scholarly journals Evolutionary Distance of Gene-Gene Interactions: Estimation under Statistical Uncertainty

2020 ◽  
Author(s):  
Xun Gu
Keyword(s):  
Fundamental Problem ◽  
Markov Chain Model ◽  
Gene Interaction ◽  
Interaction Networks ◽  
Chain Model ◽  
Interaction Distance ◽  
Highly Sensitive ◽  
The Status ◽  
Or Gene ◽  
High Level

AbstractConsider the functional interaction of gene A to an interaction subject X; for instance, it is the gene-gene interaction if X represents for a gene, or gene-tissue interaction (expression status) if X for a tissue. In the simplest case, the status of this A-X interaction is r=1 if they are interacted, or r=0 otherwise. A fundamental problem in molecular evolution is, given two homologous (orthologous or paralogous) genes A and B, to what extent their functional overlapping could be by the means of interaction networks. Given a set of interaction subjects (X1, … XN), it is straightforward to calculate the interaction distance (IAB) between genes A and B, by a Markov-chain model. However, since the high throughput interaction data always involve a high level of noises, reliable inference of r=1 or r=0 for each gene remains a big challenge. Consequently, the estimated interaction distance (IAB) is highly sensitive to the cutoff of interaction inference which is subject to some arbitrary. In this paper we will address this issue by developing a statistical method for estimating IAB based on the p-values (significant levels). Computer simulations are carried out to evaluate the performance of different p-value transformations against the uncertainty of interaction networks.

scholarly journals Drought Forecast Using Standardized Precipitation Index and Markov Chain in Iran

2018 ◽  
Author(s):  
Hadi Siasar ◽  
F Shahrdarazi ◽  
Saeed Shojaei
Keyword(s):  
Markov Chain ◽  
Water Resources ◽  
Markov Chain Model ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Annual Data ◽  
Chain Model ◽  
Planning And Management ◽  
The Status ◽  
The Standardized Precipitation Index

Drought is a natural disaster which occurs as a result of a lack of ambient humidity due to reduced rainfall compared to normal conditions. Successful planning and management of water in agriculture and proper use of water resources is subject to recognition of this disaster. As drought is inevitable, we can minimize its damages through monitoring and forecasting. It is therefore important to predict drought in the planning and management of water resources. In studying the status of drought in Saravan city, rain annual data of synoptic station in the period (1976-2015) and the Standardized Precipitation Index (SPI) in a twelve-month time scale have been used. Then the Markov chain model was used to calculate the probabilities of the balance of the periods of wet, dry, and normal in SPI. The results showed that the probability of balance in wet, dry, and normal periods in Saravan station is 80.2, 24.20, and 96.76 respectively. This means that the area is most of the time in normal conditions and the probability of draught is about 7 times bigger than rain.

scholarly journals A model of the New Zealand beef value chain: evaluating opportunities

2005 ◽  
pp. 81-86
Author(s):  
A.K. Mcdermott ◽  
D.C. Smeaton ◽  
G.W. Sheath ◽  
A.E. Dooley
Keyword(s):  
New Zealand ◽  
Value Chain ◽  
National Level ◽  
Chain Model ◽  
Land Price ◽  
Beef Industry ◽  
Long Run ◽  
The Status ◽  
High Level ◽  
Beef Value Chain

A model of the New Zealand beef value chain, from conception to export, was constructed. The model was parameterised at the national level so that issues and opportunities within the beef industry can be examined at a high level by researchers and industry participants. The model is capable of modelling changes in farm practice, market situations and the industry structure. To illustrate the integrative power and value of the model in evaluating change within the beef sector, three scenarios are presented and compared to the status quo: changes in land price; wider use of beef semen in the dairy industry; and introduction of a gene to improve net feed intake. From the three scenarios presented, it is apparent that land price dominates the ability of the NZ beef industry to create value in the long-run. Although behaviour, practices and technologies can contribute to overcoming this factor, such changes will need to be substantive - incremental improvements will not be sufficient. This model provides the basis for facilitating debate on the future of NZ's beef industry and how to ensure long-run profitability. Keywords: beef industry, scenario evaluation, beef systems, value chain model

scholarly journals Semantic-Enhanced Multi-Dimensional Markov Chains on Semantic Trajectories for Predicting Future Locations

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3582 ◽  
Author(s):  
Antonios Karatzoglou ◽  
Dominik Köhler ◽  
Michael Beigl
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
Markov Chain Model ◽  
Predictive Performance ◽  
Chain Model ◽  
Semantic Level ◽  
Trajectory Mining ◽  
Solid Foundation ◽  
Semantic Similarity Analysis ◽  
High Level

In this work, we investigate the performance of Markov Chains with respect to modelling semantic trajectories and predicting future locations. In the first part, we examine whether and to what degree the semantic level of semantic trajectories affects the predictive performance of a spatial Markov model. It can be shown that the choice of the semantic level when describing trajectories has a significant impact on the accuracy of the models. High-level descriptions lead to better results than low-level ones. The second part introduces a multi-dimensional Markov Chain construct that considers, besides locations, additional context information, such as time, day and the users’ activity. While the respective approach is able to outperform our baseline, we could also identify some limitations. These are mainly attributed to its sensitivity towards small-sized training datasets. We attempt to overcome this issue, among others, by adding a semantic similarity analysis component to our model that takes the varying role of locations due each time to the respective purpose of visiting the particular location explicitly into consideration. To capture the aforementioned dynamics, we define an entity, which we refer to as Purpose-of-Visit-Dependent Frame (PoVDF). In the third part of this work, we describe in detail the PoVDF-based approach and we evaluate it against the multi-dimensional Markov Chain model as well as with a semantic trajectory mining and prefix tree based model. Our evaluation shows that the PoVDF-based approach outperforms its competition and lays a solid foundation for further investigation.

scholarly journals Performance Analysis in Table Tennis - Stochastic Simulation by Numerical Derivation

2016 ◽  
Vol 15 (1) ◽  
pp. 22-36 ◽  
Author(s):  
S. Wenninger ◽  
M. Lames
Keyword(s):  
Performance Analysis ◽  
Markov Chain Model ◽  
Chain Model ◽  
Table Tennis ◽  
Simulation Approach ◽  
Winning Probability ◽  
Risky Play ◽  
High Level ◽  
The Impact ◽  
Positive Effect

Abstract The aim of this study was to identify the impact of different tactical behaviors on the winning probability in table tennis. The performance analysis was done by mathematical simulation using a Markov chain model. 259 high-level table tennis games were evaluated by means of a new simulation approach using numerical derivation to remove the necessity to perform a second modeling step in order to determine the difficulty of tactical behaviors. Based on the derivation, several mathematical constructs like directional derivations and the gradient are examined for application in table tennis. Results reveal errors and long rallies as the most influencing game situations, together with the positive effect of risky play on the winning probability of losing players.

An Efficient Markov Chain Model for the Simulation of Heterogeneous Soil Structure

2004 ◽  
Vol 68 (2) ◽  
pp. 346 ◽  
Author(s):  
Keijan Wu ◽  
Naoise Nunan ◽  
John W. Crawford ◽  
Iain M. Young ◽  
Karl Ritz
Keyword(s):  
Markov Chain ◽  
Soil Structure ◽  
Markov Chain Model ◽  
Chain Model ◽  
Heterogeneous Soil

Analysis of Protein-Protein Interaction Networks through Computational Approaches

2020 ◽  
Vol 27 (4) ◽  
pp. 265-278 ◽  
Author(s):  
Ying Han ◽  
Liang Cheng ◽  
Weiju Sun
Keyword(s):  
Protein Interaction ◽  
Biological Networks ◽  
Interaction Networks ◽  
Computational Techniques ◽  
Cellular Functions ◽  
Protein Protein Interaction ◽  
Comprehensive Information ◽  
Protein Interaction Prediction ◽  
Or Gene ◽  
Experimental Findings

The interactions among proteins and genes are extremely important for cellular functions. Molecular interactions at protein or gene levels can be used to construct interaction networks in which the interacting species are categorized based on direct interactions or functional similarities. Compared with the limited experimental techniques, various computational tools make it possible to analyze, filter, and combine the interaction data to get comprehensive information about the biological pathways. By the efficient way of integrating experimental findings in discovering PPIs and computational techniques for prediction, the researchers have been able to gain many valuable data on PPIs, including some advanced databases. Moreover, many useful tools and visualization programs enable the researchers to establish, annotate, and analyze biological networks. We here review and list the computational methods, databases, and tools for protein−protein interaction prediction.

Data Mining Technique Applied To DNA Sequencing

2015 ◽  
Vol 2 (7) ◽  
pp. 18
Author(s):  
R. Jamuna
Keyword(s):  
Data Mining ◽  
Dna Methylation ◽  
Markov Chain ◽  
Human Genome ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Effective Means ◽  
Cpg Islands ◽  
Chain Model ◽  
The Given

CpG islands (CGIs) play a vital role in genome analysis as genomic markers.  Identification of the CpG pair has contributed not only to the prediction of promoters but also to the understanding of the epigenetic causes of cancer. In the human genome [1] wherever the dinucleotides CG occurs the C nucleotide (cytosine) undergoes chemical modifications. There is a relatively high probability of this modification that mutates C into a T. For biologically important reasons the mutation modification process is suppressed in short stretches of the genome, such as ‘start’ regions. In these regions [2] predominant CpG dinucleotides are found than elsewhere. Such regions are called CpG islands. DNA methylation is an effective means by which gene expression is silenced. In normal cells, DNA methylation functions to prevent the expression of imprinted and inactive X chromosome genes. In cancerous cells, DNA methylation inactivates tumor-suppressor genes, as well as DNA repair genes, can disrupt cell-cycle regulation. The most current methods for identifying CGIs suffered from various limitations and involved a lot of human interventions. This paper gives an easy searching technique with data mining of Markov Chain in genes. Markov chain model has been applied to study the probability of occurrence of C-G pair in the given   gene sequence. Maximum Likelihood estimators for the transition probabilities for each model and analgously for the  model has been developed and log odds ratio that is calculated estimates the presence or absence of CpG is lands in the given gene which brings in many  facts for the cancer detection in human genome.

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