Application of a Hidden Markov Model in Identifying Target Genes in Breast Cancer

2020 ◽  
pp. 523-532
Author(s):  
Parisa Torkaman
Keyword(s):  
Breast Cancer ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Target Genes ◽  
Hidden Markov ◽  
Transition Probability ◽  
Number Of Patients ◽  
Gene Regulatory

Breast cancer is one of the most common malignant cancers among women with increasing number of patients. Gene regulatory network and identifying target genes for cancer treatment, and reducing breast cancer death rates is of great importance medically. This study aims to model gene regulatory network of breast cancer using hidden Markov model which greatly aids doctors in early diagnosis and faster treatment of breast cancer using identification of target genes. In this study, gene expressions of $206$ patients diagnosed with four subtypes of breast cancer including, Basal, Her2, LumA, LumB, were obtained from the Cancer Genome Atlas (TCGA). $8$ genes with the verified interaction among them were investigated by hidden Markov model of gene regulatory network and target genes. with the results of transition probability matrix, FADD, TNFRSF10B, CASP8 are the target genes in the mentioned cancer subtypes so that genes that their transmit probabilities are more than an initial value of $0.125$ are regulatory genes and transmit matrix identifies the probability of the mentioned cancers regarding gene expression level.

scholarly journals Deciphering the genetic links between NAFLD and co-occurring conditions using a liver gene regulatory network

2021 ◽  
Author(s):  
Sreemol Gokuladhas ◽  
William Schierding ◽  
Roan Eltigani Zaied ◽  
Tayaza Fadason ◽  
Murim Choi ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Complex Traits ◽  
Target Genes ◽  
Specific Gene ◽  
Fat Percentage ◽  
Alcoholic Fatty Liver ◽  
Regulatory Interactions ◽  
Hepatic Diseases ◽  
Gene Regulatory

Background & Aims: Non-alcoholic fatty liver disease (NAFLD) is a multi-system metabolic disease that co-occurs with various hepatic and extra-hepatic diseases. The phenotypic manifestation of NAFLD is primarily observed in the liver. Therefore, identifying liver-specific gene regulatory interactions between variants associated with NAFLD and multimorbid conditions may help to improve our understanding of underlying shared aetiology. Methods: Here, we constructed a liver-specific gene regulatory network (LGRN) consisting of genome-wide spatially constrained expression quantitative trait loci (eQTLs) and their target genes. The LGRN was used to identify regulatory interactions involving NAFLD-associated genetic modifiers and their inter-relationships to other complex traits. Results and Conclusions: We demonstrate that MBOAT7 and IL32, which are associated with NAFLD progression, are regulated by spatially constrained eQTLs that are enriched for an association with liver enzyme levels. MBOAT7 transcript levels are also linked to eQTLs associated with cirrhosis, and other traits that commonly co-occur with NAFLD. In addition, genes that encode interacting partners of NAFLD-candidate genes within the liver-specific protein-protein interaction network were affected by eQTLs enriched for phenotypes relevant to NAFLD (e.g. IgG glycosylation patterns, OSA). Furthermore, we identified distinct gene regulatory networks formed by the NAFLD-associated eQTLs in normal versus diseased liver, consistent with the context-specificity of the eQTLs effects. Interestingly, genes targeted by NAFLD-associated eQTLs within the LGRN were also affected by eQTLs associated with NAFLD-related traits (e.g. obesity and body fat percentage). Overall, the genetic links identified between these traits expand our understanding of shared regulatory mechanisms underlying NAFLD multimorbidities.

Analysis of Consumption of Rural Residents in Jilin Province Based on Hidden Markov Model

2014 ◽  
Vol 971-973 ◽  
pp. 2281-2284
Author(s):  
Xin Zhao ◽  
Qian Sun ◽  
Yan Hong Huang Fu ◽  
Chao Ran Li
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Transition Probability ◽  
Jilin Province ◽  
Probability Estimation ◽  
Rural Residents ◽  
Future Situation ◽  
Estimation Function ◽  
Sample Interval

Analysis status consumption of residents according to the statistical data in the recently twenty years of rural residents in Jilin province the Engel Coefficient.Select the sample interval properly based on hidden markov model,modeled using MATLAB and estimate the transition probability between states using probability estimation function of MATLAB’s hidden markov model toolbox, contact probability estimation in Markov model toolbox function, and predicting the Engel Coefficients of rural residents in the province for the next ten years (2013-2022). Studies have shown that, using the hidden Markov model established by MATLAB can accurately predict the future situation of residents consumption.

An Improved Integrative Random Forest for Gene Regulatory Network Inference for Breast Cancer

2018 ◽  
Vol 24 (10) ◽  
pp. 7566-7571
Author(s):  
Suntharaamurthi Chandran ◽  
Kohbalan Moorthy ◽  
Mohd Arfian Ismail ◽  
Mohd Zamri Osman ◽  
Mohd Azwan Mohamad Hamza ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Random Forest ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Network Inference ◽  
Gene Regulatory Network Inference ◽  
Gene Regulatory

scholarly journals Classification of Online Game Players Using Action Transition Probability and Kullback Leibler Entropy

2007 ◽  
Vol 11 (3) ◽  
pp. 319-326 ◽  
Author(s):  
Ruck Thawonmas ◽  
◽  
Ji-Young Ho ◽  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Transition Probability ◽  
Online Game ◽  
Classification Approach ◽  
Game Players ◽  
Better Than

Online game players are more satisfied with contents tailored to their preferences. Player classification is necessary for determining which classes players belong to. In this paper, we propose a new player classification approach using action transition probability and Kullback Leibler entropy. In experiments with two online game simulators, Zereal and Simac, our approach performed better than an existing approach based on action frequency and comparably to another existing approach using the Hidden Markov Model (HMM). Our approach takes into account both the frequency and order of player action. While HMM performance depends on its structure and initial parameters, our approach requires no parameter settings.

scholarly journals Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Christopher A Jackson ◽  
Dayanne M Castro ◽  
Giuseppe-Antonio Saldi ◽  
Richard Bonneau ◽  
David Gresham
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Gene Regulatory Network ◽  
Gene Regulatory Networks ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Target Genes ◽  
Single Cell Rna Sequencing ◽  
Gene Regulatory

Understanding how gene expression programs are controlled requires identifying regulatory relationships between transcription factors and target genes. Gene regulatory networks are typically constructed from gene expression data acquired following genetic perturbation or environmental stimulus. Single-cell RNA sequencing (scRNAseq) captures the gene expression state of thousands of individual cells in a single experiment, offering advantages in combinatorial experimental design, large numbers of independent measurements, and accessing the interaction between the cell cycle and environmental responses that is hidden by population-level analysis of gene expression. To leverage these advantages, we developed a method for scRNAseq in budding yeast (Saccharomyces cerevisiae). We pooled diverse transcriptionally barcoded gene deletion mutants in 11 different environmental conditions and determined their expression state by sequencing 38,285 individual cells. We benchmarked a framework for learning gene regulatory networks from scRNAseq data that incorporates multitask learning and constructed a global gene regulatory network comprising 12,228 interactions.

Recovering Truncated Streaming Event Log Using Coupled Hidden Markov Model

2019 ◽  
Vol 34 (04) ◽  
pp. 2059012
Author(s):  
Riyanarto Sarno ◽  
Kelly Rossa Sungkono
Keyword(s):  
Information Systems ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Process Model ◽  
Hidden Markov ◽  
Transition Probability ◽  
Process Discovery ◽  
Model Based ◽  
Event Logs ◽  
Event Log

Process discovery is a technique for obtaining process model based on traces recorded in the event log. Nowadays, information systems produce streaming event logs to record their huge processes. The truncated streaming event log is a big issue in process discovery because it inflicts incomplete traces that make process discovery depict wrong processes in a process model. Earlier research suggested several methods for recovering the truncated streaming event log and none of them utilized Coupled Hidden Markov Model. This research proposes a method that combines Coupled Hidden Markov Model with Double States and the Modification of Viterbi–Backward method for recovering the truncated streaming event log. The first layer of states contains the transition probability of activities. The second layer of states uses patterns for detecting traces which have a low appearance in the event log. The experiment results showed that the proposed method recovered appropriately the truncated streaming event log. These results also have proven that the accuracies of recovered traces obtained by the proposed method are higher than those obtained by the Hidden Markov Model and the Coupled Hidden Markov Model.

A HIDDEN MARKOV MODEL FOR PREDICTING PROTEIN INTERFACES

2007 ◽  
Vol 05 (03) ◽  
pp. 739-753 ◽  
Author(s):  
CAO NGUYEN ◽  
KATHELEEN J. GARDINER ◽  
KRZYSZTOF J. CIOS
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Protein Interactions ◽  
Protein Function ◽  
Structural Information ◽  
Protein Complexes ◽  
Hidden Markov ◽  
Transition Probability ◽  
Accessible Surface Area ◽  
Protein Chain

Protein–protein interactions play a defining role in protein function. Identifying the sites of interaction in a protein is a critical problem for understanding its functional mechanisms, as well as for drug design. To predict sites within a protein chain that participate in protein complexes, we have developed a novel method based on the Hidden Markov Model, which combines several biological characteristics of the sequences neighboring a target residue: structural information, accessible surface area, and transition probability among amino acids. We have evaluated the method using 5-fold cross-validation on 139 unique proteins and demonstrated precision of 66% and recall of 61% in identifying interfaces. These results are better than those achieved by other methods used for identification of interfaces.

Map-matching approach based on link factor and hidden Markov model

2021 ◽  
pp. 1-17
Author(s):  
Haiyan Zhang ◽  
Yonglong Luo ◽  
Qingying Yu ◽  
Xiaoyao Zheng ◽  
Xuejing Li
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Road Network ◽  
Hidden Markov ◽  
Transition Probability ◽  
Map Matching ◽  
The Road ◽  
Positioning Errors ◽  
Road Section ◽  
Gps Devices

An accurate map matching is an essential but difficult step in mapping raw float car trajectories onto a digital road network. This task is challenging because of the unavoidable positioning errors of GPS devices and the complexity of the road network structure. Aiming to address these problems, in this study, we focus on three improvements over the existing hidden Markov model: (i) The direction feature between the current and historical points is used for calculating the observation probability; (ii) With regard to the reachable cost between the current road section and the destination, we overcome the shortcoming of feature rarefaction when calculating the transition probability with low sampling rates; (iii) The directional similarity shows a good performance in complex intersection environments. The experimental results verify that the proposed algorithm can reduce the error rate in intersection matching and is suitable for GPS devices with low sampling rates.

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