Attentive Aspect Modeling for Review-aware Recommendation

In recent years, many studies extract aspects from user reviews and integrate them with ratings for improving the recommendation performance. The common aspects mentioned in a user's reviews and a product's reviews indicate indirect connections between the user and product. However, these aspect-based methods suffer from two problems. First, the common aspects are usually very sparse, which is caused by the sparsity of user-product interactions and the diversity of individual users' vocabularies. Second, a user's interests on aspects could be different with respect to different products, which are usually assumed to be static in existing methods. In this paper, we propose an Attentive Aspect-based Recommendation Model (AARM) to tackle these challenges. For the first problem, to enrich the aspect connections between user and product, besides common aspects, AARM also models the interactions between synonymous and similar aspects. For the second problem, a neural attention network which simultaneously considers user, product and aspect information is constructed to capture a user's attention towards aspects when examining different products. Extensive quantitative and qualitative experiments show that AARM can effectively alleviate the two aforementioned problems and significantly outperforms several state-of-the-art recommendation methods on top-N recommendation task.Comment: Camera-ready manuscript for TOI

Inference of Gene Regulatory Networks Using Bayesian Nonparametric Regression and Topology Information

Gene regulatory networks (GRNs) play an important role in cellular systems and are important for understanding biological processes. Many algorithms have been developed to infer the GRNs. However, most algorithms only pay attention to the gene expression data but do not consider the topology information in their inference process, while incorporating this information can partially compensate for the lack of reliable expression data. Here we develop a Bayesian group lasso with spike and slab priors to perform gene selection and estimation for nonparametric models. B-spline basis functions are used to capture the nonlinear relationships flexibly and penalties are used to avoid overfitting. Further, we incorporate the topology information into the Bayesian method as a prior. We present the application of our method on DREAM3 and DREAM4 datasets and two real biological datasets. The results show that our method performs better than existing methods and the topology information prior can improve the result

A kernel-based multivariate feature selection method for microarray data classification.

High dimensionality and small sample sizes, and their inherent risk of overfitting, pose great challenges for constructing efficient classifiers in microarray data classification. Therefore a feature selection technique should be conducted prior to data classification to enhance prediction performance. In general, filter methods can be considered as principal or auxiliary selection mechanism because of their simplicity, scalability, and low computational complexity. However, a series of trivial examples show that filter methods result in less accurate performance because they ignore the dependencies of features. Although few publications have devoted their attention to reveal the relationship of features by multivariate-based methods, these methods describe relationships among features only by linear methods. While simple linear combination relationship restrict the improvement in performance. In this paper, we used kernel method to discover inherent nonlinear correlations among features as well as between feature and target. Moreover, the number of orthogonal components was determined by kernel Fishers linear discriminant analysis (FLDA) in a self-adaptive manner rather than by manual parameter settings. In order to reveal the effectiveness of our method we performed several experiments and compared the results between our method and other competitive multivariate-based features selectors. In our comparison, we used two classifiers (support vector machine, [Formula: see text]-nearest neighbor) on two group datasets, namely two-class and multi-class datasets. Experimental results demonstrate that the performance of our method is better than others, especially on three hard-classify datasets, namely Wang's Breast Cancer, Gordon's Lung Adenocarcinoma and Pomeroy's Medulloblastoma

Detecting Susceptibility to Breast Cancer with SNP-SNP Interaction Using BPSOHS and Emotional Neural Networks

Studies for the association between diseases and informative single nucleotide polymorphisms (SNPs) have received great attention. However, most of them just use the whole set of useful SNPs and fail to consider the SNP-SNP interactions, while these interactions have already been proven in biology experiments. In this paper, we use a binary particle swarm optimization with hierarchical structure (BPSOHS) algorithm to improve the effective of PSO for the identification of the SNP-SNP interactions. Furthermore, in order to use these SNP interactions in the susceptibility analysis, we propose an emotional neural network (ENN) to treat SNP interactions as emotional tendency. Different from the normal architecture, just as the emotional brain, this architecture provides a specific path to treat the emotional value, by which the SNP interactions can be considered more quickly and directly. The ENN helps us use the prior knowledge about the SNP interactions and other influence factors together. Finally, the experimental results prove that the proposed BPSOHS_ENN algorithm can detect the informative SNP-SNP interaction and predict the breast cancer risk with a much higher accuracy than existing methods

The genes expression levels of two datasets, namely the Leukemia and the Lymphoma data.

<p>Expression levels for each gene are normalized across the samples such that the mean is 0 and the SD is 1. Expression levels greater than the mean are shaded in black, and those below the mean are shaded in white. (a) The expression profiles of the Lymphoma dataset. Each row corresponds to a gene, with the columns corresponding to expression levels in different samples. (b) The expression profiles of the Leukemia dataset. Each row expresses a probe while each column describes expression level in different samples. (c) Display the results on the Lymphoma dataset using sammon mapping. This projection expresses the gene expression levels of genes that perfectly separate the three types of Lymphoma subtypes, i.e. DLBCL, FL, and CLL.</p

Comparison of kernelPLS with four other methods. For 10-fold cross validation classification accuracy(%) and AUC (%) of SVM on two-class datasets.

<p>Comparison of kernelPLS with four other methods. For 10-fold cross validation classification accuracy(%) and AUC (%) of SVM on two-class datasets.</p

Abstract An adjustable algorithm for color quantization

Color quantization is an important technique in digital image processing. Generally it involves two steps. The first step is to choose a proper color palette. The second step is to reconstruct an image by replacing original colors with the most similar palette colors. However a problem exists while choosing palette colors. That is how to choose the colors with different illumination intensities (we call them color layers) as well as the colors that present the essential details of the image. This is an important and difficult problem. In this paper, we propose a novel algorithm for color quantization, which considers both color layers and essential details by assigning weights for pixel numbers and color distances. Also this algorithm can tune the quantization results by choosing proper weights. The experiments show that our algorithm is effective for adjusting quantization results and it also has very good quality of quantization. Ó 2004 Elsevier B.V. All rights reserved