scholarly journals EM and component-wise boosting for Hidden Markov Models: a machine-learning approach to capture-recapture

2016 ◽  
Author(s):  
Robert W. Rankin
Keyword(s):  
Machine Learning ◽  
Model Selection ◽  
Markov Models ◽  
Hidden Markov ◽  
Individual Heterogeneity ◽  
Learning Approach ◽  
Unified Framework ◽  
Machine Learning Approach ◽  
Capture Recapture ◽  
Linear Effects

AbstractThis study introduces statistical boosting for capture-mark-recapture (CMR) models. It is a shrinkage estimator that constrains the complexity of a CMR model in order to promote automatic variable-selection and avoid over-fitting. I discuss the philosophical similarities between boosting and AIC model-selection, and show through simulations that a boosted Cormack-Jolly-Seber model often out-performs AICc methods, in terms of estimating survival and abundance, yet yields qualitatively similar estimates. This new boosted CMR framework is highly extensible and could provide a rich, unified framework for addressing many topics in CMR, such as non-linear effects (splines and CART-like trees), individual-heterogeneity, and spatial components.

scholarly journals HMMRATAC: a Hidden Markov ModeleR for ATAC-seq

2019 ◽  
Vol 47 (16) ◽  
pp. e91-e91 ◽  
Author(s):  
Evan D Tarbell ◽  
Tao Liu
Keyword(s):  
Machine Learning ◽  
Hidden Markov ◽  
Current Data ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Analysis Tool ◽  
Peak Calling ◽  
Entire Genome ◽  
Machine Learning Approach ◽  
Accessible Chromatin

Abstract ATAC-seq has been widely adopted to identify accessible chromatin regions across the genome. However, current data analysis still utilizes approaches initially designed for ChIP-seq or DNase-seq, without considering the transposase digested DNA fragments that contain additional nucleosome positioning information. We present the first dedicated ATAC-seq analysis tool, a semi-supervised machine learning approach named HMMRATAC. HMMRATAC splits a single ATAC-seq dataset into nucleosome-free and nucleosome-enriched signals, learns the unique chromatin structure around accessible regions, and then predicts accessible regions across the entire genome. We show that HMMRATAC outperforms the popular peak-calling algorithms on published human ATAC-seq datasets. We find that single-end sequenced or size-selected ATAC-seq datasets result in a loss of sensitivity compared to paired-end datasets without size-selection.

A Genetic Algorithm-Based, Hybrid Machine Learning Approach to Model Selection

2006 ◽  
Vol 33 (2) ◽  
pp. 195-221 ◽  
Author(s):  
Robert R. Bies ◽  
Matthew F. Muldoon ◽  
Bruce G. Pollock ◽  
Steven Manuck ◽  
Gwenn Smith ◽  
...  
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Model Selection ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Hybrid Machine

scholarly journals HMMRATAC: a Hidden Markov ModeleR for ATAC-seq

2018 ◽  
Author(s):  
Evan D. Tarbell ◽  
Tao Liu
Keyword(s):  
Machine Learning ◽  
Hidden Markov ◽  
Current Data ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Analysis Tool ◽  
Peak Calling ◽  
Entire Genome ◽  
Machine Learning Approach ◽  
Accessible Chromatin

ABSTRACTATAC-seq has been widely adopted to identify accessible chromatin regions across the genome. However, current data analysis still utilizes approaches initially designed for ChIP-seq or DNase-seq, without considering the transposase digested DNA fragments that contain additional nucleosome positioning information. We present the first dedicated ATAC-seq analysis tool, a semi-supervised machine learning approach named HMMRATAC. HMMRATAC splits a single ATAC-seq dataset into nucleosome-free and nucleosome-enriched signals, learns the unique chromatin structure around accessible regions, and then predicts accessible regions across the entire genome. We show that HMMRATAC outperforms the popular peak-calling algorithms on published human ATAC-seq datasets. We find that single-end sequenced or size-selected ATAC-seq datasets result in a loss of sensitivity compared to paired-end datasets without size-selection.

1552-P: Machine Learning Approach to Decision-Making for Initial Insulin Use in Japanese Patients with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1552-P
Author(s):  
KAZUYA FUJIHARA ◽  
MAYUKO H. YAMADA ◽  
YASUHIRO MATSUBAYASHI ◽  
MASAHIKO YAMAMOTO ◽  
TOSHIHIRO IIZUKA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Decision Making ◽  
Japanese Patients ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Insulin Use
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