45055182
Mining frequent tree-like patterns in large datasets
Sequential pattern mining is crucial to data mining domains. This paper proposes a novel data mining approach for exploring hierarchical tree structures, named tree-like patterns, representing the relationships for a pair of items in a sequence. Using tree-like patterns, the relationships for a pair of items can be identified in terms of the cause and effect. A novel technique that efficiently counts support values for tree-like patterns using a queue structure is proposed. In addition, this paper addresses an efficient scheme for determining the frequency of a tree-like pattern in a sequence using a dynamic programming approach. Each tree-like pattern embedded in a sequence is considered to have a certain valuable meaning or the degree of importance used in different applications. Two addressed formulas are applied to determine the degree of significance for a specific sequence, which denotes the degree of consecutive items in a tree-like pattern for a sequence. The larger the degree of significance a tree-like pattern has, the more the tree-like pattern is compacted in the sequence. The characteristics differentiating the explored patterns from those obtained with other schemes are discussed. A simulation analysis of the proposed data mining approach is utilized to demonstrate its efficacy. Finally, the proposed approach is designed and implemented in a data mining system integrated into a novel e-learning platform.
41663574
Mining Frequent Tree-Like Patterns in Large Datasets
In this paper, we propose a novel data mining scheme to explore the frequent hierarchical structure patterns, named tree-like patterns, with the relationship of each item on a sequence. By tree-like patterns, we are clear to find out the relation of items between the cause and effect. Finally, we discuss the different characteristics to our mined patterns with others. As a consequence, we can find out that our addressed tree-like patterns can be widely used to explore a variety of different applications.
7383373
Scale-Driven Iterative Optimization for Brain Extraction and Registration
We present a novel framework to automatically separate brain region from other non-brain regions in head images. The idea of the proposed method is to estimate larger scale patterns in an image and then correct the boundaries iteratively. The scale estimation is based on the recently proposed total variation (TV) regularized L^1 functional. An iterative optimization method is used to refine non-convex and acute angle boundaries. The final algorithm is able to extract large scale patterns with arbitrary shapes, which is particularly suitable for brain extraction. In order to reduce the computation overhead in 3D data, a multi-level technique is proposed to exponentially improve the speed of the brain extraction process. Based on accurate results of brain extraction, a non-rigid brain registration algorithm is proposed to improve accuracy and consistency of existing registration methods. Experimental results on real 3D brain MR images demonstrate that the proposed methods outperform existing solutions. In addition, results are provided to show that the proposed algorithm can also be used to segment large scale patterns in general images.
1201195
Prediction of Protein Function Using Protein-Protein Interaction Data
Assigning functions to novel proteins is one of the most important problems in the post-genomic era. Several approaches have been applied to this problem, including analyzing gene expression patterns, phylogenetic profiles, protein fusions and protein-protein interactions. We develop a novel approach that applies the theory of Markov random fields to infer a protein's functions using protein-protein interaction data and the functional annotations of its interaction protein partners. For each function of interest and a protein, we predict the probability that the protein has that function using Bayesian approaches. Unlike in other available approaches for protein annotation where a protein has or does not have a function of interest, we give a probability for having the function. This probability indicates how confident we are about the prediction. We apply our method to predict cellular functions (43 categories including a category "others") for yeast proteins defined in the Yeast Proteome Database (YPD), using the protein-protein interaction data from the Munich Information Center for Protein Sequences (MIPS, http://mips.gsf.de). We show that our approach outperforms other available methods for function prediction based on protein interaction data.
15759301
Animating human face under arbitrary illumination
The authors present a method to animate a human face under arbitrary lighting conditions. We first acquire the reflectance models of a human face in various expressions by employing a robot to precisely control the light position. By developing a relighting tool, named LUXMASTER, we achieve great freedom to change the illumination condition and get a variety of photorealistic novel renderings of the human face. Then, we introduce morphing techniques to add the time dimension and produce a clip of facial animation with varying illumination and expression. In order to make the morphing work easily, we design an expressive, intuitive and efficient tool, called FLUIDMAN, which at the same time enables an enormous possibility of visual effects.
19799763
An approach to isochronous slot reuse for multimedia services on DQDB network
This paper discusses an isochronous connection management protocol and its slot-reuse scheme for multimedia services on DQDB network. For asynchronous services, slot-reuse improves slot utilization, which has already been discussed in depth. However only few literature addressed the slot-reuse for synchronous services. Our proposed scheme reuses video slots for either synchronous or asynchronous services according to the need of downstream nodes. Simulation shows that the scheme significantly increases the maximum number of video connections as well as reduces the failure rate of connection request comparing with that without slot-reuse.
2083478
An integrative approach for causal gene identification and gene regulatory pathway inference
MOTIVATION
Gene expression variation can often be linked to certain chromosomal regions and are tightly associated with phenotypic variation such as disease conditions. Inferring the causal genes for the expression variation is of great importance but rather challenging as the linked region generally contains multiple genes. Even when a single candidate gene is proposed, the underlying biological mechanism by which the regulation is enforced remains unknown. Novel approaches are needed to both infer the causal genes and generate hypothesis on the underlying regulatory mechanisms.


RESULTS
We propose a new approach which aims at achieving the above objectives by integrating genotype information, gene expression, protein-protein interaction, protein phosphorylation, and transcription factor (TF)-DNA binding information. A network based stochastic algorithm is designed to infer the causal genes and identify the underlying regulatory pathways. We first quantitatively verified our method by a test using data generated by yeast knock-out experiments. Over 40% of inferred causal genes are correct, which is significantly better than 10% by random guess. We then applied our method to a recent genome-wide expression variation study in yeast. We show that our method can correctly identify the causal genes and effectively output experimentally verified pathways. New potential gene regulatory pathways are generated and presented as a global network.


AVAILABILITY
Source code is available upon request.