6739132
Improving MHC binding peptide prediction by incorporating binding data of auxiliary MHC molecules
MOTIVATION
Various computational methods have been proposed to tackle the problem of predicting the peptide binding ability for a specific MHC molecule. These methods are based on known binding peptide sequences. However, current available peptide databases do not have very abundant amounts of examples and are highly redundant. Existing studies show that MHC molecules can be classified into supertypes in terms of peptide-binding specificities. Therefore, we first give a method for reducing the redundancy in a given dataset based on information entropy, then present a novel approach for prediction by learning a predictive model from a dataset of binders for not only the molecule of interest but also for other MHC molecules.


RESULTS
We experimented on the HLA-A family with the binding nonamers of A1 supertype (HLA-A*0101, A*2601, A*2902, A*3002), A2 supertype (A*0201, A*0202, A*0203, A*0206, A*6802), A3 supertype (A*0301, A*1101, A*3101, A*3301, A*6801) and A24 supertype (A*2301 and A*2402), whose data were collected from six publicly available peptide databases and two private sources. The results show that our approach significantly improves the prediction accuracy of peptides that bind a specific HLA molecule when we combine binding data of HLA molecules in the same supertype. Our approach can thus be used to help find new binders for MHC molecules.
38761790
Social Choice For Data Fusion
Social choice theory is the study of decision theory on how to aggregate separate preferences into group's rational preference. It has wide applications, especially on the design of voting rules, and brings far-reaching influence on the development of modern political science and welfare economics. With the advent of the information age, social choice theory finds its up-to-date application on designing effective Metasearch engines. Metasearch engines provide effective searching by combining the results of multiple source search engines that make use of diverse models and techniques. In this work, we analyze social choice algorithms in a graph-theoretic approach. In addition to classical social choice algorithms, such as Borda and Condorcet, we study one special type of social choice algorithms, elimination voting, to tackle Metasearch problem. Some new algorithms are proposed and examined in the fusion experiment on TREC data. It shows that these elimination voting algorithms achieve satisfied performance when compared with Borda algorithm.
15408418
CatRelate: A New Hierarchical Document Category Integration Algorithm by Learning Category Relationships
We address the problem of integrating documents from a source catalog into a master catalog. Current technologies for solving the problem deem it as a flat category integration problem without considering the useful hierarchy information in the catalog, or deal with it hierarchically but without a rigorous model. In contrast, our method is based on correctly identifying relationships among categories, such as Match, Disjoint, SubConcept, SuperConcept, and Overlap, which come from the relations of sets in Set theory. Compared with traditional Match/NotMatch relationship in literature, our approach is more expressive in defining the relationship. The relationships among categories are first learned in a probabilistic way, and then refined by considering the hierarchy context. Our preliminary experiments show that it can help to correctly identify category relationships, and thus increase the accuracy of document integration.
15097946
Algorithms for clustering high dimensional and distributed data
Clustering is the problem of identifying the distribution of patterns and intrinsic correlations in large data sets by partitioning the data points into similarity classes. The clustering problem has been widely studied in machine learning, databases, and statistics. This paper studies the problem of clustering high dimensional data. The paper proposes an algorithm called the CoFD algorithm, which is a non-distance based clustering algorithm for high dimensional spaces. Based on the Maximum Likelihood Principle, CoFD attempts to optimize its parameter settings to maximize the likelihood between data points and the model generated by the parameters. The distributed versions of the problem, called the D-CoFD algorithms, are also proposed. Experimental results on both synthetic and real data sets show the efficiency and effectiveness of CoFD and D-CoFD algorithms.
7539352
On the Relationship Between Image and Motion Segmentation
In this paper we present a generative model for image sequences, which can be applied to motion segmentation and tracking, and to image sequence compression. The model consists of regions of relatively constant color that have a motion model explaining their motion in time. At each frame, the model can allow accretion and deletion of pixels. We also present an algorithm for maximizing the posterior probability of the image sequence model, based on the recently introduced Swendsen-Wang Cuts algorithm. We show how one can use multiple cues and model switching in a reversible manner to make better bottom-up proposals. The algorithm works on the 3d spatiotemporal pixel volume to reassign entire trajectories of constant color in very few steps, while maintaining detailed balance.
17718282
A Stochastic Algorithm for 3D Scene Segmentation and Reconstruction
In this paper, we present a stochastic algorithm by effective Markov chain Monte Carlo (MCMC) for segmenting and reconstructing 3D scenes. The objective is to segment a range image and its associated reflectance map into a number of surfaces which fit to various 3D surface models and have homogeneous reflectance (material) properties. In comparison to previous work on range image segmentation, the paper makes the following contributions. Firstly, it is aimed at generic natural scenes, indoor and outdoor, which are often much complexer than most of the existing experiments in the "polyhedra world". Natural scenes require the algorithm to automatically deal with multiple types (families) of surface models which compete to explain the data. Secondly, it integrates the range image with the reflectance map. The latter provides material properties and is especially useful for surface of high specularity, such as glass, metal, ceramics. Thirdly, the algorithm is designed by reversible jump and diffusion Markov chain dynamics and thus achieves globally optimal solutions under the Bayesian statistical framework. Thus it realizes the cue integration and multiple model switching. Fourthly, it adopts two techniques to improve the speed of the Markov chain search: One is a coarse-to-fine strategy and the other are data driven techniques such as edge detection and clustering. The data driven methods provide important information for narrowing the search spaces in a probabilistic fashion. We apply the algorithm to two data sets and the experiments demonstrate robust and satisfactory results on both. Based on the segmentation results, we extend the reconstruction of surfaces behind occlusions to fill in the occluded parts.
1511154
Association-based similarity testing and its applications
This paper proposes a new similarity measure between basket datasets based on associations. The new measure is calculated from support counts using a formula inspired by information entropy. Experiments on both real and synthetic datasets show the effectiveness of the measure. This paper then investigates the applications of the similarity measure. It first studies the problem of finding a mapping between categorical database attribute sets using similarity measures. A generic approach for identifying such a mapping is proposed. The approach is implemented based on the similarity measure proposed in the paper and its performance has been evaluated and validated. Moreover, this paper also explores the applications of using the similarity measure to mine distributed datasets.
13451366
Comparing Different Colour Discrimination Data Sets