QoS parameters for Comparison and Performance Evaluation of Reactive protocols

Latchoumi, T. P.; Vasanth, A. Vijay; Bhavya, Battula; Viswanadapalli, Anusha; Jayanthiladevi, A.

doi:10.1109/cispsse49931.2020.9212285

Cited by 8 publications

(5 citation statements)

References 2 publications

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“…If one of the prototypes has a high Z-score resemblance to such inquiry, the query's design is predicted, or a framework precise definition is built between predicted architecture and the design's RNA [8].To use an understanding integral equation theprototype, detailed structure could then be used to estimate the propensity for protein-RNA binding. An RBP is forecast if the structural similarity was greater than a criterion [9].PSI-BLAST, which employs a sequence-toprofile suitability heuristic algorithm, outperforms SPOT-Seek in terms of sensitivity and reliability [10][11][12]. More crucially, while implemented to 250 DNA binding proteins, SPOTSeq (RNAs) was able to distinguish between DNAbinding or RNA association (less false positive rate) unlike several mathematical techniques [13][14][15].…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach to Predict the RNA Bind Protein

Rao,

Sajith,

Gujjula

2023

Recent Trends in Data Science and Its Applications

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Since RNAs were numerous, prevalent through biological distribution or purpose, but adaptable overall architecture, discovering protein RNA associations were difficult either analytically or statistically. As a consequence, a large number of RNA-binding enzymes (RBPs) have yet to be discovered. A findings were similar or by detecting 860 mRNAbinding proteins in such a later chromatographies research, or by using SPOT-Seq of RBPs, a template-based achievement predicting tool, to the gene regulation (Mbps). According to the Nucleotide Sequences, predicted penetration (or sensitivity) of 1217 recognized RBPs or 43.6 percent of 860 recently discovered human Mbps is 42.6 % or 43.6 %, respectively. SPOT-robust Seq's capacity to foresee RBPs was demonstrated by its continued sensitivity. More importantly, SPOT-Seq discovers 2418 new RBPs across associated proteins, including 291 of these verified by the recently discovered mRBP set.There are 61 unique RBPs that are not comparable to any recognized RBPs among 291 authenticated novels RBPs. We may now investigate the phenotypic roles of anticipated novel RBPs in sickness cascades after they have been validated. For actual verification or hypothesis development, the information of 2418 anticipated novels RBPs, together with confidence level, but also complex mechanisms, was accessible at http://sparks-lab.org (with articles).

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Section: Related Workmentioning

confidence: 99%

A Novel Approach to Predict the RNA Bind Protein

Rao,

Sajith,

Gujjula

2023

Recent Trends in Data Science and Its Applications

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“…Earlier gene function prediction approaches simply took this annotation data & turned it into a simple binary classification task. As a result of ignoring the relationships b/w GO terms as well as the uneven features of terms, these techniques were inaccurate [9]. Some researchers characterize gene function prediction as just a multiple-label / prediction of structural output problem [10] because a gene is frequently labeled with a group of structurally ordered GO terms at the same time.…”

Section: Introductionmentioning

confidence: 99%

Identify The Gene Function Using The Multisource Association of Genes By Integration of Clusters Bayesian Network Model

Lalitha,

Ashraf,

Bhuvaneswari

et al. 2023

Recent Trends in Data Science and Its Applications

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Genomic sequencing is no longer new, the annotation of gene function continues to be a major challenge in biological evolution.From traditional approaches such as affinity precipitation to contemporary broadband approaches such as gene expression microarrays, there are several methods for testing functional genomics.Professional information concerning relative levels of accuracy of data sources is explicitly incorporated into the system for mixing in the normative framework.Multisource Association of Genes by Integration of Clusters (MAGIC) has a level of confidence that enables users to change the rigour of forecasts through Bayesian Model. Researchers used MAGIC to analyze Saccharomyces cerevisiae genetic & physical interactions, micro-array, as well as transcription factor binding site information, but the researchers used Gene Ontology annotations from the Saccharomyces Genome Database to determine a biological value for gene clusters.Compared to microarray analysis, MAGIC simply improves the reliability of functional clustering by building functional clusterings based on a variety of data types.

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“…Those approaches were predicated on the fact that semantic relatedness produced from gene GO classifications correlates with familiarity estimated from a variety of biological information, including such sequencing, gene expression patterns, as well as posttranslational modifications [8]. These primarily aim to fill up gaps in GO classifications imperfectly identified genes [9].The structures of GO descriptors could be designed to estimate gene function using a tree structure or perhaps a Bays classifier coupled to the data. However, to effectively estimate the likelihood function between GO words, such a template method requires enough annotations [10].…”

Section: Introductionmentioning

confidence: 99%

The Algorithms in Disarray Hashing Technique That Preserves Gene Ontology Hierarchy Application of the Phish Methodology

Mohite,

Madhuri,

Banik

et al. 2023

Recent Trends in Data Science and Its Applications

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Gene Ontology (GO) is a systematic taxonomy that employs organize languages to explain the biochemical activities, functional activities, including tissue placements underlying transcription factors. Gene Ontology (GO) markers link proteins to GO concepts as well as show which genetic variants perform the physiological activity specified by the words. Forecasting proper GO descriptors for individuals from a large number of GO words specified by GO, on the other hand, would be a challenging task.To address these issues, researchers present a conceptual approach for functional genomics predictions involved in Gene Ontology Hierarchical Preserving Hashing (Phish). Phish begins by comparing the taxonomy homology of GO keywords. It all then optimizes a succession of hashed algorithms to store huge GO concepts via a small binary format, using a hierarchal structure scrambling method to maintain the hierarchical system between GO words. Following that, Phish uses those scrambling techniques to transfer the gene-term connection matrices into a cheaper region, where it conducts lexical resemblance gene function estimation. Phish outperforms existing similar methods as well as being resistant to the number of encryption schemes, according to experimental observations on 3 model organisms for intercultural gene function forecasting. In particular, researchers use Phish as a BLAST-based gene function forecast component. According to the findings of the experiments, Phish greatly enhances forecasting accuracy.

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QoS parameters for Comparison and Performance Evaluation of Reactive protocols

Cited by 8 publications

References 2 publications

A Novel Approach to Predict the RNA Bind Protein

A Novel Approach to Predict the RNA Bind Protein

Identify The Gene Function Using The Multisource Association of Genes By Integration of Clusters Bayesian Network Model

The Algorithms in Disarray Hashing Technique That Preserves Gene Ontology Hierarchy Application of the Phish Methodology

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