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Background: With the rapid accumulation of proteomic and genomic datasets in terms of genome-scale features and interaction networks through high-throughput experimental techniques, the process of manual predicting functional properties of the proteins has become increasingly cumbersome, and computational methods to automate this annotation task are urgently needed. Most of the approaches in predicting functional properties of proteins require to either identify a reliable set of labeled proteins with similar attribute features to unannotated proteins, or to learn from a fully-labeled protein interaction network with a large amount of labeled data. However, acquiring such labels can be very difficult in practice, especially for multi-label protein function prediction problems. Learning with only a few labeled data can lead to poor performance as limited supervision knowledge can be obtained from similar proteins or from connections between them. To effectively annotate proteins even in the paucity of labeled data, it is important to take advantage of all data sources that are available in this problem setting, including interaction networks, attribute feature information, correlations of functional labels, and unlabeled data. Results: In this paper, we show that the underlying nature of predicting functional properties of proteins using various data sources of relational data is a typical collective classification (CC) problem in machine learning. The protein functional prediction task with limited annotation is then cast into a semi-supervised multi-label collective classification (SMCC) framework. As such, we propose a novel generative model based SMCC algorithm, called GM-SMCC, to effectively compute the label probability distributions of unannotated protein instances and predict their functional properties. To further boost the predicting performance, we extend the method in an ensemble manner, called EGMSMCC, by utilizing multiple heterogeneous networks with various latent linkages constructed to explicitly model the relationships among the nodes for effectively propagate the supervision knowledge from labeled to unlabeled nodes. Conclusion: Experimental results on a yeast gene dataset predicting the functions and localization of proteins demonstrate the effectiveness of the proposed method. In the comparison, we find that the performances of the proposed algorithms are better than the other compared algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new random forest (RF) based ensemble method, ForesTexter, to solve the imbalanced text categorization problems. RF has shown great success in many real-world applications. However, the problem of learning from text data with class imbalance is a relatively new challenge that needs to be addressed. A RF algorithm tends to use a simple random sampling of features in building their decision trees. As a result, it selects many subspaces that contain few, if any, informative features for the minority class. Furthermore, the Gini measure for data splitting is considered to be skew sensitive and bias towards the majority class. Due to the inherent complex characteristics of imbalanced text datasets, learning RF from such data requires new approaches to overcome challenges related to feature subspace selection and cut-point choice while performing node splitting. To this end, we propose a new tree induction method that selects splits, both feature subspace selection and splitting criterion, for RF on imbalanced text data. The key idea is to stratify features into two groups and to generate effective term weighting for the features. One group contains positive features for the minority class and the other one contains the negative features for the majority class. Then, for feature subspace selection, we effectively select features from each group based on the term weights. The advantage of our approach is that each subspace contains adequate informative features for both minority and majority classes. One difference between our proposed tree induction method and the classical RF method is that our method uses Support Vector Machines (SVM) classifier to split the training data into smaller and more balance subsets at each tree node, and then successively retrains the SVM classifiers on the data partitions to refine the model while moving down the tree. In this way, we force the classifiers to learn from refined feature subspaces and data subsets to fit the imbalanced data better. Hence, the tree model becomes more robust for text categorization task with imbalanced dataset. Experimental results on various benchmark imbalanced text datasets (Reuters-21578, Ohsumed, and imbalanced 20 newsgroup) consistently demonstrate the effectiveness of our proposed ForesTexter method. The performance of our proposed approach is competitive against the standard random forest and different variants of SVM algorithms. [ABSTRACT FROM AUTHOR]

Background: Automated assignment of functions to unknown proteins is one of the most important task in computational biology. The development of experimental methods for genome scale analysis of molecular interaction networks offers new ways to infer protein function from protein-protein interaction (PPI) network data. Existing techniques for collective classification (CC) usually increase accuracy for network data, wherein instances are interlinked with each other, using a large amount of labeled data for training. However, the labeled data are timeconsuming and expensive to obtain. On the other hand, one can easily obtain large amount of unlabeled data. Thus, more sophisticated methods are needed to exploit the unlabeled data to increase prediction accuracy for protein function prediction. Results: In this paper, we propose an effective Markov chain based CC algorithm (ICAM) to tackle the label deficiency problem in CC for interrelated proteins from PPI networks. Our idea is to model the problem using two distinct Markov chain classifiers to make separate predictions with regard to attribute features from protein data and relational features from relational information. The ICAM learning algorithm combines the results of the two classifiers to compute the ranks of labels to indicate the importance of a set of labels to an instance, and uses an ICA framework to iteratively refine the learning models for improving performance of protein function prediction from PPI networks in the paucity of labeled data. Conclusion: Experimental results on the real-world Yeast protein-protein interaction datasets show that our proposed ICAM method is better than the other ICA-type methods given limited labeled training data. This approach can serve as a valuable tool for the study of protein function prediction from PPI networks. [ABSTRACT FROM AUTHOR]

With continuous queries that are used widely in location based mobile social networking services, how to protect the location privacy effectively for continuous query has been a hot topic for researchers. In this paper, we analyze the existing location privacy protection systems and algorithms for location based services; considering their disadvantages of slow responding time and high anonymization costs, we propose a new enhanced greedy cloaking algorithm which predicts a cloaking area at the initial query to be the cloaking region in the whole query lifetime by the comprehensive computation of privacy monitor, quality monitor, and dynamic adjuster. Privacy monitor and quality monitor charge the privacy protection level and service quality degree respectively; dynamic adjuster can adjust the cycle center point dynamically. We employ cycle as cloaking region form which can effectively alleviate the computation overhead. And we compare it with the earlier algorithm on three aspects. The experimental result shows that the enhanced greedy cloaking algorithm is better than the original greedy algorithm on average responding time or anonymization cost. [ABSTRACT FROM AUTHOR]

Background: Identifying modules from time series biological data helps us understand biological functionalities of a group of proteins/genes interacting together and how responses of these proteins/genes dynamically change with respect to time. With rapid acquisition of time series biological data from different laboratories or databases, new challenges are posed for the identification task and powerful methods which are able to detect modules with integrative analysis are urgently called for. To accomplish such integrative analysis, we assemble multiple time series biological data into a higher-order form, e.g., a gene × condition × time tensor. It is interesting and useful to develop methods to identify modules from this tensor. Results: In this paper, we present MultiFacTV, a new method to find modules from higher-order time series biological data. This method employs a tensor factorization objective function where a time-related total variation regularization term is incorporated. According to factorization results, MultiFacTV extracts modules that are composed of some genes, conditions and time-points. We have performed MultiFacTV on synthetic datasets and the results have shown that MultiFacTV outperforms existing methods EDISA and Metafac. Moreover, we have applied MultiFacTV to Arabidopsis thaliana root(shoot) tissue dataset represented as a gene×condition×time tensor of size 2395 × 9 × 6(3454 × 8 × 6), to Yeast dataset and Homo sapiens dataset represented as tensors of sizes 4425 × 6 × 6 and 2920×14×9 respectively. The results have shown that MultiFacTV indeed identifies some interesting modules in these datasets, which have been validated and explained by Gene Ontology analysis with DAVID or other analysis. Conclusion: Experimental results on both synthetic datasets and real datasets show that the proposed MultiFacTV is effective in identifying modules for higher-order time series biological data. It provides, compared to traditional non-integrative analysis methods, a more comprehensive and better view on biological process since modules composed of more than two types of biological variables could be identified and analyzed. [ABSTRACT FROM AUTHOR]

Natural disasters caused by heavy rainfall often cause huge loss of life and property. Hence, the task of precipitation nowcasting is of great importance. To solve this problem, several deep learning methods have been proposed to forecast future radar echo images and then the predicted maps are converted to the distribution of rainfall. The prevailing spatiotemporal sequence prediction methods apply ConvRNN structure which combines the Convolution and Recurrent neural network. Although ConvRNN methods achieve remarkable success, they ignore capturing both local and global spatial features simultaneously, which degrades the nowcasting in regions of heavy rainfall. To address this issue, we propose a Region Attention Block (RAB) and embed it into ConvRNN to enhance forecasting in the area with strong rainfall. Besides, the ConvRNN models are hard to memorize longer historical representations with limited parameters. To this end, we propose Recall Attention Mechanism (RAM) to improve the prediction. By preserving longer temporal information, RAM contributes to the forecasting, especially in the middle rainfall intensity. The experiments show that the proposed model Region Attention Predictive Network (RAP-Net) significantly outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Background: Predicting functional properties of proteins in protein-protein interaction (PPI) networks presents a challenging problem and has important implication in computational biology. Collective classification (CC) that utilizes both attribute features and relational information to jointly classify related proteins in PPI networks has been shown to be a powerful computational method for this problem setting. Enabling CC usually increases accuracy when given a fully-labeled PPI network with a large amount of labeled data. However, such labels can be difficult to obtain in many real-world PPI networks in which there are usually only a limited number of labeled proteins and there are a large amount of unlabeled proteins. In this case, most of the unlabeled proteins may not connected to the labeled ones, the supervision knowledge cannot be obtained effectively from local network connections. As a consequence, learning a CC model in sparsely-labeled PPI networks can lead to poor performance. Results: We investigate a latent graph approach for finding an integration latent graph by exploiting various latent linkages and judiciously integrate the investigated linkages to link (separate) the proteins with similar (different) functions. We develop a regularized non-negative matrix factorization (RNMF) algorithm for CC to make protein functional properties prediction by utilizing various data sources that are available in this problem setting, including attribute features, latent graph, and unlabeled data information. In RNMF, a label matrix factorization term and a network regularization term are incorporated into the non-negative matrix factorization (NMF) objective function to seek a matrix factorization that respects the network structure and label information for classification prediction. Conclusion: Experimental results on KDD Cup tasks predicting the localization and functions of proteins to yeast genes demonstrate the effectiveness of the proposed RNMF method for predicting the protein properties. In the comparison, we find that the performance of the new method is better than those of the other compared CC algorithms especially in paucity of labeled proteins. [ABSTRACT FROM AUTHOR]

Background: Predicting functional properties of proteins in protein-protein interaction (PPI) networks presents a challenging problem and has important implication in computational biology. Collective classification (CC) that utilizes both attribute features and relational information to jointly classify related proteins in PPI networks has been shown to be a powerful computational method for this problem setting. Enabling CC usually increases accuracy when given a fully-labeled PPI network with a large amount of labeled data. However, such labels can be difficult to obtain in many real-world PPI networks in which there are usually only a limited number of labeled proteins and there are a large amount of unlabeled proteins. In this case, most of the unlabeled proteins may not connected to the labeled ones, the supervision knowledge cannot be obtained effectively from local network connections. As a consequence, learning a CC model in sparsely-labeled PPI networks can lead to poor performance. Results: We investigate a latent graph approach for finding an integration latent graph by exploiting various latent linkages and judiciously integrate the investigated linkages to link (separate) the proteins with similar (different) functions. We develop a regularized non-negative matrix factorization (RNMF) algorithm for CC to make protein functional properties prediction by utilizing various data sources that are available in this problem setting, including attribute features, latent graph, and unlabeled data information. In RNMF, a label matrix factorization term and a network regularization term are incorporated into the non-negative matrix factorization (NMF) objective function to seek a matrix factorization that respects the network structure and label information for classification prediction. Conclusion: Experimental results on KDD Cup tasks predicting the localization and functions of proteins to yeast genes demonstrate the effectiveness of the proposed RNMF method for predicting the protein properties. In the comparison, we find that the performance of the new method is better than those of the other compared CC algorithms especially in paucity of labeled proteins. [ABSTRACT FROM AUTHOR]