Related Experiment Video
Updated: Aug 8, 2026
11:22
Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
Published on: January 30, 2018
Metrics and similarity measures for hidden Markov models
R B Lyngsø1, C N Pedersen, H Nielsen
1Department of Computer Science, University of Aarhus, Denmark. rlyngsoe@daimi.au.dk
Proceedings. International Conference on Intelligent Systems for Molecular Biology
|April 29, 2000
Summary
Hidden Markov models (HMMs) are now vital in computational biology for sequence analysis. This study introduces new HMM comparison measures and an efficient algorithm for profile HMMs, aiding biological sequence classification.
Area of Science:
- Computational Biology
- Bioinformatics
- Machine Learning
Background:
- Hidden Markov models (HMMs) originated in speech recognition.
- HMMs have gained prominence in computational biology for tasks like gene finding and phylogenetic analysis.
- Comparing different HMMs is crucial for accurate biological sequence characterization.
Purpose of the Study:
- To propose novel quantitative measures for comparing Hidden Markov Models.
- To develop an efficient algorithm for computing these measures, particularly for profile HMMs.
- To demonstrate the utility of these measures in a biological context.
Main Methods:
- Development of new distance/similarity measures between HMMs.
- Design of an efficient algorithm for computing measures on left-right HMMs (e.g., profile HMMs).
- Experimental comparison of HMMs for signal peptide classification using the proposed measures.
Main Results:
- The proposed measures provide a quantitative way to assess differences between HMMs.
- An efficient algorithm was developed and tested for profile HMMs.
- The measures successfully differentiated HMMs representing different classes of signal peptides.
Conclusions:
- The new measures and algorithm enhance the application of HMMs in computational biology.
- This work facilitates more accurate sequence family characterization and classification.
- The approach is applicable to various HMM-based biological analyses.
Related Concept Videos
02:16Measures of Central Tendency
The "center" of a data set is also a way of describing location. The two most widely used measures of the "center" of the data are the mean (average) and the median. The words "mean" and "average" are often used interchangeably. The substitution of one word for the other is common practice. The technical term is "arithmetic mean" and "average" is technically a center location. However, in practice among non-statisticians, "average" is commonly accepted for "arithmetic mean."
01:19Expected Frequencies in Goodness-of-Fit Tests
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
01:13Multiple Comparison Tests
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
01:11Statistical Analysis: Overview
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
01:11Variability: Analysis
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
The range is a simple measure of variability, indicating the difference between the highest and...
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...