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At least 19 recordsLinked to original sources
PubMed1978

Quantitative nuclear image analysis: differentiation between normal, hyperplastic, and malignant appearing uterine glands in a paraffin section. IV. The use of Markov chain texture features in discriminant analysis.

Discriminant analysis was applied to Markov chain texture features and elementary features calculated from data from microscope photometry of nuclei in a paraffin section. The results from measurements on the nuclei of morphologically normal, atypical hyperplastic and carcinomatous uterine glands were used in discriminant analysis. With this technique it is possible to classify up to 88.1% of the nuclei correctly in one of the three groups of uterine glands. The discriminating power of several smaller subsets indicated that with more than 28 features there is hardly any increase in discriminating power. Discriminant analysis with a selection from elementary and Markov chain features provides objective criteria of assistance in histopathological diagnosis.

Cell Nucleus↗
PubMed1977

Polydisperse particulate solids mixing and segregation: nonstationary Markov chains.

The feasibility of analyzing interparticulate translocations within agitated beds of polydisperse particulate solids in terms of Markov chains was investigated. A binary mixture of spherical particles subjected to vertical sine wave vibration is shown to behave in accordance with a nonstationary. Markov chain having singly stochastic transition matrixes. The transition probability elements of such matrixes, calculated from a knowledge of the initial and final occupancy vectors, agree with those estimated using tracer particles. With appropriate restrictions, the former method, based on the solution of simultaneous equations, permits a quantitative evaluation of particle mobilities throughout the bed.

Chemistry, Pharmaceutical↗
PubMed1977

A Markov chain characterization of human neutrophil locomotion under neutral and chemotactic conditions.

The locomotion of human neutrophils is modelled by a continuous-time Markov chain model consisting of five states: state O, where the cell is stationary, and four motile states whose directions are defined by the four quadrants of a Cartesian plane. In this paper, the Markov property is verified experimentally in special cases. Further experimental evidence for the model is provided by the waiting-time distributions in each of the five states, which are well approximated by exponential distributions. Using the steady-state distribution of the Markov chain as a measure of the ultimate motion of the cells, it is possible to detect the effect of known chemotactic agents upon neutrophil locomotion. Other useful parameters describing neutrophil locomotion are presented.

Cell Movement↗
PubMed2001

Linkage analysis of a complex pedigree with severe bipolar disorder, using a Markov chain Monte Carlo method.

Recently developed algorithms permit nonparametric linkage analysis of large, complex pedigrees with multiple inbreeding loops. We have used one such algorithm, implemented in the package SimWalk2, to reanalyze previously published genome-screen data from a Costa Rican kindred segregating for severe bipolar disorder. Our results are consistent with previous linkage findings on chromosome 18 and suggest a new locus on chromosome 5 that was not identified using traditional linkage analysis.

Algorithms↗
PubMed1977

Pattern prediction for moving cells.

A continuous-time Markov chain model consisting of four positional and directional states is used to predict the eventual relative positioning of two motile cell types. It is assumed that the period of observation is small in comparison with the generation time of both cell types. The method is useful in predicting developmental phenomena and is applicable to complex patterns involving more than two types of cells.

Cell Movement↗
PubMed2025

Bayesian Inference of Pathogen Phylogeography using the Structured Coalescent Model.

Over the past decade, pathogen genome sequencing has become well established as a powerful approach to study infectious disease epidemiology. In particular, when multiple genomes are available from several geographical locations, comparing them is informative about the relative size of the local pathogen populations as well as past migration rates and events between locations. The structured coalescent model has a long history of being used as the underlying process for such phylogeographic analysis. However, the computational cost of using this model does not scale well to the large number of genomes frequently analysed in pathogen genomic epidemiology studies. Several approximations of the structured coalescent model have been proposed, but their effects are difficult to predict. Here we show how the exact structured coalescent model can be used to analyse a precomputed dated phylogeny, in order to perform Bayesian inference on the past migration history, the effective population sizes in each location, and the directed migration rates from any location to another. We describe an efficient reversible jump Markov Chain Monte Carlo scheme which is implemented in a new R package StructCoalescent. We use simulations to demonstrate the scalability and correctness of our method and to compare it with existing software. We also applied our new method to several state-of-the-art datasets on the population structure of real pathogens to showcase the relevance of our method to current data scales and research questions.

Bayes Theorem↗
PubMedDate not supplied

[Determination of the direction and degree of an animal's tendency to migrate based on a theoretico-probabilistic analysis of its movements in a maza].

Using Markov chains a mathematical model of animal displacement in the final labyrinth is obtained. Direction and value of animal tendency towards migration from a given location point are reflected in the complex of absolute frequencies of the system appearing in its states. When estimating the tendency towards migration in the applied labyrith the whole complex of frequencies can be featured by its component, e. g. by the frequencies of animal appearance in the extreme labyrinth passages.

Animals↗
PubMed1977

A computer program for multiple decrement life table analyses.

Life table analysis has traditionally been the tool of choice in analyzing distribution of "survival" times when a parametric form for the survival curve could not be reasonably assumed. Chiang, in two papers [1,2] formalized the theory of life table analyses in a Markov chain framework and derived maximum likelihood estimates of the relevant parameters for the analyses. He also discussed how the techniques could be generalized to consider competing risks and follow-up studies. Although various computer programs exist for doing different types of life table analysis [3] to date, there has not been a generally available, well documented computer program to carry out multiple decrement analyses, either by Chiang's or any other method. This paper describes such a program developed by Research Triangle Institute. A user's manual is available at printing costs which supplements the contents of this paper with a discussion of the formula used in the program listing.

Computers↗
PubMed1978

Quantitative nuclear image analysis: differentiation between normal, hyperplastic, and malignant appearing uterine glands in a paraffin section. III. The use of texture features for differentiation.

Markov chain statistic based texture features were used to discriminate nuclei from three regions in a paraffin section of human endometrium. The data of the digitized nuclei from which the texture features were calculated were the same as described previously (Baak and Diegenbach, 1977; Diegenbach and Baak, 1977). Several texture features proved to give a good separation between two of the three groups of nuclei. Combining two or more features can make a separation of all three groups possible. Most of the features are correlated, indicating that fewer than the 20 features used are needed for discrimination. The results are much better than with the features described in the previous articles.

Cell Nucleus↗
PubMed2026

GAMMA: gap-aware motif mining under incomplete labeling with applications to MHC motifs.

MOTIVATION: Sequence motif identification is crucial for understanding molecular recognition, particularly in immune responses involving peptide binding to major histocompatibility complex (MHC) Class I molecules for antigen presentation to T cells. Traditionally, MHC Class I binding motifs are assumed to be contiguous and span nine amino acids. However, structural evidence suggests that binding may involve nonadjacent residues, challenging the assumptions of existing methods. RESULTS: In this study, we propose Gap-Aware Motif Mining Algorithm (GAMMA), a probabilistic framework designed to identify noncontiguous motifs under conditions of incomplete labeling. GAMMA employs Bayesian inference with Markov chain Monte Carlo sampling to jointly estimate motif parameters, binding locations, and the relative spacing between binding positions. Through extensive simulations and real-world applications to MHC Class I peptide datasets, GAMMA outperforms existing motif discovery tools such as GLAM2 in accurately localizing binding residues and identifying the underlying motifs. Notably, our results suggest that the true number of binding residues may be eight, fewer than the commonly assumed nine. In addition, for longer peptides, the model captures increased flexibility in the central region, consistent with structural observations that peptides may bulge in the middle. AVAILABILITY AND IMPLEMENTATION: The raw data and the source codes are available on GitHub (https://github.com/RanLIUaca/GAMMAmotif).

Amino Acid Motifs↗
PubMed2025

Navigating Sampling Bias in Discrete Phylogeographic Analysis: Assessing the Performance of an Adjusted Bayes Factor.

Bayesian phylogeographic inference is widely used in molecular epidemiological studies to reconstruct the dispersal history of pathogens. Discrete phylogeographic analysis treats geographic locations as discrete traits and infers lineage transition events among them, and is typically followed by a Bayes factor (BF) test to assess the statistical support. In the standard BF (BFstd) test, the relative abundance of the involved trait states is not considered, which can be problematic in the case of unbalanced sampling. Existing methods to correct sampling bias in discrete phylogeographic analyses using continuous-time Markov chain (CTMC) model, often require additional epidemiological information to balance the sampling effort among locations. As such data is not necessarily available, alternative approaches that rely solely on available genomic data are needed. In this perspective, we assess the performance of a modification of the BFstd, the adjusted Bayes factor (BFadj), which incorporates information on the relative abundance of samples by location when inferring support for transition events and root location inference without requiring additional data. Using a simulation framework, we assess the statistical performance of BFstd and BFadj under varying levels of sampling bias, estimating their type I and type II error rates. Our results show that BFadj complements the BFstd by reducing type I errors at the cost increasing type II errors for inferred transition events, while improving type I and type II errors in root location inference. Our findings provide guidelines for implementing the complementary BFadj to detect and mitigate sampling bias in discrete phylogeographic inference using CTMC modeling.

Bayes Theorem↗
PubMed2025

Bayesian identification of differentially expressed isoforms using a novel joint model of RNA-seq data.

We develop a Bayesian approach, BayesIso, to identify differentially expressed isoforms from RNA-seq data. The approach features a novel joint model of the sample variability and the deferential state of isoforms. Specifically, the within-sample variability and the between-sample variability of each isoform are modeled by a Poisson-Lognormal model and a Gamma-Gamma model, respectively. Using a Bayesian framework, the differential state of each isoform and the model parameters are jointly estimated by a Markov Chain Monte Carlo (MCMC) method. Extensive studies using simulation and real data demonstrate that BayesIso can effectively detect isoforms of less differentially expressed and differential transcripts for genes with multiple isoforms. We applied the approach to breast cancer RNA-seq data and uncovered a unique set of isoforms that form key pathways associated with breast cancer recurrence. First, PI3K/AKT/mTOR signaling and PTEN signaling pathways are identified as being involved in breast cancer development. Further integrated with protein-protein interaction data, pathways of Jak-STAT, mTOR, MAPK and Wnt signaling are revealed in association with breast cancer recurrence. Finally, several pathways are activated in the early recurrence of breast cancer. In tumors that occur early, members of pathways of cellular metabolism and cell cycle (such as CD36 and TOP2A) are upregulated, while immune response genes such as NFATC1 are downregulated.

Humans↗
PubMed2026

Quantifying uncertainty of predictions from cancer progression models.

MOTIVATION: Cancer progresses through the accumulation of genomic events. Cancer progression models such as Mutual Hazard Networks (MHNs) describe this dynamic, enabling prediction of temporal event positions and patient-specific risks of acquiring mutations. However, current MHN analyses rely on single most likely models and do not quantify the uncertainty inherent to parameter estimation. Assessing forecast stability is essential before using them to anticipate treatment-relevant mutations, adapt targeted therapies, or prioritize monitoring of patients at elevated progression risk. RESULTS: We address a key prerequisite for the responsible clinical use of cancer progression models by making MHN-derived predictions uncertainty-aware. We present a Bayesian framework for MHN that uses Markov Chain Monte Carlo to sample from the posterior distributions of model parameters and derived predictions. For practical use we implemented the Random-Walk Metropolis, Metropolis-Adjusted Langevin Algorithm (MALA), and simplified manifold MALA samplers as part of the existing mhn Python package. Only MALA and smMALA were successful in sampling from MHN posteriors, with MALA performing best. While most MHN parameters and predictions showed low posterior variance, a small subset displayed greater variability across the posterior distribution. This differentiation cannot be obtained from a single most likely model, emphasizing the need for uncertainty quantification, especially in clinical contexts. As an illustrative example, posterior sampling identified a subgroup of STK11$-$, KRAS$+$ lung adenocarcinoma patients with a high predicted short-term risk-with low variance across posterior samples-to develop an STK11 mutation. This subgroup exhibited poorer survival under immunotherapy, resembling patterns observed in STK11+ patients. AVAILABILITY AND IMPLEMENTATION: Our implementation is part of version 1.2.0 of the mhn package (https://github.com/spang-lab/LearnMHN). All analyses including the code to produce all figures in this article can be found under https://github.com/huy29433/MCMC-sampling-for-MHN (https://doi.org/10.5281/zenodo.21160219).

Humans↗
PubMed1979

Human lymphocyte migration in vitro: characterization and quantitation of locomotory parameters.

Using a time-lapse cinephotomicrographic technique, the locomotory paths taken by lymphocytes have been shown to satisfy the requirements for a continuous-time Markov chain analysis consisting of four directional states defined by the four quadrants of a Cartesian plane and state 0 in which the cells are stationary. The important parameters characterizing this path motion are the average time (waiting time) that a cell spends in each state and the state transition probabilites which, using the Markov model, allow the computation of the probability of the cells moving in a given direction. Our results show that lymphocytes moving alone on glass exhibit a random migration pattern.

Cell Movement↗
PubMed2025

scACCorDiON: a clustering approach for explainable patient level cell-cell communication graph analysis.

MOTIVATION: Combining single-cell sequencing with ligand-receptor (LR) analysis paves the way for the characterization of cell communication events in complex tissues. In particular, directed weighted graphs naturally represent cell-cell communication events. However, current computational methods cannot yet analyze sample-specific cell-cell communication events, as measured in single-cell data produced in large patient cohorts. Cohort-based cell-cell communication analysis presents many challenges, such as the nonlinear nature of cell-cell communication and the high variability given by the patient-specific single-cell RNAseq datasets. RESULTS: Here, we present scACCorDiON (single-cell Analysis of Cell-Cell Communication in Disease clusters using Optimal transport in Directed Networks), an optimal transport algorithm exploring node distances on the Markov Chain as the ground metric between directed weighted graphs. Benchmarking indicates that scACCorDiON performs a better clustering of samples according to their disease status than competing methods that use undirected graphs. We provide a case study of pancreas adenocarcinoma, where scACCorDion detects a sub-cluster of disease samples associated with changes in the tumor microenvironment. Our study case corroborates that clusters provide a robust and explainable representation of cell-cell communication events and that the expression of detected LR pairs is predictive of pancreatic cancer survival. AVAILABILITY AND IMPLEMENTATION: The code of scACCorDiON is available at https://scaccordion.readthedocs.io/en/latest/. and https://doi.org/10.5281/zenodo.15267648. The survival analysis package can be found at https://github.com/CostaLab/scACCorDiON.su.

Humans↗