Hidden Markov models

doi:10.1016/S0959-440X(96)80056-X

Current Opinion in Structural Biology

Volume 6, Issue 3, June 1996, Pages 361-365

https://doi.org/10.1016/S0959-440X(96)80056-X Get rights and content

Abstract

‘Profiles’ of protein structures and sequence alignments can detect subtle homologies. Profile analysis has been put on firmer mathematical ground by the introduction of hidden Markov model (HMM) methods. During the past year, applications of these powerful new HMM-based profiles have begun to appear in the fields of protein-structure prediction and large-scale genome-sequence analysis.

References (33)

WR Taylor
Identification of protein sequence homology by consensus template alignment
J Mol Biol
(1986)
A Krogh et al.
Hidden Markov models in computational biology: applications to protein modeling
J Mol Biol
(1994)
JV White et al.
Protein classification by stochastic modeling and optimal filtering of amino-acid sequences
Math Biosci
(1994)
A Godzik et al.
Topology fingerprint approach to the inverse protein folding problem
J Mol Biol
(1992)
P Baldi et al.
Smooth on-line learning algorithms for hidden Markov models
Neural Computation
(1994)
SR Eddy et al.
RNA sequence analysis using covariance models
Nucleic Acids Res
(1994)
SF Altschul et al.
Issues in searching molecular sequence databases
Nat Genet
(1994)
GJ Barton
Protein multiple sequence alignment and flexible pattern matching
Methods Enzymol
(1990)
M Gribskov et al.
Profile analysis: detection of distantly related proteins
Proc Natl Acad Sci USA
(1987)
JU Bowie et al.
A method to identify protein sequences that fold into a known three-dimensional structure
Science
(1991)

R Luthy et al.

Assessment of protein models with three-dimensional profiles

Nature

(1992)

P Baldi et al.

Hidden Markov models of biological primary sequence information

Proc Natl Acad Sci USA

(1994)

SR Eddy et al.

Maximum discrimination hidden Markov models of sequence consensus

J Comput Biol

(1995)

GA Churchill

Stochastic models for heterogeneous DNA sequences

Bull Math Biol

(1989)

CM Stultz et al.

Structural analysis based on state-space modeling

Protein Sci

(1993)

LR Rabiner

A tutorial on hidden Markov models and selected applications in speech recognition

Proc IEEE

(1989)

Cited by (1014)

Morpheme-based Korean text cohesion analyzer
2024, SoftwareX
The fundamental difference between Korean and English text analysis lies in morpheme analysis. While existing Korean text analysis relies on English analysis tools, it often yields inaccurate results due to the difficulty of morpheme analysis. The primary reason is the existing morpheme analyzer depends on eojeol tokens, making it challenging to grasp Korean characteristics. Therefore, we introduce a Transformer-based morpheme analyzer that uses morpheme tokens to capture the inherent feature in Korean sentences. Then, we successfully integrate this morpheme analyzer into our Korean text analysis tool, offering it as a web service for efficient usage.
Adaptive undersampling and short clip-based two-stream CNN-LSTM model for surgical phase recognition on cholecystectomy videos
2024, Biomedical Signal Processing and Control
Surgical phase recognition is challenging due to overfitting problems caused by imbalanced data among surgical phases. We proposed an adaptive sampling rate-based undersampling method that could generate the number of each surgical phase data similarly to alleviate biased learning. To improve the performance of our method, we also introduced a two-stream CNN-LSTM model that could extract temporal information on behavioral changes between each image frame. First, we extracted a total of 40,236 short clips using an adaptive subsampling rate from the entire video. Each short clip was entered into a pre-trained GoogLeNet. The output with visual information was then immediately fed into a sequence-to-sequence LSTM model to extract temporal information of neighbor frames within a short clip. At the same time, another sequence-to-vector LSTM was used, to extract temporal information from all successive image frames to predict the final surgical phase. The proposed method was evaluated with a public dataset Cholec80. The proposed approach outperformed state-of-the-art methods, showing a high F1-score of 87.12% and an AUC of 98.00%. In addition, the F1-score deviation between all phases decreased by about 10% compared to that before applying undersampling. Experimental results confirmed that employing our proposed method could learn enrich temporal information from short clips. It outperformed the conventional one-stream CNN-LSTM architecture.
A hybrid method of combination probability and machine learning for Chinese geological text segmentation
2024, Computers and Geosciences
To address the issues surrounding incomplete coverage of core dictionaries, limited training corpora, and low precision in Chinese geological text segmentation, a knowledge- and data-driven word segmentation method by combining combination probability and machine learning was proposed in this paper. We extracted mathematical feature information from terms in Chinese geological text to construct a Term Combination Probability Model (TCPM) for Chinese word combinations by integrating the combination features of geological terms and the Chinese writing styles under zero-sample conditions. The TCPM was used to extract geological terms with high combination characteristics as a user-defined dictionary, and then a geological corpus was constructed by using a general domain word segmentation method based on this dictionary. After a small amount of manual review and optimization, the geological corpus was trained with a BiLSTM-CRF model to segment Chinese geological text. The proposed method in this paper was tested using a regional geological survey report set in Henan Province, and the precision, recall, and F1-score of the method are 92.65%, 92.53%, and 92.59%, respectively. The experimental results demonstrated that this method, combined with the inherent knowledge features of geological text and the machine learning method, can assist in expanding the core dictionary for Chinese geological text segmentation based on zero-sample, and can improve the segmentation precision of Chinese geological text compared to simple general word segmentation methods or machine learning methods.
A new dataset to study a century of innovation in Europe and in the US
2024, Research Policy
Innovation is an important driver of potential growth but quantitative evidence on the dynamics of innovative activities in the long-run are hardly documented due to the lack of data, especially in Europe. In this paper, we introduce PatentCity, a novel dataset on the location and nature of patentees from the 19th century using information derived from an automated extraction of relevant information from patent documents published by the German, French, British and US Intellectual Property offices. This dataset has been constructed with the view of facilitating the exploration of the geography of innovation and includes additional information on citizenship and occupation of inventors.
TrajectoFormer: Transformer-Based Trajectory Prediction of Autonomous Vehicles with Spatio-temporal Neighborhood Considerations
2024, International Journal of Computational Intelligence Systems
A Review of Privacy and Security of Edge Computing in Smart Healthcare Systems: Issues, Challenges, and Research Directions
2024, Tsinghua Science and Technology

View all citing articles on Scopus

View full text

Hidden Markov models

Abstract

J Mol Biol

J Mol Biol

Math Biosci

J Mol Biol

Neural Computation

Nucleic Acids Res

Issues in searching molecular sequence databases

Nat Genet

Protein multiple sequence alignment and flexible pattern matching

Methods Enzymol

Profile analysis: detection of distantly related proteins

Proc Natl Acad Sci USA

A method to identify protein sequences that fold into a known three-dimensional structure

Science

Assessment of protein models with three-dimensional profiles

Nature

Hidden Markov models of biological primary sequence information

Proc Natl Acad Sci USA

Maximum discrimination hidden Markov models of sequence consensus

J Comput Biol

Stochastic models for heterogeneous DNA sequences

Bull Math Biol

Structural analysis based on state-space modeling

Protein Sci

A tutorial on hidden Markov models and selected applications in speech recognition

Proc IEEE