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DTSTAMP:20230124T171520Z
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DTSTART;TZID=America/Chicago:20221117T083000
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UID:submissions.supercomputing.org_SC22_sess226_spostu104@linklings.com
SUMMARY:Multi-Objective Evolutionary Clustering of Single-Cell RNA Sequenc
 ing Data
DESCRIPTION:ACM Student Research Competition: Graduate Poster, ACM Student
  Research Competition: Undergraduate Poster, Posters\n\nMulti-Objective Ev
 olutionary Clustering of Single-Cell RNA Sequencing Data\n\nZhao\n\nCells 
 are the basic building blocks of human organisms. Single-cell RNA sequenci
 ng is a technology for studying the heterogeneity of cells of different or
 gans, tissues, subjects, conditions, and treatments. Identification of cel
 l types and states in sequenced data is an important and challenging task,
  requiring computational approaches that are accurate, robust, and scalabl
 e. Existing approaches use cluster analysis as the first step of cell-type
 s prediction. Their performance remains limited because they optimize only
  one objective function. In this study, two evolutionary clustering approa
 ches were designed, implemented, and systematically validated, namely a si
 ngle-objective evolutionary algorithm and a multi-objective evolutionary a
 lgorithm. The algorithms were evaluated on synthetic and real datasets. Th
 e results demonstrated that the performance and the accuracy of both evolu
 tionary algorithms were consistent, stable, and on par with or better than
  baseline algorithms. Running time analysis of multi-processing on an HPC 
 showed that evolutionary algorithms can efficiently handle large datasets.
 \n\nRegistration Category: Tech Program Reg Pass
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