Avian influenza subtype A/H9N2—which infects chickens, reducing egg production by up to 80%—may be transmissible to humans. In humans, this virus is very harmful since it attacks the respiratory system and reproductive tract, replicating in both. Previous attempts to find antiviral candidates capable of inhibiting influenza A/H9N2 transmission were unsuccessful. This study aims to better characterize A/H9N2 to facilitate the discovery of antiviral compounds capable of inhibiting its transmission. The Symmetry of this study is to apply several machine learning methods to perform virtual screening to identify H9N2 antivirus candidates. The parameters used to measure the machine learning model’s quality included accuracy, sensitivity, specificity, balanced accuracy, and receiver operating characteristic score. We found that the extreme gradient boosting method yielded better results in classifying compounds predicted to be suitable antiviral compounds than six other machine learning methods, including logistic regression, k-nearest neighbor analysis, support vector machine, multilayer perceptron, random forest, and gradient boosting. Using this algorithm, we identified 10 candidate synthetic compounds with the highest scores. These high scores predicted that the molecular fingerprint may involve strong bonding characteristics. Thus, we were able to find significant candidates for synthetic H9N2 antivirus compounds and identify the best machine learning method to perform virtual screenings.

Protein Interaction Analysis (PPI) can be used to identify proteins that have a supporting function on the main protein, especially in the synthesis process. Insulin is synthesized by proteins that have the same molecular function covering different but mutually supportive roles. To identify this function, the translation of Gene Ontology (GO) gives certain characteristics to each protein. This study purpose to predict proteins that interact with insulin using the centrality method as a feature extractor and extreme gradient boosting as a classification algorithm. Characteristics using the centralized method produces features as a central function of protein. Classification results are measured using measurements, precision, recall and ROC scores. Optimizing the model by finding the right parameters produces an accuracy of and a ROC score of . The prediction model produced by XGBoost has capabilities above the average of other machine learning methods.

The surge in cryptocurrency investors in Indonesia, reaching 18.83 million by January 2024, signifies an expanding interest in this market. This research conducts a sentiment analysis of user reviews on Indodax and Tokocrypto, the premier cryptocurrency trading platforms in Indonesia. Utilizing the Multinomial Naive Bayes method, the study examines the influence of various dataset split scenarios and random states on the model's performance. The findings reveal substantial variability in the model's accuracy based on different random states and test sizes. Notably, the Positive sentiment label consistently shows high-performance metrics, while the Neutral label underperforms. These insights are invaluable for developers aiming to improve user experience and for investors seeking to make informed decisions. This research underscores the significance of sentiment analysis in understanding user interactions and enhancing the credibility of cryptocurrency investment platforms.

Patogen penyebab Meningitis menyebabkan peradangan pada selaput otak. Kondisi ini menyebabkan kondisi kronis jika dibiarkan dalam jangka waktu lama. Patogen Meningitis menyerang protein tertentu yang berkaitan langsung dengan fungsional selaput otak. Dengan mendeteksi protein signifikan dari Meningitis dapat kita dapat menganalisis lebih jauh untuk menemukan inhibitor dari patogen tersebut. Menemukan protein yang signifikan dengan menganalisis jaringan interaksi protein yang terlibat ketika meningitis menginfeksi. Data jaringan protein diolah untuk mendapatkan klaster protein yang signifikan. Data jaringan pada klaster terbaik digunakan untuk mencari protein tertentu yang memiliki pengaruh signifikan. Skor sigifikansi protein berdasarkan nilai karakterisktik simpul pada jaringan graf dengan mendapatkan nilai eigen dan vektor eigen. Nilai keseluruhan karakteristik diperoleh dari hasil perkalian vektor eigen dengan vektor karakteristik simpul yang menghasilkan nilai skalar. Kami menemukan protein yang paling signifikan terhadap Meningitis adalah TLR2, hal ini diketahui dari nilai keseluruhan karakteristiknya yang paling tinggi dibandingkan protein lainnya.

In bioinformatics, graphs are often used to describe cell processes, representing interactions between proteins in a protein-protein interaction network. Some proteins significantly influence these tissues and play an essential role in a regulation called significant proteins. Significant proteins are functional as data for drug discovery. Graph analysis is needed to reduce the graph and find these significant proteins. So far, there have been no studies discussing protein interactions in avian influenza type A/H9N2, a zoonotic subtype of avian influenza. Although this virus is classified as Low Pathogenic Avian Influenza, it is troubling the public because it reduces the production of chicken eggs by 80% and disrupts the reproductive organs of mammals. This study aimed to find a significant protein from avian influenza type A/H9N2. From the results of this study, we found ten network clusters with the ClusterONE algorithm. We perform graph analysis on the first cluster because it is the best cluster with the smallest p-value of 0.000023. The cluster contains 20 nodes representing 20 proteins. The analysis graph (Analysis centrality) that is simulated in MATLAB includes betweenness centrality, closeness centrality, degree centrality, eigenvector centrality, and page rank centrality. Of the 20 proteins, nine significant proteins were obtained, namely CD247, CD48, FCGR2A, FCGR2B, IL10, PTPN22, PTPRC, SLAMF1, and TRL5, the highest score on the FCGR2A protein. The highest score indicates that the FCGR2A protein has the most dominant effect on the protein-protein interaction network of Avian Influenza virus type A/H9N2.

Meningitis is an infection of the lining of the brain caused by diffuse inflammation, and this condition is caused by viruses or bacteria that cause Meningitis. Prevention for this disease is still in the form of strengthening antibodies with vaccines. There is no significant compound to relieve or treat Meningitis patients. In previous studies, they got seven proteins vital to Meningitis. We continued to investigate the compounds associated with the seven proteins. We chose the in-silico process by utilizing data in an open database. We use several databases for the data collection process. After that, the compound data were extracted for bonding features and chemical elements using molecular fingerprints. We use two fingerprint methods, where both we combine with three types of combinations. The combined results produce three types of datasets with different matrix sizes. We establish the Extreme Gradient Boosting (XGB) method to form the classification model for the three datasets, select the best classification model, and compare it with other classification algorithms. The XGB model has better quality than the classification model of other algorithms. We used this model to predict and quantify compounds that strongly bind to seven vital meningitis proteins. The compound with the highest predictive score (we found more than 0.99) became a drug candidate to inhibit or neutralize Meningitis.