4 research outputs found

    The Structural Annotations of The Mir-122 Non-Coding RNA from The Tilapia Fish (Oreochromis niloticus)

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    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

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    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

    IN SILICO STUDY OF MIRNA-REGULATED IQ MOTIF-CONTAINING GTPASE-ACTIVATING PROTEIN FAMILY IN LIVER CANCER

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    Objective: The aim of this paper is to identify the list of microRNA (miRNA) which can regulate the aberrant expression of IQGAP in liver cancer formation. The aberrant expression of IQ motif-containing GTPase-activating protein (IQGAP) family which consists of IQGAP1, IQGAP2, and IQGAP3 has been linked to carcinogenesis in human cancers. The reciprocal expression of IQGAP family in human cancer has been studied to act as oncogenes or tumor suppressor genes. A growing number of studies suggest that upregulated or downregulated expression of IQGAP family triggers cancer development.Methods: A correlation study was performed to construct a pathway to inhibit or activate IQGAP family between miRNAs and IQGAPs. A pre-processing step was conducted to download, filter and process the dataset from TCGA. It yields miRNA and IQGAP gene expression matrix. Then, correlation computation was computed using MATLAB. Moreover, this study linked the results to the MiRTarBase to validate the prediction result with the wet lab experimental result.Results: This study identified significantly inversely correlation in 51 miRNAs-IQGAP1, 169 miRNAs-IQGAP2, and 33 miRNAs-IQGAP3, respectively, which may potentially play a role in a liver cancer formation. Some of the results also can be found in miRTarBase. It supports the precision of those miRNA and IQGAP interaction between dry lab and wet lab study. IQGAP1 and IQGAP2 mostly has been identified as an oncogene in cancer but IQGAP2 has been discovered as tumor suppressor gene. The list of miRNA in the result of this study can become a potential therapy to target the aberrant expression of IQGAP family.Conclusion: miRNA function is known as an oncogene or tumor suppressor gene in cancer development. Therefore, it can be one of the important molecular biology which may target the aberrant expression of IQGAP in liver cancer

    Pengaruh Beban Kerja, Kompensasi, dan Motivasi Kerja terhadap Produktivitas Kerja melalui Kinerja Karyawan sebagai Intervening

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    Produktivitas kerja merupakan salah satu tujuan Perusahaan karena dapat mempengaruhi profitabilitas dan daya saing sebuah Perusahaan. Jika produktivitas kerja sebuah Perusahaan rendah maka akan mengalami kerugiaan dan penurunan reputasi Perusahaan. Salah satu faktor yang mempengaruhi produktivitas kerja adalah beban kerja, kompensasi, motivasi kerja, dan kinerja karyawan yang menjadi variabel-variabel yang akan diteliti. Pengumpulan data ini dilakukan penyebaran kuisioner, data yang berhasil didapatkan sebanyak 126 data dan variabel dan indikator dibangkitkan melalui studi literatur. Tujuan dilakukannya penelitain ini adalah untuk mengetahui pengaruh variabel beban kerja, kompensasi, motivasi kerja terhadap produktivitas kerja melalui kinerja karyawan sebagai variabel intervening. Metode yang cocok digunakan untuk mengetahui pengaruh variabel satu dengan variabel lain adalah metode structural equation modelling (SEM). Hasil penelitian ini adalah nilai p-value sebesar 0.079, nilai tersebut sudah memenuhi syarat minimum goodness of fit dan diperoleh. Dari hasil hubungan antara variabel diketahui bahwa Beban kerja berpengaruh negatif terhadap kinerja pegawai, kompensasi berpengaruh positif terhadap kinerja pegawai, motivasi kerja berpengaruh positif terhadap kinerja pegawai, kinerja pegawai berpengaruh positif terhadap produktivitas kerja
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