Identification of Rare Cells Using Hybridized Stem Cells Algorithm

Abstract

Identification of Rare Cells Using Hybridized Stem Cells Algorithm Abstract: Identification of rare cells is a challenging issue in the field of bioinformatics. As an initial phase, DNA, RNA or protein sequences need to be aligned to identify the similarity in sequences. Even though many algorithms and standard tools exist to solve the problem of multiple sequence alignment, still there are research gaps which need to be sorted out to prevail over the constraints. Accurately aligned sequences allow the biologists to explore similarity in function which leads to the discovery of new evolutionary information. Genetic algorithm poses few limitations in solving the problem of multiple sequence alignment such that it may get trapped in local optima and it requires more number of iteration to reach the convergence point. It leads to inaccurate alignment of sequences which may be the chance to infer wrong information from the sequences. When RaceID/StemID algorithms were used to cluster and identify the rare cells, clustering accuracy was less and within cluster dispersion was high. These disadvantages were rectified with the help of the proposed Hybridized Stem Cells algorithm. Hybridized Stem Cells Algorithm acts as enhancer in solving multiple sequence alignment followed by clustering and identification of rare cells from the benchmark data sets. newline