scholarly journals Multi-scale chromatin state annotation using a hierarchical hidden Markov model

2017 ◽  
Vol 8 (1) ◽  
Author(s):  
Eugenio Marco ◽  
Wouter Meuleman ◽  
Jialiang Huang ◽  
Kimberly Glass ◽  
Luca Pinello ◽  
...  
2018 ◽  
Author(s):  
Liangyu Tao ◽  
Siddhi Ozarkar ◽  
Jeff Beck ◽  
Vikas Bhandawat

AbstractMost behaviors such as making tea are not stereotypical but have an obvious structure. However, analytical methods to objectively extract structure from non-stereotyped behaviors are immature. In this study, we analyze the locomotion of fruit flies and show that this non-stereotyped behavior is well-described by a Hierarchical Hidden Markov Model (HHMM). HHMM shows that a fly’s locomotion can be decomposed into a small number of locomotor features, and odors modulate locomotion by altering the time a fly spends performing different locomotor features. Importantly, although all flies in our dataset use the same set of locomotor features, individual flies vary considerably in how often they employ a given locomotor feature, and how this usage is modulated by odor. This variation is so large that the behavior of individual flies is best understood as being grouped into at least 3-5 distinct clusters, rather than variations around an average fly.


Genomics ◽  
2013 ◽  
Vol 102 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Jessica L. Larson ◽  
Curtis Huttenhower ◽  
John Quackenbush ◽  
Guo-Cheng Yuan

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