An immense volume of digital documents exists online and offline with content that can offer useful information and insights. Utilizing topic modeling enhances the analysis and understanding of digital documents. Topic modeling discovers latent semantic structures or topics within a set of digital textual documents. The Internet of Things, Blockchain, recommender system, and search engine optimization applications use topic modeling to handle data mining tasks, such as classification and clustering. The usefulness of topic models depends on the quality of resulting term patterns and topics with high quality. Topic coherence is the standard metric to measure the quality of topic models. Previous studies build topic models to generally work on conventional documents, and they are insufficient and underperform when applied to web content data due to differences in the structure of the conventional and HTML documents. Neglecting the unique structure of web content leads to missing otherwise coherent topics and, therefore, low topic quality. This study aims to propose an innovative topic model to learn coherence topics in web content data. We present the HTML Topic Model (HTM), a web content topic model that takes into consideration the HTML tags to understand the structure of web pages. We conducted two series of experiments to demonstrate the limitations of the existing topic models and examine the topic coherence of the HTM against the widely used Latent Dirichlet Allocation (LDA) model and its variants, namely the Correlated Topic Model, the Dirichlet Multinomial Regression, the Hierarchical Dirichlet Process, the Hierarchical Latent Dirichlet Allocation, the pseudo-document based Topic Model, and the Supervised Latent Dirichlet Allocation models. The first experiment demonstrates the limitations of the existing topic models when applied to web content data and, therefore, the essential need for a web content topic model. When applied to web data, the overall performance dropped an average of five times and, in some cases, up to approximately 20 times lower than when applied to conventional data. The second experiment then evaluates the effectiveness of the HTM model in discovering topics and term patterns of web content data. The HTM model achieved an overall 35% improvement in topic coherence compared to the LDA.
Query expansion is the process of reformulating an original query to improve retrieval performance in information retrieval systems. Relevance feedback is one of the most useful query modification techniques in information retrieval systems. In this paper, we introduce query expansion into ligand-based virtual screening (LBVS) using the relevance feedback technique. In this approach, a few high-ranking molecules of unknown activity are filtered from the outputs of a Bayesian inference network based on a single ligand molecule to form a set of ligand molecules. This set of ligand molecules is used to form a new ligand molecule. Simulated virtual screening experiments with the MDL Drug Data Report and maximum unbiased validation data sets show that the use of ligand expansion provides a very simple way of improving the LBVS, especially when the active molecules being sought have a high degree of structural heterogeneity. However, the effectiveness of the ligand expansion is slightly less when structurally-homogeneous sets of actives are being sought.
The purpose of this study was to simultaneously predict the drug release and skin permeation of Piroxicam (PX) topical films based on Chitosan (CTS), Xanthan gum (XG) and its Carboxymethyl derivatives (CMXs) as matrix systems. These films were prepared by the solvent casting method, using Tween 80 (T80) as a permeation enhancer. All of the prepared films were assessed for their physicochemical parameters, their in vitro drug release and ex vivo skin permeation studies. Moreover, deep learning models and machine learning models were applied to predict the drug release and permeation rates. The results indicated that all of the films exhibited good consistency and physicochemical properties. Furthermore, it was noticed that when T80 was used in the optimal formulation (F8) based on CTS-CMX3, a satisfactory drug release pattern was found where 99.97% of PX was released and an amount of 1.18 mg/cm2 was permeated after 48 h. Moreover, Generative Adversarial Network (GAN) efficiently enhanced the performance of deep learning models and DNN was chosen as the best predictive approach with MSE values equal to 0.00098 and 0.00182 for the drug release and permeation kinetics, respectively. DNN precisely predicted PX dissolution profiles with f2 values equal to 99.99 for all the formulations.
Orthosiphon stamineus (OS) is a popular medicinal herb used in traditional Chinese medicine as a diuretic agent and for renal system disorders. This study employed 1H NMR based metabolomics approach to investigate the possible protective activity of OS in cisplatin induced nephrotoxicity owing to its diuretic and antioxidant activities. Aqueous (OSAE) and 50% aqueous ethanolic (OSFE) extracts of OS leaves were orally administered at 400mg/kg BW doses to rats which were then intraperitoneally injected with cisplatin at 5mg/kg BW dose. The 1H NMR profile of the urine samples collected on day 5 after cisplatin administration were analyzed by multivariate pattern recognition techniques, whereby 19 marker metabolites suggestive in the involvement of TCA cycle, disturbed energy metabolism, altered gut microflora and BCAA metabolism pathways were identified. It was observed that OSFE caused significant changes (p<0.05) in the levels of 8 markers namely leucine, acetate, hippurate, lysine, valine, 2-oxoglutarate, 3-HBT and acetoacetate resulting in a moderate ameliorative effect, however, it did not completely protect from nephrotoxicity. OSAE did not demonstrate significant down regulatory effects on any markers, albeit, it potentiated the cisplatin nephrotoxicity by inducing significant increase in glucose, glycine, creatinine, citrate, TMAO, acetate and creatine levels. A Principal Component Analysis (PCA) of the 1H NMR spectra of OS extracts identified that OSFE had higher concentrations of the secondary metabolites such as caffeic acid, chlorogenic acid, protocatechuic acid and orthosiphol, among others. Whereas, OSAE was characterized by higher concentrations of acetate, lactate, succinic acid, valine and phosphatidylcholine. This research denotes the first comprehensive analysis to identify the effects of OS extracts on cisplatin nephrotoxicity.