Rui Xu

Rui Xu
Institute of Electrical and Electronics Engineers | IEEE · IEEE Computational Intelligence Society

PhD

About

41
Publications
13,404
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6,642
Citations
Citations since 2016
0 Research Items
3387 Citations
20162017201820192020202120220100200300400500
20162017201820192020202120220100200300400500
20162017201820192020202120220100200300400500
20162017201820192020202120220100200300400500

Publications

Publications (41)
Article
Demand response (DR) programs provide incentives for load reductions during event periods. Measuring these reductions requires estimating load if there was no event, known as baselines. Differences in metered load below the baseline are assumed to be load reductions. Baselines are typically calculated from historical data, sometimes with adjustment...
Article
Swarm intelligence has emerged as a worthwhile class of clustering methods due to its convenient implementation, parallel capability, ability to avoid local minima, and other advantages. In such applications, clustering validity indices usually operate as fitness functions to evaluate the qualities of the obtained clusters. However, as the validity...
Article
Full-text available
It is very difficult to analyze large amounts of hyperspectral data. Here we present a method based on reducing the dimensionality of the data and clustering the result in moving toward classification of the data. Dimensionality reduction is done with diffusion maps, which interpret the eigenfunctions of Markov matrices as a system of coordinates o...
Article
Clustering has been used extensively in the analysis of high-throughput messenger RNA (mRNA) expression profiling with microarrays. Furthermore, clustering has proven elemental in microRNA expression profiling, which demonstrates enormous promise in the areas of cancer diagnosis and treatment, gene function identification, therapy development and d...
Conference Paper
The applications of recently developed meta-heuristics in cluster analysis, such as particle swarm optimization (PSO) and differential evolution (DE), have increasingly attracted attention and popularity in a wide variety of communities owing to their effectiveness in solving complicated combinatorial optimization problems. Here, we propose to use...
Article
Applications of clustering algorithms in biomedical research are ubiquitous, with typical examples including gene expression data analysis, genomic sequence analysis, biomedical document mining, and MRI image analysis. However, due to the diversity of cluster analysis, the differing terminologies, goals, and assumptions underlying different cluster...
Article
The importance of gene expression data in cancer diagnosis and treatment has become widely known by cancer researchers in recent years. However, one of the major challenges in the computational analysis of such data is the curse of dimensionality because of the overwhelming number of variables measured (genes) versus the small number of samples. He...
Article
High-throughput messenger RNA (mRNA) expression profiling with microarray has been demonstrated as a more effective method of cancer diagnosis and treatment than the traditional morphology or clinical parameter based methods. Recently, the discovery of a category of small non-coding RNAs, named microRNAs (miRNAs), provides another promising method...
Conference Paper
The presence of large amounts of data in hyperspectral images makes it very difficult to perform further tractable analyses. Here, we present a method of analyzing real hyperspectral data by dimensionality reduction using diffusion maps. Diffusion maps interpret the eigenfunctions of Markov matrices as a system of coordinates on the original data s...
Conference Paper
High-throughput messenger RNA (mRNA) expression profiling with microarray has been demonstrated as a more effective method of cancer diagnosis and treatment than the traditional morphology or clinical parameter-based methods. Recently, the discovery of a class of small non-coding RNAs, named microRNAs (miRNAs), provides another promising method of...
Article
To classify objects based on their features and characteristics is one of the most important and primi- tive activities of human beings. The task becomes even more challenging when there is no ground truth available. Cluster analysis allows new opportunities in exploring the unknown nature of data through its aim to separate a finite data set, with...
Article
Purpose The purpose of this paper is to provide a review of the issues related to cluster analysis, one of the most important and primitive activities of human beings, and of the advances made in recent years. Design/methodology/approach The paper investigates the clustering algorithms rooted in machine learning, computer science, statistics, and...
Conference Paper
Full-text available
Early detection of a tumorpsilas site of origin is particularly important for cancer diagnosis and treatment. The employment of gene expression profiles for different cancer types or subtypes has already shown significant advantages over traditional cancer classification methods. Here, we apply a neural network clustering theory, Fuzzy ART, to gene...
Conference Paper
The importance of gene expression data in cancer diagnosis and treatment by now has been widely recognized by cancer researchers in recent years. However, one of the major challenges in the computational analysis of such data is the curse of dimensionality, due to the overwhelming number of measures of gene expression levels versus the small number...
Article
This is the first book to take a truly comprehensive look at clustering. It begins with an introduction to cluster analysis and goes on to explore: proximity measures; hierarchical clustering; partition clustering; neural network-based clustering; kernel-based clustering; sequential data clustering; large-scale data clustering; data visualization a...
Chapter
IntroductionFeature Types and Measurement LevelsDefinition of Proximity MeasuresProximity Measures for Continuous VariablesProximity Measures for Discrete VariablesProximity Measures for Mixed VariablesSummary
Chapter
IntroductionClustering CriteriaK-Means AlgorithmMixture Density-Based ClusteringGraph Theory-Based ClusteringFuzzy ClusteringSearch Techniques-Based Clustering AlgorithmsApplicationsSummary
Chapter
IntroductionLinear Projection AlgorithmsNonlinear Projection AlgorithmsProjected and Subspace ClusteringApplicationsSummary
Chapter
IntroductionHard Competitive Learning ClusteringSoft Competitive Learning ClusteringApplicationsSummary
Chapter
IntroductionExternal CriteriaInternal CriteriaRelative CriteriaSummary
Chapter
IntroductionRandom Sampling MethodsCondensation-Based MethodsDensity-Based MethodsGrid-Based Methods Divide and ConquerIncremental ClusteringApplicationsSummary
Chapter
IntroductionAgglomerative Hierarchical ClusteringDivisive Hierarchical ClusteringRecent AdvancesApplicationsSummary
Conference Paper
The possibility of the usage of deadly aerosolized pathogens, particularly anthrax, in bioterrorist attack has raised tremendous concerns in recent years. Several anthrax incubation models have been introduced in order to characterize the incubation period of human inhalation anthrax. It is important to accurately identify the model that fits best...
Article
Full-text available
Genetic regulatory network inference is critically important for revealing fundamental cellular processes, investigating gene functions, and understanding their relations. The availability of time series gene expression data makes it possible to investigate the gene activities of whole genomes, rather than those of only a pair of genes or among sev...
Article
In the last decade, recurrent neural networks (RNNs) have attracted more efforts in inferring genetic regulatory networks (GRNs), using time series gene expression data from microarray experiments. This is critically important for revealing fundamental cellular processes, investigating gene functions, and understanding their relations. However, RNN...
Article
Full-text available
Early detection of a tumor's site of origin is particularly important for cancer diagnosis and treatment. The employment of gene expression profiles for different cancer types or subtypes has already shown significant advantages over traditional cancer classification methods. One of the major problems in cancer type recognition-oriented gene expres...
Chapter
Full-text available
Cluster analysis plays an important role for understanding various phenomena and exploring the nature of obtained data. A remarkable diversity of ideas, in a wide range of disciplines, has been applied to clustering research. Here, we survey clustering algorithms in computational intelligence, particularly based on neural networks and kernel-based...
Conference Paper
Gene regulatory inference from time series gene expression data, generated from DNA microarray, has become increasingly important in investigating genes functions and unveiling fundamental cellular processes. Computational methods in machine learning and neural networks play an active role in analyzing the obtained data. Here, we investigate the pe...
Article
Full-text available
Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey...
Article
Gene expression profiles have become an important and promising way for cancer prognosis and treatment. In addition to their application in cancer class prediction and discovery, gene expression data can be used for the prediction of patient survival. Here, we use particle swarm optimization (PSO) to address one of the major challenges in gene expr...
Conference Paper
Full-text available
Large-scale time series gene expression data generated from DNA microarray experiments provide us a new means to reveal fundamental cellular processes, investigate functions of genes, and understand their relations and interactions. To infer gene regulatory networks from these data with effective computational tools has attracted intensive efforts...
Conference Paper
Large-scale gene expression data coming from microarray experiments provide us a new means to reveal fundamental cellular processes, investigate functions of genes, and understand relations and interactions among them. To infer genetic regulatory networks from these data with effective computational tools has become increasingly important. Several...
Article
Full-text available
To accurately identify the site of origin of a tumor is crucial to cancer diagnosis and treatment. With the emergence of DNA microarray technologies, constructing gene expression profiles for different cancer types has already become a promising means for cancer classification. In addition to binary classification, the discrimination of multiple tu...
Article
Large-scale gene expression data coming from microarray experiments provide us a new means to reveal fundamental cellular processes, investigate functions of genes, and understand relations and interactions among them. To infer genetic regulatory networks from these data with effective computational tools has become increasingly important Several m...
Conference Paper
Full-text available
With the emergence and rapid advancement of DNA microarray technologies, construction of gene expression profiles for different cancer types has already become a promising means for cancer diagnosis and treatment. Most previous research has focused on binary classification. Here, we use a probabilistic neural network (PNN) for multi-classification...
Article
Full-text available
Correct classification is crucial to cancer diagnosis and treatment. In the paper, we demonstrate that a new family of neural network architectures - Ellipsoid ART and ARTMAP (EA/EAM) can cluster and classify tissues successfully through analysis of gene expression data generated by DNA microarray experiments.
Conference Paper
Full-text available
Given an encoded unknown text message in the form of a three dimensional spatial series generated by the use of four smooth nonlinear functions, we use a method based on simple statistical reasoning to pick up samples for rebuilding the four functions. The estimated functions are then used to decode the sequence. The experimental results show that...

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