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All Journal HAYATI Journal of Biosciences Jurnal Teknologi Industri Pertanian Indonesian Journal of Biotechnology Journal of Agromedicine and Medical Sciences Journal of Tropical Biodiversity and Biotechnology Manajemen Pendidikan BERITA BIOLOGI JURNAL BIOLOGI INDONESIA Jurnal Teknologi Laboratorium Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Chimica et Natura Acta Indonesian Journal of Chemistry Jurnal Sinergitas PkM & CSR FaST : Jurnal Sains dan Teknologi Walisongo Journal of Chemistry Menara Perkebunan Jurnal Teknik Informatika (JUTIF) Bioinformatics and Biomedical Research Journal Cermin Dunia Kedokteran Indonesian Journal of Life Sciences Makara Journal of Science Makara Journal of Technology Biosaintifika: Journal of Biology & Biology Education Jurnal Bioteknologi & Biosains Indonesia (JBBI) Cermin Dunia Kedokteran Kesmas: Jurnal Kesehatan Masyarakat Nasional (National Public Health Journal) Jurnal Bioteknologi & Biosains Indonesia Berita Biologi
Arli Aditya Parikesit
Department Of Bioinformatics, School Of Life Sciences, Indonesia International Institute For Life Sciences (i3l), Jakarta, Indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Other

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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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Abstract

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.
COVID-19 In Silico Drug with Zingiber officinale Natural Product Compound Library Targeting the Mpro Protein Wijaya, Renadya Maulani; Hafidzhah, Muhammad Aldino; Kharisma, Viol Dhea; Ansori, Arif Nur Muhammad; Parikesit, Arli Aditya
Makara Journal of Science Vol. 25, No. 3
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Coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a worldwide pandemic. Ginger (Zingiber officinale) is a rhizome, which is commonly used for culinary and medicinal purposes. In Indonesia, ginger is taken as traditional medicine by processing it into a drink known as jamu. The present study aimed to assess and evaluate the bioactive compounds in ginger that can be used in drug design for treating COVID-19. The crystal structure of the SARS-CoV-2 main protease (Mpro) was generated from a protein sequence database, i.e., Protein Data Bank, and the bioactive compounds in ginger were derived from the existing compounds library. Mpro is involved in polyprotein synthesis, including viral maturation and nonstructural protein gluing, making it a potential antiviral target. Furthermore, the bioactive compounds in ginger were analyzed using Lipinski’s rule of five to determine their drug-like molecular properties. Moreover, molecular docking analysis was conducted using the Python Prescription 0.8 (Virtual Screening Tool) software, and the interaction between SARS-CoV-2 Mpro and the bioactive compounds in ginger was extensively examined using the PyMOL software. Out all of the 16 bioactive compounds that were docked successfully, 4-gingerol, which has the lowest binding energy against SARS-CoV-2 Mpro, as per the virtual screening results, was proven to have the most potential as a viral inhibitor of SARS-CoV-2
Use of the “DNAChecker” Algorithm for Improving Bioinformatics Research Bhat, Nausheen; Wijaya, Ezra Bernadus; Parikesit, Arli Aditya
Makara Journal of Technology Vol. 23, No. 2
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Basic Local Alignment Sequencing Tool (BLAST) is a bioinformatics tool used for analyzing nucleotide sequences with regards to their similarity. BLAST can be found online on biological databases such as the National Center for Biotechnology Information (NCBI) and other such repositories. The mechanism of BLAST allows the target sequence to be compared with other sequences to find regions of local similarity, and thus, a comparability quotient that determines the resemblance between the sequences is created. Due to the open-platform nature of the online databanks, several sequences can be accepted with little to no interjections regarding the quality of sequence submitted. An example of unclean nucleotide sequences can be based on the number of non-template nucleotides, denoted as “N,” present within the sequence. Here we develop a self-established nucleotide sequence reading program known as “DNAChecker,” which helps identify the quality of the target sequence and therefore proposes the effectiveness of the BLAST result. DNAChecker is an inbuilt, program that runs on Python 3.4 and was implemented in the United States Agency for International Development (USAID) project conducted in Indonesia International Institute for Life Sciences. Although DNAChecker has proven to be useful, it has a lot of room for improvements, such as having a more objectively accurate means of differentiating between good and bad sequences.
Troubled Helix – Tinjauan Multiperspektif Genetika dalam Bioetika Dito Anurogo; Arli Aditya Parikesit
Cermin Dunia Kedokteran Vol. 48 No. 3 (2021): Obstetri - Ginekologi
Publisher : PT Kalbe Farma Tbk.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55175/cdk.v48i3.50

Abstract

Dalam review ini, dibahas tinjauan multiperspektif genetika dalam bioetika. Dikemukakan prinsip-prinsip etika mutakhir, seperti: reciprocity, mutuality, solidarity, citizenry, dan universality. Dibahas pula prinsip-prinsip etika dan pemeriksaan genetika, seperti: otonomi, privasi, kebaikan, nonmaleficence, keadilan. Didiskusikan pula perspektif etnokultural dalam layanan genetika, milestones guideline etika dan regulasi riset biomedis internasional, prinsip-prinsip etika menurut Universal Declaration on Bioethics and Human Rights 2005, hak asasi manusia dan etika profesional: apresiasi translasional, perspektif utilitarianisme, perspektif deontologi, “simalakama” pemeriksaan genetika, globalisasi bioetika, etika bioinformatika, dan riset eugenik. In this review, a multiperspective review of genetics in bioethics is discussed. The latest ethical principles are mentioned, such as: reciprocity, mutuality, solidarity, citizenry, and universality. The principles of ethics and genetic inquiry, such as: autonomy, privacy, kindness, nonmaleficence, justice was also discussed. Also discussed are multiperspective, ethnocultural perspectives in genetic services, milestones of ethical guidelines and international biomedical research regulations, ethical principles according to the Universal Declaration on Bioethics and Human Rights 2005, human rights and professional ethics: translational appreciation, utilitarianism perspective, deontological perspective, the “simulacra” of genetic examination, bioethics globalization, bioinformatics ethics, and eugenic research.
Revolution in Detecting Tuberculosis using Radiology with Application of Deep Learning Algorithm Putri Gabriella Angel Natalia Satya; Arli Aditya Parikesit
Cermin Dunia Kedokteran Vol 48 No 4 (2021): Dermatologi
Publisher : PT Kalbe Farma Tbk.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55175/cdk.v48i4.70

Abstract

Radiology is a medical examination of internal body parts using data imaging to interpret an illness. Many illnesses can be detected using this medical discipline; one of the diseases is tuberculosis caused by Mycobacterium tuberculosis bacteria. The supreme ability of Artificial Intelligence and Machine learning has amazed the radiologist in analyzing big data-based information. A better deep learning algorithm can lead radiologist to accurate results. This article will review ten (10) research papers that use a deep learning algorithm in the application to detect tuberculosis by data processing technique. The goal is to know the best type of data processing in deep learning to detect TB. Radiologi adalah pemeriksaan bagian dalam tubuh menggunakan data pencitraan untuk interpretasi suatu penyakit. Banyak penyakit dapat dideteksi menggunakan disiplin medis ini; salah satunya adalah tuberkulosis yang disebabkan oleh bakteri Mycobacterium tuberculosis yang menyerang paru. Ahli radiologi tertarik atas kemampuan Artificial Intelligence dan Machine Learning untuk analisis data yang akurat. Artikel ini akan mengulas sepuluh (10) makalah penelitian aplikasi algoritma deep learning untuk deteksi tuberkulosis menggunakan teknik pengolahan data
Use of Artificial Intelligence in the Diagnostics of Autism Spectrum Disorder Gabriele Mustika Kresnia; Arli Aditya Parikesit
Cermin Dunia Kedokteran Vol 49 No 6 (2022): Nutrisi
Publisher : PT Kalbe Farma Tbk.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55175/cdk.v49i6.246

Abstract

Autism Spectrum Disorder (ASD) is a neurologic development disorder; it is listed in the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-V). Early diagnosis is critical in improving the life quality of individuals affected by ASD. Several studies show that Artificial Intelligence can be used in the diagnosis of ASD through biological means such as observing patient EEG data and surveying their genome. Articles were searched in the PUBMED database, ScienceDirect, and Springer Link between 2019 - 2020. Four papers were selected for review. The papers devised models that can accurately predict ASD in affected individuals, though some are based on old data and/or require testing on larger datasets to determine accuracy. As ASD diagnosis usually cannot be achieved before the individual shows symptoms, AI has the potential to improve ASD diagnosis in affected individuals. Further study to confirm the models and test on larger, more recent datasets would be required to develop more accurate models and achieve even better results. Autism spectrum disorder (ASD) merupakan salah satu gangguan perkembangan saraf yang tercantum pada Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-V). Diagnosis dini sangat penting untuk meningkatkan kualitas hidup individu ASD. Beberapa penelitian menunjukkan bahwa kecerdasan buatan dapat digunakan untuk diagnosis ASD melalui metode berbasis biologis seperti mengamati data EEG pasien dan mensurvei genomnya. Review ini berbasis pencarian data antara tahun 2019 – 2020 di database PUBMED, ScienceDirect, dan Springer Link. Empat makalah kunci dipilih untuk ditinjau. Makalah-makalah tersebut mampu merancang model yang dapat memprediksi ASD secara akurat, meskipun beberapa aspek implementasinya didasarkan pada data usang dan/ atau memerlukan pengujian pada kumpulan data yang lebih besar untuk menentukan akurasi. Mengingat diagnosis ASD biasanya tidak dapat dilakukan sebelum individu menunjukkan gejala, kecerdasan buatan berpotensi meningkatkan ketepatan diagnosis ASD. Masih diperlukan studi lanjutan untuk mengkonfirmasi model dan pengujian pada kumpulan data yang lebih besar dan lebih baru untuk mengembangkan model yang memiliki presisi lebih baik dan hasil lebih akurat.
Drug Repurposing Option for COVID-19 with Structural Bioinformatics of Chemical Interactions Approach Arli Aditya Parikesit; Rizky Nurdiansyah
Cermin Dunia Kedokteran Vol. 47 No. 3 (2020): Dermatologi
Publisher : PT Kalbe Farma Tbk.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55175/cdk.v47i3.359

Abstract

The SARS-CoV-2 virus is the pathogenic agent that caused the COVID-19 disease. The epicenter of this disease is the city of Wuhan, China. It is already categorized as “pandemic” by WHO, as many countries already affected with the infections, including recently Indonesia. Although the standard RT-PCR and DNA sequencing protocols has already developed for diagnostic, no drugs are available to cure this disease until today. The anti-malaria drug of chloroquine phosphate was repurposed, as well as other anti-viral drugs. In this regard, a structural bioinformatics pipeline was utilized to validate the claim in the computational realm. Within the sphere of the online molecular docking method, it was found that all the tested repurposed drugs attached accordingly with the SARS-CoV-2 protease enzyme that plays a role in viral replication. The repurposed drugs could be proposed as drug candidates for COVID-19, after clinical trials or further laboratory testing. Virus SARS-CoV-2 adalah patogen penyebab penyakit COVID-19. Episentrum penyakit ini adalah kota Wuhan, Tiongkok. WHO mengeluarkan peringatan ‘pandemi’ karena banyak negara sudah terkena infeksi, termasuk Indonesia. Meskipun protokol RT-PCR dan sekuensing DNA standar telah dikembangkan untuk tujuan diagnostik, hingga saat ini tidak ada obat untuk menyembuhkan penyakit ini. Obat anti-malaria chloroquine phosphate dicoba, bersama dengan beberapa obat anti-virus. Alur analisis bioinformatika struktural digunakan untuk validasi di ranah komputasi. Dalam lingkup metode molecular docking secara online, ditemukan bahwa obat tersebut tertambat dengan enzim protease SARS-CoV-2 yang berperan dalam replikasi virus. Obat ini dapat diusulkan sebagai kandidat obat untuk COVID-19, setelah pengujian laboratorium dan uji klinis lebih lanjut.
The Role of Bioinformatics in Personalized Medicine: Your Future Medical Treatment Margareta Deidre Valeska; Gabriella Patricia Adisurja; Stefanus Bernard; Renadya Maulani Wijaya; Muhammad Aldino Handzhah; Arli Aditya Parikesit
Cermin Dunia Kedokteran Vol 46 No 12 (2019): Kardiovakular
Publisher : PT Kalbe Farma Tbk.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55175/cdk.v46i12.399

Abstract

Bioinformatics is beneficial in personalized medicine. Two methods stand out, the randomized algorithm and computer assisted drug design (CADD). This article will discuss application, pitfalls, and future of those two methods. Suggestion to improve the clarity of the bioinformatics research in the field of personalized medicine will also be reviewed. Bioinformatika berperan sangat penting dalam personalized medicine. Dua metode penting dalam kajian ini adalah randomized algorithm dan computer assisted drug design (CADD). Kajian ini membahas aplikasi, kekurangan, dan masa depan kedua metode tersebut. Saran-saran untuk meningkatkan efek riset bioinformatika dalam kajian personalized medicine juga akan ditelaah.
Kontribusi Aplikasi Medis dari Ilmu Bioinformatika Berdasarkan Perkembangan Pembelajaran Mesin (Machine Learning) Terbaru Arli Aditya Parikesit
Cermin Dunia Kedokteran Vol 45 No 9 (2018): Infeksi
Publisher : PT Kalbe Farma Tbk.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55175/cdk.v50i9.729

Abstract

Berkembangnya ilmu bioinformatika merupakan konsekuensi banyaknya data eksperimen laboratorium para peneliti biologi molekuler ataupun biomedis. Selain pengembangan basis data terpusat yang merupakan kompetensi inti ilmu bioinformatika, pendekatan komputasi lain seperti pembelajaran mesin juga dikembangkan, sehingga data tersebut dapat diolah menjadi informasi yang berguna bagi dunia kesehatan. Kajian ini akan menelaah perkembangan pendekatan pembelajaran mesin pada ilmu bioinformatika, dan aplikasinya pada dunia kesehatan terutama pada informatika kanker dan virus. Masa depan aplikasi medis dengan ilmu bioinformatika menarik karena melibatkan berbagai pendekatan baru seperti kecerdasan buatan dan biologi sistem. The development of bioinformatics science is a consequence of the massive data generation of laboratory experiments conducted by molecular biology and biomedical researchers. In addition to the development of a centralized database that is the core competence of bioinformatics science, other computing approaches such as machine learning are also developed so that the data can be processed into useful information for the human health. This review will examine the development of machine learning approaches in bioinformatics science, and its application to the human health, especially in cancer and virus informatics. The future of medical applications with bioinformatics science is exciting as it involves various new approaches such as artificial intelligence and system biology.
STUDI IN SILICO MODIFIKASI POS TRANSLASI DISAIN VAKSIN CHIMERIC BERBASIS VIRUS LIKE PARTICLES HUMAN PAPILLOMAVIRUS DENGAN KAPSID VIRION L1 Tambunan, Usman Sumo Friend; Parikesit, Arli Aditya; Tochary, Theo A.; Sugiono, Dedy
Makara Journal of Science Vol. 11, No. 2
Publisher : UI Scholars Hub

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Abstract

Computational Study of Post Translation Modification in Chimeric Virus Like Particles Vaccine of Human Papilloma Virus with Virion Capsid L1. The Human Papillomavirus (HPV) infection has a tight correlation with the incidence of cervical cancer. Chimeric virus like particles (cVLP) has been developed as vaccine candidate for preventing cervical cancer. cVLPs are improvement of Virus Like Particles (VLP) by substituting the epitope of L1 HPV -18 and -52 protein to L1 HPV -16 protein. They are ANN1, ANN2, HMM1, and HMM2. The impact of post translation modification will be determined. Based on In Silico study, the dominant post translation modification is glycosylation
Co-Authors Adi Sofyan Ansori, Muhammad Albert Widjaja Aldino Hafidzhah, Muhammad Alhussain, Shaheer Alyaa Farrah Dibha Angelique, Priscilla Arif Nur Muhammad Ansori Bernard, Stefanus Bhat, Nausheen Burkov, Pavel Chandra, Nelson David Agustriawan Dedy Sugiono Deidre Valeska, Margareta Derkho, Marina Dian, Farida Aryani Didik Huswo Utomo Dito Anurogo Dito ANUROGO Dito Anurogo Dito Anurogo Dito Anurogo Dito Anurogo Dito Anurogo, Dito Ema Utami Ezra Bernandus Wijaya Fugaha, Daniel Ryan Gabriela, Vania Gabriele Mustika Kresnia Gabriella Patricia Adisurja Hafidzhah, Muhammad Aldino Heerlie, Devita Mayanda Herdiansyah, Mochammad Aqilah Hutapea, Hotma Martogi Lorensi Imron Imron Jakhmola, Vikash Jeremias Ivan Josephine, Evalina Junaida Astina Karimah, Nihayatul Karimah, Nihayatul Kharisma, Viol Dhea KUSRINI Kusrini, Kusrini Maksim Rebezov Margareta Deidre Valeska Margaretha, Febrina Maria Kiseleva Maulani Wijaya, Renadya Miko Wahyono, Tri Yunis Muhammad Aldino Hafidzhah Muhammad Aldino Handzhah Muhammad Hermawan Widyananda Murtadlo, Ahmad Affan Ali Nadezhda Kenijz Natalia Satya, Putri Gabriella Angel Nelda Aprilia Salim Nihayatul Karimah Patricia Adisurja, Gabriella Patricia, Gabriella Posa, Gabrielle Ann Villar Prakoso, Muhammad Ja'far Pratama, Rico Alexander Putri Gabriella Angel Natalia Satya Rahadian Zainul Ramanto, Kevin Nathanael Ratnasari, Nanda Risqia Pradana Renadya Maulani Wijaya Ridarto, Afif Maulana Yusuf Riza A PUTRANTO Rizky Nurdiansyah Rizky, Wahyu Choirur Ryan Fugaha, Daniel Ryan Wijaya Ryan Wijaya, Ryan Satrio Wibowo Scherbakov, Pavel Sepiashvili, Ekaterina Shemuel, Josia Sofy Permana Sri Wahyuningsih Stefanus Bernard Sudaryo, Mondastri Korib Sugiono, Dedy Svetlana Artyukhova Tambunan, Usman Sumo Friend Teguh Hari Sucipto, Teguh Hari Theo A Tochary Tochary, Theo A. Usman Sumo Friend Tambunan Utomo, Didik Huswo Utomo, Didik Huswo Vikash Jakhmola Viol Dhea Kharisma Wicaksono, Adhityo Wijaya, Renadya Maulani Yanuargi, Bayu Yulia Matrosova