Region-Based Distance Analysis of Keyphrases: A New Unsupervised Method for Extracting Keyphrases Feature from Articles

Due to the exponential growth of information’s and web sources, Automatic keyphrase extraction is still a challenging issue in the current research area. Keyphrases are very helpful for several tasks in natural language processing (NLP) and information retrieval (IR) systems. Feature extractions for those keyphrases execute a vital role in extracting the top-quality keyphrases and summarising the documents at a superior level. This paper proposes a new region-based distance analysis of keyphrases (RDAK) unsupervised technique for feature extraction of keyphrases from articles. The proposed method comprises six phases: data acquisition and preprocessing, data processing, distance calculation, average distance, curve plotting, and curve fitting. At first, the system inputs the collected different datasets to the preprocessing step by employing some text preprocessing techniques. Afterwards, the preprocessed data is applied to the data processing phase, and then after distance calculation, it is passed to the region-based average calculation process, then curve plotting analysis, and afterwards, the curve fitting technique is utilized. Finally, the proposed system has tested and evaluated the performance through implementing them on benchmark datasets. The proposed system will significantly improve the performance of existing keyphrase extraction techniques

Keyphrases Concentrated Area Identification from Academic Articles as Feature of Keyphrase Extraction: A New Unsupervised Approach

The extraction of high-quality keywords and sum-marising documents at a high level has become more difficult in current research due to technological advancements and the expo-nential expansion of textual data and digital sources. Extracting high-quality keywords and summarising the documents at a high-level need to use features for the keyphrase extraction, becoming more popular. A new unsupervised keyphrase concentrated area (KCA) identification approach is proposed in this study as a feature of keyphrase extraction: corpus, domain and language independent; document length-free; utilized by both supervised and unsupervised techniques. In the proposed system, there are three phases: data pre-processing, data processing, and KCA identification. The system employs various text pre-processing methods before transferring the acquired datasets to the data processing step. The pre-processed data is subsequently used during the data processing step. The statistical approaches, curve plotting, and curve fitting technique are applied in the KCA identification step. The proposed system is then tested and evaluated using benchmark datasets collected from various sources. To demonstrate our proposed approach’s effectiveness, merits, and significance, we compared it with other proposed techniques. The experimental results on eleven (11) datasets show that the proposed approach effectively recognizes the KCA from articles as well as significantly enhances the current keyphrase extraction methods based on various text sizes, languages, and domains

Hypermethylation of CpG islands and shores around specific microRNAs and mirtrons is associated with the phenotype and presence of bladder cancer

PURPOSE To analyze the role and translational potential for hypermethylation of CpG islands and shores in the regulation of small RNAs within urothelial cell carcinoma (UCC). To examine microRNAs (miR) and mirtrons, a new class of RNA located within gene introns and processed in a Drosha-independent manner. EXPERIMENTAL DESIGN The methylation status of 865 small RNAs was evaluated in normal and malignant cell lines by using 5-azacytidine and microarrays. Bisulfite sequencing was used for CpG regions around selected RNAs. Prognostic and diagnostic associations for epigenetically regulated RNAs were examined by using material from 359 patients, including 216 tumors and 121 urinary samples (68 cases and 53 controls). Functional analyses examined the effect of silencing susceptible RNAs in normal urothelial cells. RESULTS Exonic/UTR-located miRs and mirtons are most susceptible to epigenetic regulation. We identified 4 mirtrons and 16 miRs with CpG hypermethylation across 35 regions in normal and malignant urothelium. For several miRs, hypermethylation was more frequent and dense in CpG shores than islands (e.g., miRs-9/149/210/212/328/503/1224/1227/1229), and was associated with tumor grade, stage, and prognosis (e.g., miR-1224 multivariate analysis OR = 2.5; 95% CI, 1.3-5.0; P = 0.006). The urinary expression of epigenetically silenced RNAs (miRs-152/328/1224) was associated with the presence of UCC (concordance index, 0.86; 95% CI, 0.80-0.93; ANOVA P < 0.016). CONCLUSIONS Hypermethylation of mirtrons and miRs is common in UCC. Mirtrons appear particularly susceptible to epigenetic regulation. Aberrant hypermethylation of small RNAs is associated with the presence and behavior of UCC, suggesting potential roles as diagnostic and prognostic biomarkers

In-silico design of curcumin analogs as potential inhibitors of dengue virus NS2B/NS3 protease

Curcumin can interact with a variety of molecules implicated in a wide range of disorders. It can also hinder dengue virus’s (DENV’s) ability to infect cells. This work used computational analysis to identify and forecast the most potent curcumin analogs against the DENV NS2B/NS3 protease. In this study, curcumin-like compounds were screened using a rational in-silico study, with the least similarity score, docking analysis, and then additional screening for suitable pharmacokinetic properties. According to the findings, DB11672 has been identified as the primary inhibitor of DENV NS2B/NS3 protease. It is recommended that additional research be done on this antiviral property of the lead compound as a part of the process of finding and developing a new drug against DENV

A geofencing-based recent trends identification from twitter data

For facilitating users from information overloading by finding recent trends in twitter, several techniques are proposed. However, most of these techniques need to process extensive data. Therefore, in this paper, a geofencing-based recent trends identification technique is proposed, which acquires data based on a geofence. Afterwards, they are cleaned and the weight of these tweet data is calculated. For that, the frequency of tweet texts and hashtags are taken into account along with a boosting factor. Thereafter, they are ranked to recommend recent trends to the user. This proposed technique is applied in developing a system using Java and python. It is compared with other relevant systems, where it demonstrates that the performance of the proposed system is comparable. Over and above, since the proposed system integrates geofencing feature, it is more preferable over other systems

Pharmacophore-based molecular docking of usnic acid derivatives to discover anti-viral drugs against influenza A virus

For decades, influenza virus infection has been a serious health concern due to seasonal epidemics and pandemics, and it is continuing on the rise today, yet there is no gold-standard medication available for treating influenza viral infection. As a result, better influenza medicine is necessary to prevent illness. The purpose of this work was to investigate how effective usnic acid derivatives were as antiviral medications against the influenza virus in a computational approach. To discover the prospective medication as an anti-influenza agent, we employed pharmacophore-based molecular docking, ADMET, and drug-likeness studies, CYP isoform analysis and MD simulation approaches. Using pharmacophore filtering processes, twenty-three (23) usnic acid derivatives were acquired from an in-house database of 340 usnic acid derivatives. A docking simulation on the Influenza A H1N1 polymerase resulted in four molecules with a high affinity for the protein. The pharmacokinetics and drug-likeness predictions yielded two hit compounds, which were then subjected to cytochrome P450 enzyme screening to provide the lead molecule, denoted as compound-4. In addition, MD simulation of lead compound (Compound-4) was performed to verify the stability of the docked complex and the binding posture acquired in docking experiments. The findings revealed that compound-4 is a promising option for antiviral treatment of influenza illness in the future

Pharmacophore-based molecular docking and in-silico study of novel usnic acid derivatives as avian influenza A (H7N9) inhibitor

The Avian Influenza virus is not only dangerous to birds, but it is also dangerous to people and other animals. It is a serious danger to poultry worldwide with the capacity to spread to other species, including people; consequently, more efficient medicines are required to treat this virus. This study examined the binding effectiveness of twentyone (21) Usnic acid derivatives out of 340 generated via pharmacophore filtering with AIV A (H7N9) utilising an in-silico technique. The docking simulation to AIV A obtained five compounds with a high affinity to the target protein. The ADMET and druggability prediction produced two lead molecules that were then submitted to Cytochrome (CYP) P450 enzyme screening to generate the best molecule, labelled as compound 5. According to the findings, compound 5 might be employed as a lead inhibitor in developing an anti-AIV medicatio

Identification and Diagnostic Performance of a Small RNA within the PCA3 and BMCC1 Gene Locus That Potentially Targets mRNA

Background: PCA3 is a long noncoding RNA (lncRNA) with unknown function, upregulated in prostate cancer. LncRNAs may be processed into smaller active species. We hypothesized this for PCA3. Methods: We computed feasible RNA hairpins within the BMCC1 gene (encompassing PCA3) and searched a prostate transcriptome for these. We measured expression using qRT-PCR in three cohorts of prostate cancer tissues (n = 60), exfoliated urinary cells (n = 484 with cancer and n = 166 controls), and in cell lines (n = 22). We used in silico predictions and RNA knockup to identify potential mRNA targets of short transcribed RNAs. Results: We predicted 13 hairpins, of which PCA3-shRNA2 was most abundant within the prostate transcriptome. PCA3-shRNA2 is located within intron 1 of PCA3 and appears regulated by androgens. Expression of PCA3-shRNA2 was upregulated in malignant prostatic tissues, exfoliated urinary cells from men with prostate cancer (13–273 fold change; t test P < 0.003), and closely correlated to PCA3 expression (r = 0.84–0.93; P < 0.001). Urinary PCA3-shRNA2 (C-index, 0.75–0.81) and PCA3 (C-index, 0.78) could predict the presence of cancer in most men. PCA3-shRNA2 knockup altered the expression of predicted target mRNAs, including COPS2, SOX11, WDR48, TEAD1, and Noggin. PCA3-shRNA2 expression was negatively correlated with COPS2 in patient samples (r = −0.32; P < 0.001). Conclusion: We identified a short RNA within PCA3, whose expression is correlated to PCA3, which may target mRNAs implicated in prostate biology

In silico evaluation of usnic acid derivatives to discover potential antibacterial drugs against DNA gyrase B and DNA topoisomerase IV

Due to the rising increase in infectious diseases brought on by bacteria and anti-bacterial drug resistance, antibacterial therapy has become difficult. The majority of first-line antibiotics are no longer effective against numerous germs, posing a new hazard to global human health in the 21st century. Through the drug-likeness screening, 184 usnic acid derivatives were selected from an in-house database of 340 usnic acid compounds. The pharmacokinetics (ADMET) prediction produced fifteen hit compounds, of which the lead molecule was subsequently obtained through a molecular docking investigation. The lead compounds, labelled compound-277 and compound-276, respectively, with the substantial binding affinity towards the enzymes were obtained through further docking simulation on the DNA gyrase and DNA topoisomerase proteins. Additionally, molecular dynamic (MD) simulation was performed for 300 ns on the lead compounds in order to confirm the stability of the docked complexes and the binding pose discovered during docking tests. Due to their intriguing pharmacological characteristics, these substances may be promising therapeutic candidate for anti-bacterial medication

Identification of pyrazole derivatives of usnic acid as novel inhibitor of SARS-CoV-2 main protease through virtual screening approaches

The infection produced by the SARS-CoV-2 virus remains a significant health crisis worldwide. The lack of specific medications for COVID-19 necessitates a concerted effort to find the much-desired therapies for this condition. The main protease (Mpro) of SARS-CoV-2 is a promising target, vital for virus replication and transcription. In this study, fifty pyrazole derivatives were tested for their pharmacokinetics and drugability, resulting in eight hit compounds. Subsequent molecular docking simulations on SARS-CoV-2 main protease afforded two lead compounds with strong affinity at the active site. Additionally, the molecular dynamics (MD) simulations of lead compounds (17 and 39), along with binding free energy calculations, were accomplished to validate the stability of the docked complexes and the binding poses achieved in docking experiments. Based on these findings, compound 17 and 39, with their favorable projected pharmacokinetics and pharmacological characteristics, are the proposed potential antiviral candidates which require further investigation to be used as anti-SARS-CoV-2 medication