TY - GEN
T1 - Unsupervised in-silico modeling of complex biological systems
AU - Kalantari, John
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/12/16
Y1 - 2016/12/16
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85011067188&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85011067188&partnerID=8YFLogxK
U2 - 10.1109/FAS-W.2016.69
DO - 10.1109/FAS-W.2016.69
M3 - Conference contribution
AN - SCOPUS:85011067188
T3 - Proceedings - IEEE 1st International Workshops on Foundations and Applications of Self-Systems, FAS-W 2016
SP - 287
EP - 292
BT - Proceedings - IEEE 1st International Workshops on Foundations and Applications of Self-Systems, FAS-W 2016
A2 - Lewis, Peter R.
A2 - Muller-Schloer, Christian
A2 - Elnikety, Sameh
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st International Workshops on Foundations and Applications of Self-Systems, FAS-W 2016
Y2 - 12 September 2016 through 16 September 2016
ER -