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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

Author-ID : 605955

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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Kontribusi Aplikasi Medis dari Ilmu Bioinformatika Berdasarkan Perkembangan Pembelajaran Mesin (Machine Learning) Terbaru Parikesit, Arli Aditya
Cermin Dunia Kedokteran Vol 45, No 9 (2018): Infeksi
Publisher : PT. Kalbe Farma Tbk.

Berkembangnya ilmu bioinformatika merupakan konsekuensi banyaknya data eksperimen laboratorium para peneliti biologi molekuler maupun 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.Â

Revolution in Detecting Tuberculosis using Radiology with Application of Deep Learning Algorithm Natalia Satya, Putri Gabriella Angel; Parikesit, Arli Aditya
Cermin Dunia Kedokteran Vol 48, No 4 (2021): Dermatologi
Publisher : PT. Kalbe Farma Tbk.

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 satu adalah tuberkulosis yang disebabkan oleh bakteri Mycobacterium tuberculosis yang menyerang paru-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.

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.

PENERAPAN PENDEKATAN MACHINE LEARNING PADA PENGEMBANGAN BASIS DATA HERBAL SEBAGAI SUMBER INFORMASI KANDIDAT OBAT KANKER Arli Aditya Parikesit, Rizky Nurdiansyah, dan David Agustriawan
Jurnal Teknologi Industri Pertanian Vol. 29 No. 2 (2019): Jurnal Teknologi Industri Pertanian
Publisher : Department of Agroindustrial Technology, Bogor Agricultural University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24961/j.tek.ind.pert.2019.29.2.175

Cancer is still an epidemiological disease in Indonesia. Drug development against cancer still relies to pharmacological laboratories and natural chemicals, which could have side effects. Cancer drug development has entered the stage of molecular biology, where the interaction of ligand chemical structure with receptor protein can be studied with high accuracy. Various chemical compounds, ranging from synthetic, semi-synthetic, to natural materials, developed for the purpose to fight one of the most dangerous diseases. In the context of the development of herbal-based drugs, there has been found heaps of natural compounds, curated and annotated, in various databases belonging to China, Taiwan, Indonesia, Japan, and several other countries. However, problems arise when choosing the best bioactive compounds to develop against cancer. Complexity arises because the metabolic pathway of cancer is very diverse, depending on the type and phase of cancer. Therefore, in this systematic review, we developed a machine learning approach to screen for these bioactive compounds, then took the best candidates for molecular simulation operations that would be tested for validity in wet experiments. Thus, the automation of the candidate drug development process for cancer could be achieved with great significance. It is known that the most effective and efficient machine learning method was Naïve Bayes, but the best in processing large amounts of compound data was classfier SVM. The future of complex bioactive compounds data could be secured by employing deep learning method. Keywords: machine learning, drug development, natural material compounds, metabolic pathways, cancer

PREDIKSI STRUKTUR 2-DIMENSI NON-CODING RNA DARI BIOMARKER KANKER PAYUDARA TRIPLE-NEGATIVE DENGAN VIENNA RNA PACKAGE Arli Aditya Parikesit; Dito Anurogo
Chimica et Natura Acta Vol 4, No 1 (2016)
Publisher : Departemen Kimia

Berdasarkan data WHO, kanker adalah penyakit yang paling berbahaya. Dewasa ini para peneliti sedang berusaha memahami mekanisme molekular kanker. Berdasarkan dogma sentral, hanya protein coding gene yang diketahui fungsinya, sementara non-coding gene belum dapat dijelaskan. Kemudian, diketahui bahwa non-coding RNA (ncRNA) berperan dominan dalam regulasi molekular sel, sehingga berpengaruh secara langsung kepada proliferasi kanker. Dalam hal ini, instrumen RNA-seq maupun Tiling Array sudah mengumpulkan banyak data biologis dan mendeposisikannya kepada database genom. Diketahui bahwa, ncRNA tidak hanya dapat berperan sebagai biomarker untuk diagnostik kanker, namun juga akan dapat dikembangkan sebagai agen terapeutik. Kanker payudara memiliki empat subtipe molekular, yaitu luminal A, luminal B, Her-2 dan triple negative/basal-like. Kanker Payudara Triple-negative (TNBC) merupakan penyakit yang sangat berbahaya dan belum ditemukan pengobatan yang efektif. Memahami mekanisme dan struktur ncRNA pada Biomarker TNBC merupakan langkah awal untuk menentukan agen terapeutik dan propilaksis terbaik. Diketahui bahwa jalur ekspresi lincRNA-RoR/miR-145/ARF6 berperan dalam proliferasi TNBC. Berdasarkan pencarian di GenBank, ditemukan lema-lema ncRNA untuk jalur tersebut. Hasil pencarian diolah dengan software Vienna RNA Package, untuk ditentukan struktur 2 dimensi (2-D) yang solid. Kedepannya, diharapkan dengan mencegah terbentuknya struktur 2-D tersebut, maka semua gen tersebut akan menjadi tidak aktif dan menghentikan proliferasi kanker.

Determination of secondary and tertiary structures of cervical cancer lncRNA diagnostic and siRNA therapeutic biomarkers Arli Aditya Parikesit; Didik Huswo Utomo; Nihayatul Karimah
Indonesian Journal of Biotechnology Vol 23, No 1 (2018)
Publisher : Universitas Gadjah Mada

Cervical cancer is one of the primary causes of mortality in women due to human papilloma virus (HPV) infection. The fingerprint of an HPV infection could be detected using a long non-coding RNA (lncRNA) biomarker, enabling it to be utilized in molecular diagnostics. The primary structure or sequences of RNA should be annotated within conventional bioinformatics tools. Therefore, this study aimed to determine the fine-grained 2D and 3D structures of lncRNA PVT1 and its respective siRNA inhibitors. lncRNA PVT1 sequences from Homo sapiens, Mus musculus, and Rattus norvegicus were retrieved from Genbank (NCBI). Prediction of the 2D structure and analysis of the interactions of the lncRNA and siRNA were performed using the Vienna RNA package. The 3D structure of the RNA was computed using the SimRNA and ModeRNA software programs. The results showed that lncRNA PVT1 from H. sapiens and M. musculus had a conserved region. However, the lncRNA from both H. sapiens and M. musculus showed a low conserved region, and the 2D structure could not be determined; thus, the annotation and 2D model focused only on H. sapiens. Both of their lncRNA PVT1 also had a short half-life in the cell. Based on the 3D modeling pipeline, the 3D model of lncRNA PVT1 showed the stability and possible function as molecules, while the PVT1 siRNA-lncRNA interaction analysis revealed that the molecules could bind well. Based on these findings, the structures of both lncRNA PVT1 and its siRNA have the potential to be utilized as biomarkers.

Computational modeling of AGO-mediated molecular inhibition of ARF6 by miR-145 Jeremias Ivan; Rizky Nurdiansyah; Arli Aditya Parikesit
Indonesian Journal of Biotechnology Vol 25, No 2 (2020)
Publisher : Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijbiotech.55631

Inhibition of ADP-ribosylation factor 6 messenger RNA (ARF6 mRNA) by microRNA-145 (miR-145), mediated by Argonaute (AGO) protein, has been found to play essential roles in several types of cancer and cellular processes. This study aimed to model the molecular interaction between miR-145 and ARF6 mRNA with AGO protein. The sequences of miR-145 and the 3’ untranslated region (UTR) of ARF6 mRNA were retrieved from miRTarBase, followed by miRNA target-site and structure predictions were done using RNAhybrid, RNAfold, and simRNAweb, respectively. The interaction between the miRNA-mRNA duplex and AGO was further assessed via molecular docking, interaction analysis, and dynamics, using PatchDock Server, PLIP, and VMD/NAMD, respectively. The models between miR-145, predicted target site of ARF6 mRNA, and AGO protein returned stable thermodynamic variables with negative free energy. Specifically, the RNA duplex had an energy of -19.80 kcal/mol, while the docking had -84.58 atomic contact energy supported by 70 hydrogen bonds and 14 hydrophobic interactions. However, the stability of the RMSD plot was still unclear due to limited computational resources. Nevertheless, these results computationally confirm favorable interaction of the three molecules, which can be utilized for further transcriptomics-based drugs or treatments.

KECEMASAN TERHADAP MATA AJAR ILMU PENGETAHUAN ALAM (IPA) PADA SISWA SEKOLAH DASAR DAN MENENGAH Arli Aditya Parikesit
Manajemen Pendidikan Vol. 14, No. 2, Tahun 2019
Publisher : Universitas Muhammadiyah Surakarta

Ilmu Pengetahuan Alam (IPA) merupakan mata ajar wajib yang diajarkan di sekolah dasar dan menegah. Permasalahan yang terjadi pada pembelajaran IPA adalah terjadinya kecemasan pada siswa. Kecemasan pada IPA terjadi di berbagai spektrum populasi siswa, mulai dari kelompok minoritas, sampai pada kebangsaan dan gender tertentu. Di Indonesia, kecemasan IPA juga telah terjadi, dan menjadi masalah serius. Saat ini beberapa solusi untuk mengatasi kecemasan IPA sudah dapat dilakukan, diantaranya melalui pembelajaran tematik, pengembangan sekolah anak berbakat, pendekatan psikoterapi, pendekatan spiritualisme, kondisi kelas yang positif, Problem Based Learning, dan teknologi informasi. Namun, keberhasilan semua pendekatan tersebut baru dapat terjamin jika guru dapat menjalankan peran sebagai motivator bagi siswa.

THE PREDICTED STRUCTURE FOR THE ANTI-SENSE SIRNA OF THE RNA POLYMERASE ENZYME (RDRP) GENE OF THE SARS-COV-2 Arli Aditya Parikesit; Rizky Nurdiansyah
BERITA BIOLOGI Vol 19, No 1 (2020)
Publisher : Research Center for Biology-Indonesian Institute of Sciences

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/beritabiologi.v19i1.3849

The SARS-CoV-2 or COVID-19 pandemic has reached a new height with an unprecedented infection rate and mortality post-world war II history. However, there is no particular designed drug for COVID-19 up to this point. Thus, there exist three strategies for COVID-19 drug design; drug repurposing option, herbal medicine development, and transcriptomics-based drug lead. As the most underutilized option, transcriptomics-based drug lead could be leveraged to deal with SARS-CoV-2 infection. One of the main methods to block the SARS-CoV-2 infection is to inhibit the RNA polymerase enzyme that is responsible to the viral replication. In this regard, the objective of the strategy is to design the anti-sense siRNA drug and lead to inhibit the mRNA of the RNA Polymerase Enzyme (RdRp) gene that encodes the viral RNA Polymerase of the SARS-CoV-2. The Computer-Aided Drug Design (CADD)-based method was leveraged with sequence retrieval of 24 RdRp gene sequences, multiple sequence alignment, phylogenetic tree reconstruction, 2D/3D RNA structure predictions, and RNA-RNA docking. Both the RNAalifold conserved structure from the RdRp genes and the RNAfold structure of the siRNA for blocking the conserved structure are negative or less than 0 kcal/mol. The predicted RNA docking occurred with the best RMSD score of 22.53 Å, which is beyond the accepted threshold of 10-20 Å. Based on the findings, the 2D/3D structures of both the siRNA and mRNA could be elucidated, and the docking between them is feasible. However, this finding should be elucidated in the wet laboratory setting for the final lead validation.

Bioinformatics Approach towards Transcriptomics of Filaggrin Dito Anurogo; Arli Aditya Parikesit
Journal of Agromedicine and Medical Sciences Vol 2 No 3 (2016)
Publisher : Faculty of Medicine, University of Jember

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

Filaggrin, or filaments which combines protein, is one of the important structural protein that works for the development, maintenance, and the formation of the skin as an intact barrier. Filaggrin breakdown products regulate the hydration of the skin; contribute to the acidic pH of the skin, which in turn is essential for the activity of various proteases in the stratum corneum desquamation and lipid synthesis. Filaggrin produced by keratinocytes granular as a major precursor called profilaggrin, encoded by the FLG gene, located in the epidermal differentiation complex on chromosome 1 (1q21 locus). The locus contains a group of genes involved in epidermal differentiation. Filaggrin deficiency has some consequences on the organization and function of epidermal with important implications such as increased risk for atopic disease or a microbial infection. FLG mutation, a gene that encodes filaggrin, has been shown to cause ichthyosis vulgaris, increasing the risk of atopic dermatitis and other atopic diseases. This research examined the FLG gene based bioinformatics approach to search for conserved region of representative mammals that encode coding (m) and non-coding (nc) RNAs. Expected mRNA expression can be used as a diagnostic and therapeutic agent against deficiencies and filaggrin mutations.Key words: filaggrin, FLG, profilaggrin, filaggrin deficiency, bioinformatics.

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

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