An Introduction to Infinite HMMs for Single-Molecule Data Analysis

Ioannis Sgouralis, Steve Presse

Research output: Contribution to journalReview article

13 Citations (Scopus)

Abstract

The hidden Markov model (HMM) has been a workhorse of single-molecule data analysis and is now commonly used as a stand-alone tool in time series analysis or in conjunction with other analysis methods such as tracking. Here, we provide a conceptual introduction to an important generalization of the HMM, which is poised to have a deep impact across the field of biophysics: the infinite HMM (iHMM). As a modeling tool, iHMMs can analyze sequential data without a priori setting a specific number of states as required for the traditional (finite) HMM. Although the current literature on the iHMM is primarily intended for audiences in statistics, the idea is powerful and the iHMM's breadth in applicability outside machine learning and data science warrants a careful exposition. Here, we explain the key ideas underlying the iHMM, with a special emphasis on implementation, and provide a description of a code we are making freely available. In a companion article, we provide an important extension of the iHMM to accommodate complications such as drift.

Original languageEnglish (US)
Pages (from-to)2021-2029
Number of pages9
JournalBiophysical Journal
Volume112
Issue number10
DOIs
StatePublished - May 23 2017

Fingerprint

Biophysics
Machine Learning

ASJC Scopus subject areas

  • Biophysics

Cite this

An Introduction to Infinite HMMs for Single-Molecule Data Analysis. / Sgouralis, Ioannis; Presse, Steve.

In: Biophysical Journal, Vol. 112, No. 10, 23.05.2017, p. 2021-2029.

Research output: Contribution to journalReview article

Sgouralis, Ioannis ; Presse, Steve. / An Introduction to Infinite HMMs for Single-Molecule Data Analysis. In: Biophysical Journal. 2017 ; Vol. 112, No. 10. pp. 2021-2029.
@article{7912abf2ad4b49e2bcb21a97625e7574,
title = "An Introduction to Infinite HMMs for Single-Molecule Data Analysis",
abstract = "The hidden Markov model (HMM) has been a workhorse of single-molecule data analysis and is now commonly used as a stand-alone tool in time series analysis or in conjunction with other analysis methods such as tracking. Here, we provide a conceptual introduction to an important generalization of the HMM, which is poised to have a deep impact across the field of biophysics: the infinite HMM (iHMM). As a modeling tool, iHMMs can analyze sequential data without a priori setting a specific number of states as required for the traditional (finite) HMM. Although the current literature on the iHMM is primarily intended for audiences in statistics, the idea is powerful and the iHMM's breadth in applicability outside machine learning and data science warrants a careful exposition. Here, we explain the key ideas underlying the iHMM, with a special emphasis on implementation, and provide a description of a code we are making freely available. In a companion article, we provide an important extension of the iHMM to accommodate complications such as drift.",
author = "Ioannis Sgouralis and Steve Presse",
year = "2017",
month = "5",
day = "23",
doi = "10.1016/j.bpj.2017.04.027",
language = "English (US)",
volume = "112",
pages = "2021--2029",
journal = "Biophysical Journal",
issn = "0006-3495",
publisher = "Biophysical Society",
number = "10",

}

TY - JOUR

T1 - An Introduction to Infinite HMMs for Single-Molecule Data Analysis

AU - Sgouralis, Ioannis

AU - Presse, Steve

PY - 2017/5/23

Y1 - 2017/5/23

N2 - The hidden Markov model (HMM) has been a workhorse of single-molecule data analysis and is now commonly used as a stand-alone tool in time series analysis or in conjunction with other analysis methods such as tracking. Here, we provide a conceptual introduction to an important generalization of the HMM, which is poised to have a deep impact across the field of biophysics: the infinite HMM (iHMM). As a modeling tool, iHMMs can analyze sequential data without a priori setting a specific number of states as required for the traditional (finite) HMM. Although the current literature on the iHMM is primarily intended for audiences in statistics, the idea is powerful and the iHMM's breadth in applicability outside machine learning and data science warrants a careful exposition. Here, we explain the key ideas underlying the iHMM, with a special emphasis on implementation, and provide a description of a code we are making freely available. In a companion article, we provide an important extension of the iHMM to accommodate complications such as drift.

AB - The hidden Markov model (HMM) has been a workhorse of single-molecule data analysis and is now commonly used as a stand-alone tool in time series analysis or in conjunction with other analysis methods such as tracking. Here, we provide a conceptual introduction to an important generalization of the HMM, which is poised to have a deep impact across the field of biophysics: the infinite HMM (iHMM). As a modeling tool, iHMMs can analyze sequential data without a priori setting a specific number of states as required for the traditional (finite) HMM. Although the current literature on the iHMM is primarily intended for audiences in statistics, the idea is powerful and the iHMM's breadth in applicability outside machine learning and data science warrants a careful exposition. Here, we explain the key ideas underlying the iHMM, with a special emphasis on implementation, and provide a description of a code we are making freely available. In a companion article, we provide an important extension of the iHMM to accommodate complications such as drift.

UR - http://www.scopus.com/inward/record.url?scp=85019889999&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85019889999&partnerID=8YFLogxK

U2 - 10.1016/j.bpj.2017.04.027

DO - 10.1016/j.bpj.2017.04.027

M3 - Review article

C2 - 28538142

AN - SCOPUS:85019889999

VL - 112

SP - 2021

EP - 2029

JO - Biophysical Journal

JF - Biophysical Journal

SN - 0006-3495

IS - 10

ER -