No abstract

No abstract

Prediction of coding region from genomic DNA sequence is the foremost step in the quest of gene identification. In the eukaryotic organism, the gene structure consists of promoter, intron, start codon, exon and stop codon, etc. In the prediction of splice site, which is the separation between exons and introns, the accuracy is lower than 90% even when the sequences adjacent to the splice sites have a high conservation. Therefore, the algorithms used in the splice sites identification must be improved in order to recover the prediction accuracy. Hence, an efficient method, MM2F-SVM is proposed through this article, which consists of three stages-initial stage, in which a second order Markov Model (MM2) is used, i.e. feature extraction; intermediate, or the second stage in which principal feature analysis (PFA) is done, i.e. feature selection; and the final or the third stage, in which a support vector machine (SVM) with Gaussian kernel is used for final classification. While comparing this proposed MM2F-SVM model with the other existing splice site prediction programs, superior performance for the former has been noticed.

There was a substantial medicine shortage and an increase in morbidity due to the second wave of the COVID-19 pandemic in India. This pandemic has also had a drastic impact on healthcare professionals' psychological health as they were surrounded by suffering, death, and isolation. Healthcare practitioners in North India were sent a self-administered questionnaire based on the COVID-19 Stress Scale (N = 436) from March to May 2021. With 10-fold cross-validation, extreme gradient boosting (XGBoost) was used to predict the individual stress levels. XGBoost classifier was applied, and classification accuracy was 88%. The results of this research show that approximately 52.6% of healthcare specialists in the dataset exceed the severe psychiatric morbidity standards. Further, to determine which attribute had a significant impact on stress prediction, advanced techniques (SHAP values), and tree explainer were applied. The two most significant stress predictors were found to be medicine shortage and trouble in concentrating.

The primary step in search of the gene prediction is an identification of the coding region from genomic DNA sequence. Gene structure in the case of a eukaryotic organism is composed of promoter, intron, start codon, exons, stop codon, etc. Splice site prediction, which separates the junction between exon and intron, though the sequence beside.The splice sites have huge preservation, however, the precision of the tool exhibits less than 90%. The main objective of this work to exhibits a hybrid technique that efficiently improves the existing gene recognition technique. Therefore to enhance the identification of splice sites, the respective algorithm needs to be improved. Over the last decade, the researcher paid more attention to improve the accuracy of a predicted model in this domain. Our proposed method, 'SpliceCombo' involves three stages. At initial stage, which considers the principal Component Analysis, based on the feature extracted. In the intermediate stage, i.e.,, the second stage Case-Based Reasoning is done, i.e., feature selection. The third stage uses support vector machine based along with polynomial kernel function for final classification. In comparison with other methods, the proposed SpliceCombo model outperforms other prediction models with respect to prediction accuracies. Particularly for donor splice site the methodology exhibits sensitivity is 97.25% accurate and specificity is 97.46% accurate. For acceptor Splice Site the sensitivity is 96.51% and Specificity is 94.48% correct.