic style, and light contrast. Novelty: These findings suggest that DTGAN with adaptive style transfer using adaptive instance normalization (AdaIN) and linearly adaptive denormalization (LADE) outperforms other methods, highlighting its practical use for 2D anime production.

Prostate Cancer Screening for Specific Races Using Bioinformatics and Artificial Intelligence on Genomic Data Agustriawan, David
ULTIMATICS Vol 16 No 2 (2024): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v16i2.3735

Prostate cancer is one of a deathly cancer worldwide. The higher incidence and mortality rate shows that it is an urgent call for all of us to fight against it in our own way. This study develops an artificial intelligence system to screening prostate cancer from normal patients in a specific race. Gene expression and its phenotype dataset was downloaded from xenabrowser.net Data preprocessing and filtering based on a particular race, bioinformatics computational analysis to determine the features and machine learning algorithm such as decision tree and random forest are used to develop AI model. All the procedure and analysis was performed using python programming The result show that only White and Black African American that has a proper number of dataset while Asian and American Indian has a very lack dataset. Differentially expression gene (DEG) analysis was performed to both White and Black African American cancer and normal dataset as a reference. 143 and 1 DEG are found in White and Black African American race respectively. ENSG00000225937.1 (PCA3) is identified as the highest up-regulated gene expression in cancer in both White and Black African American race. The results of DEG analysis then become features to develop Artificial Intelligence (AI) classification system. AI model was developed using decision tree and random forest with GriDSearch parameters optimization and stratified 10-fold cross validation. Both Decision tree and random forest model yield 96% accuracy in training dataset and 93% and 91% accuracy in testing dataset for decision tree and random forest, respectively.

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