Unsupervised in-silico modeling of complex biological systems

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The advent of high-Throughput technologies and the resultant generation of data has increased the demand for datadriven analytics. However, a comprehensive and computationally efficient method for analyzing, understanding and managing the emergent behavior of complex biological systems using timeseries data remains elusive. In this paper, we introduce a new computational framework and modeling formalism designed for unsupervised learning and model construction in highthroughput biological data applications. This framework uses an underlying Bayesian nonparametric model which effectively infers long-range temporal dependencies from heterogeneous data streams to produce grammatical rules used for real-Time insilico modeling, behavior recognition and prediction. We present initial results of unsupervised learning tasks using unlabeled livecell imaging data from experiments performed on the Large Scale Digital Cell Analysis System (LSDCAS), namely cellular event classification and large-scale spatio-Temporal behavior recognition.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE 1st International Workshops on Foundations and Applications of Self-Systems, FAS-W 2016
EditorsPeter R. Lewis, Christian Muller-Schloer, Sameh Elnikety
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages287-292
Number of pages6
ISBN (Electronic)9781509036516
DOIs
StatePublished - Dec 16 2016
Event1st International Workshops on Foundations and Applications of Self-Systems, FAS-W 2016 - Augsburg, Germany
Duration: Sep 12 2016Sep 16 2016

Publication series

NameProceedings - IEEE 1st International Workshops on Foundations and Applications of Self-Systems, FAS-W 2016

Conference

Conference1st International Workshops on Foundations and Applications of Self-Systems, FAS-W 2016
CountryGermany
CityAugsburg
Period9/12/169/16/16

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering
  • Control and Optimization

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