ABSTRACT
The bioinformatics discipline seeks to solve problems in biology with computational theories and methods. Formal concept analysis (FCA) is one such theoretical model, based on partial orders. FCA allows the user to examine the structural properties of data based on which subsets of the dataset depend on each other. This article surveys the current literature related to the use of FCA for bioinformatics. The survey begins with a discussion of FCA, its hierarchical advantages, several advanced models of FCA, and lattice management strategies. It then examines how FCA has been used in bioinformatics applications, followed by future prospects of FCA in those areas. The applications addressed include gene data analysis (with next-generation sequencing), biomarkers discovery, protein-protein interaction, disease analysis (including COVID-19, cancer, and others), drug design and development, healthcare informatics, biomedical ontologies, and phylogeny. Some of the most promising prospects of FCA are identifying influential nodes in a network representing protein-protein interactions, determining critical concepts to discover biomarkers, integrating machine learning and deep learning for cancer classification, and pattern matching for next-generation sequencing.
ABSTRACT
In medical domain, the accuracy of the data supplied is critical. Missing values, on the other hand, are a typical occurrence in this sector for a variety of reasons. Most current science concentrates on establishing novel data imputation procedures, but more research on conducting a comprehensive review of existing algorithms is highly desired. Authors have evaluated the performance of four mostly adopted data imputation techniques, i.e., MICE, EM, mean, and KNN on a real-world dataset of COVID-19. KNN is an imputation approach that, according to the findings of the studies, is expected to be a good fit for dealing with missing data in the healthcare industry. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Background: Corona virus disease 2019 is a highly infectious disease which is caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2. SARS-CoV-2 is transmitted from person to person mainly by respiratory droplets and aerosols as well as by direct or indirect contact. Aims and objective: To compare different RNA extraction methods for detection of SARSCov-2 RNA from nasopharyngeal and oropharyngeal swabs using three different methods which are based on different techniques. Material and methods: This analytical observational study was conducted in the department of Microbiology, Sawai Man Singh Medical College Jaipur, Rajasthan from December 2020 to January 2021. We selected 200 confirmed positive (extracted by Easy Mag automated system) (remnant) samples showing a wide range of different Ct values and 20 confirmed negative samples stored in Viral Transport Media VTM for this study. In order to compare quality of three extractions methods, all samples were aliquoted separately for each extraction technique. (1) Extraction by manual method (spin column base): was done by as per manufacturer’s instructions. (2) Extraction by QIA cube HT (vaccum column base): was done by as per manufacturer’s instructions. (3) Extraction by Perkins Elmer chemagic 360: (magnetic beads based). Result: A panel consisting of 200 Covid-19 positive and 20 Covid-19 negative samples were extracted by three methods (i.e. Manual column based, automated column-based and automated magnetic beads-based method). The extracted material/elutes were put for realtime RT-PCR assay for the detection of SARS CoV-2 RNA. There was no major difference seen in individual samples’ ct values between three extraction system. CONCLUSION: In conclusion, we recommended all three RNA extraction methods (i.e. magnetic beads & silica column-based) are interchangeable in a diagnostic workflow for the SARS CoV-2 by RTPCR and can be taken into account for SARS CoV-2 detection in possible future shortage of one kit or times of crisis in such pandemic time.
ABSTRACT
As the trend towards E-teaching learning strengthens,questions remain unanswered with the effectiveness of online learning in comparison to traditional classroom learning. The aim of this paper is to analyze the impact of online and offline (conventional method) teaching method on student's learning achievement. This article outlines a case study of E-teaching vs traditional teaching learning of a Mathematics course to first year engineering students within their respective programmes. The results of study are analyzed using bar/pie charts. Comparison between online teaching and traditional teaching has been done for various parameters related to students’ interest for online/offline teaching, interaction with students, students' comfort level, instructor’s competency etc. related to teaching and learning. The findings generate insights which help to understand the benefits and shortcomings of online/offline teaching consequently helping in improved design of such courses. © 2021, Rajarambapu Institute Of Technology. All rights reserved.
ABSTRACT
Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), the causative pathogen for the COVID-19, first emerged in Wuhan, China, in December 2019 and by March 2020, it was declared a pandemic. COVID-19 pandemic has overburdened healthcare systems in most countries and has led to massive economic losses. SARS-CoV-2 transmission typically occurs by respiratory droplets. The average incubation period is 6.4 days and presenting symptoms typically include fever, cough, dyspnea, myalgia or fatigue. While the majority of patients tend to have a mild illness, a minority of patients develop severe hypoxia requiring hospitalization and mechanical ventilation. Management is mostly supportive. However, several direct anti-viral agents, and immunomodulatory therapy with steroids and various cytokine blockers seem promising in early results. However, an effective vaccine has been established, which will help curb the pandemic.