Evolutionary structure learning algorithm for Bayesian network and penalized mutual information metric

This paper formulates the problem of learning Bayesian network structures from data as determining the structure that best approximates the probability distribution indicated by the data. A new metric, Penalized Mutual Information metric, is proposed, and a evolutionary algorithm is designed to search for the best structure among alternatives. The experimental results show that this approach is reliable and promising.<br /

An examination on the performance of MML causal induction

This paper presents an examination report on the performance of the improved MML based causal model discovery algorithm. In this paper, We firstly describe our improvement to the causal discovery algorithm which introduces a new encoding scheme for measuring the cost of describing the causal structure. Stiring function is also applied to further simplify the computational complexity and thus works more efficiently. It is followed by a detailed examination report on the performance of our improved discovery algorithm. The experimental results of the current version of the discovery system show that: (l) the current version is capable of discovering what discovered by previous system; (2) current system is capable of discovering more complicated causal networks with large number of variables; (3) the new version works more efficiently compared with the previous version in terms of time complexity

Ensemble parameter estimation for graphical models

Parameter Estimation is one of the key issues involved in the discovery of graphical models from data. Current state of the art methods have demonstrated their abilities in different kind of graphical models. In this paper, we introduce ensemble learning into the process of parameter estimation, and examine ensemble parameter estimation methods for different kind of graphical models under complete data set and incomplete data set. We provide experimental results which show that ensemble method can achieve an improved result over the base parameter estimation method in terms of accuracy. In addition, the method is amenable to parallel or distributed processing, which is an important characteristic for data mining in large data sets.<br /

Discovering linear causal model from incomplete data

One common drawback in algorithms for learning Linear Causal Models is that they can not deal with incomplete data set. This is unfortunate since many real problems involve missing data or even hidden variable. In this paper, based on multiple imputation, we propose a three-step process to learn linear causal models from incomplete data set. Experimental results indicate that this algorithm is better than the single imputation method (EM algorithm) and the simple list deletion method, and for lower missing rate, this algorithm can even find models better than the results from the greedy learning algorithm MLGS working in a complete data set. In addition, the method is amenable to parallel or distributed processing, which is an important characteristic for data mining in large data sets.<br /

Microarray-based estimation of SNP allele-frequency in pooled DNA using the Langmuir kinetic model

<p>Abstract</p> <p>Background</p> <p>High throughput genotyping of single nucleotide polymorphisms (SNPs) for genome-wide association requires technologies for generating millions of genotypes with relative ease but also at a reasonable cost and with high accuracy. In this work, we have developed a theoretical approach to estimate allele frequency in pooled DNA samples, based on the physical principles of DNA immobilization and hybridization on solid surface using the Langmuir kinetic model and quantitative analysis of the allelic signals.</p> <p>Results</p> <p>This method can successfully distinguish allele frequencies differing by 0.01 in the actual pool of clinical samples, and detect alleles with a frequency as low as 2%. The accuracy of measuring known allele frequencies is very high, with the strength of correlation between measured and actual frequencies having an r²= 0.9992. These results demonstrated that this method could allow the accurate estimation of absolute allele frequencies in pooled samples of DNA in a feasible and inexpensive way.</p> <p>Conclusion</p> <p>We conclude that this novel strategy for quantitative analysis of the ratio of SNP allelic sequences in DNA pools is an inexpensive and feasible alternative for detecting polymorphic differences in candidate gene association studies and genome-wide linkage disequilibrium scans.</p

Abnormal behavior detection for early warning of terrorist attack

Many terrorist attacks are accomplished by bringing explosive devices hidden in ordinary-looking objects to public places. In such case, it is almost impossible to distinguish a terrorist from ordinary people just from the isolated appearance. However, valuable clues might be discovered through analyzing a series of actions of the same person. Abnormal behaviors of object fetching, deposit, or exchange in public places might indicate potential attacks. Based on the widely equipped CCTV surveillance systems at the entrance of many public places, this paper proposes an algorithm to detect such abnormal behaviors for early warning of terrorist attack.<br /

A new perspective on HIV: effects of HIV on brain-heart axis

The human immunodeficiency virus (HIV) infection can cause damage to multiple systems within the body, and the interaction among these various organ systems means that pathological changes in one system can have repercussions on the functions of other systems. However, the current focus of treatment and research on HIV predominantly centers around individual systems without considering the comprehensive relationship among them. The central nervous system (CNS) and cardiovascular system play crucial roles in supporting human life, and their functions are closely intertwined. In this review, we examine the effects of HIV on the CNS, the resulting impact on the cardiovascular system, and the direct damage caused by HIV to the cardiovascular system to provide new perspectives on HIV treatment