Single-cell RNA sequencing (scRNA-seq) is one of the most exciting technological breakthroughs that many believe would revolutionise medical research. At the same time, this new technology has created some urgent data-mining challenges. Unlike sequencing data obtained from the old bulk sequencing technology, the scRNA-seq data are more noisy and biased. One particular data challenge is dropouts, where a low amount of mRNA leads to zero detection. Recently, an appealing idea has been to embrace dropouts and use potential dropout patterns for analysis. It has led to surprising results. In this paper, we take this idea further and focus on the problem of recovering gene dynamics from single-cell data. We show that dropouts can mislead the most commonly used model to produce the wrong dynamics. We propose a solution with two components, a nonlinear neural model based on neural ODEs and a hurdle distribution adaptable to potential signals in dropout patterns. We provide empirical evidence that demonstrates the advantages of our proposed model over a state-of-the-art method for scRNA-seq analysis.

Reconstructing gene regulatory networks (GRNs) from expression data is vital for understanding gene transcrip- tion. Although increasingly advanced algorithms, particularly deep learning models, are proposed to mine potential gene regulatory interactions, insufficient effort has been invested in improving the reliability of features in the presence of biological variability between expression samples. In this research, we propose a robust feature fusion method inspired by emerging attention-based techniques in computer vision. Our method can capture the functional asymmetry between transcription factors (TF) and target genes with an important adaptation using differentiated attention heads. Based on three different gene expression datasets: in silico, E.coli, and S.cerevisiae, we demonstrate that our method is superior to other state-of-the- art competitors. The overall GRN reconstruction performance of our method yields a gain of 3%-14% in the AUC score over the competing models.

Gene expression data often contain missing values that, if not handled properly, may mislead or invalidate the downstream analyses. With the emergence of graph neural networks (GNN), domain knowledge about gene regulation can be leveraged to guide the missing data imputation. We show in this paper, however, that naive application of GNN on the raw gene-expression data can actually lead to worse imputation. We analyse this problem considering both the intrinsic property of GNN message passing and potential data-knowledge inconsistency. We propose two measures towards optimal integration of biological networks in the gene-expression missing data imputation. These include expression data normalisation and a weighting scheme for GNN message passing. Experiments on two different biological networks and gene expression datasets show that our method outperforms state-of-the-art generic imputation algorithms and alternative GNN models, obtaining lower mean absolute error (MAE) consistently.

With the increasing development of cloud computing, a number of users choose to outsource their data to a remote database service provider and enjoy flexible data sharing with multiple parties. However, the integrity and confidentiality of outsourced data in a remote cloud server are the main threats users are concerned about. The tree structure is one of the most widely used data organization structures. It is crucial to guarantee the integrity and privacy not only for the content but also for the structure if sensitive information is organized in this structure. At present, despite some solutions have been put forward, none of these considers the additional redaction attack on tree-structured data from attackers while sharing data with others. In this article, we provide a construction of secure redactable signature scheme for tree-structured data which features a multi-party control on the redaction of vertexes in a tree while the authenticity and privacy are assured. Then, we prove that our scheme is unforgeable, private, and transparent. Furthermore, extensive theoretical and experimental analyses are conducted to assess the efficiency of our scheme. The results demonstrate our scheme achieves multi-party redaction control without sacrificing sensible resources especially when a tree has a larger number of vertexes and siblings per vertex.

With the rapid advancement of World Wide Web, people can share their knowledge and information via online tools such as sharing systems and ecommerce applications. Many approaches have been proposed to process and organize information. Recommender systems are good successful examples of such tools in providing personalized suggestions. The main purpose of a recommender system is to identify and introduce desired items of a user among many other options (e.g. music, movies, books, news and etc). The goal of our proposed method is to provide a recommender system based on information diffusion and popularity in social networks. By adding popularity, similarity and users' trusts a more efficient system is proposed. This approach makes an improvement in tackling the issues and defects of the previous methods such as prediction accuracy and coverage. The evaluation of the simulated proposed method on MovieLens and Epinions datasets shows that it provides more accurate recommendations in comparison to other approaches.

BACKGROUND: Microarray datasets consist of complex and high-dimensional samples and genes, and generally the number of samples is much smaller than the number of genes. Due to this data imbalance, gene selection is a demanding task for microarray expression data analysis. RESULTS: The gene set selected by DGS has shown its superior performances in cancer classification. DGS has a high capability of reducing the number of genes in the original microarray datasets. The experimental comparisons with other representative and state-of-the-art gene selection methods also showed that DGS achieved the best performance in terms of the number of selected genes, classification accuracy, and computational cost. CONCLUSIONS: We provide an efficient gene selection algorithm can select relevant genes which are significantly sensitive to the samples' classes. With the few discriminative genes and less cost time by the proposed algorithm achieved much high prediction accuracy on several public microarray data, which in turn verifies the efficiency and effectiveness of the proposed gene selection method.

Example-based texture synthesis aims to generate a new texture from an exemplar texture and has long been drawing attention in the fields of computer graphics, computer vision, and image processing. Nevertheless, synthesizing structured textures remains a challenging task. Most previous methods rely on additional guidance channels, which encode the structured features of textures. However, estimating the guidance channel is very difficult, and often fails when a texture has unpronounced features. In this paper, we propose a novel texture synthesis method, based on non-local operators, which captures the long-range structure of a texture without the additional guidance channel. The synthesized texture is generated by minimizing non-local texture energy through an expectation-maximization like optimization algorithm. A statistical constraint based on the Wasserstein distance is also proposed to ensure that the synthesized texture preserves the global statistics of the exemplar texture. Extensive experiments show that the proposed method can stably handle textures with different scale structures.

Lung cancer has different subtypes which are different in cell size and growth pattern. Correctly classifying subtypes of lung cancer can help design specific treatments to increase patient survival rate. In this work, we propose an innovative Structural Binary Classification (SBC) strategy for classifying lung cancer subtypes using microarray data. The strategy is based on Gene Expression Programming (GEP) algorithm. Classification performance evaluations and comparisons between our GEP based model and common binary decomposition strategies, as well as three representative machine learning methods, support vector machine, neural network and C4.5, were conducted thoroughly on real microarray lung cancer datasets. Reliability was assessed by the cross-data set validation. The experimental results showed that GEP model with our strategy outperformed other models in terms of accuracy, standard deviation and area under the receiver operating characteristic curve. The work provides a useful tool for lung cancer classification based on tumour structural information.

Group key is used to encrypt group data in group communication. Multicast key distribution scheme updates and distributes group keys safely when a member joins or leaves the group. However it has problems when arbitrary group members want to build a dynamic conference, because traditional distribution scheme uses group controller to manage the work. In fact, a n-member group will have 2n- n - 1 possible conferences. Moreover, members may need to join and/or leave the group dynamically. If a group controller deals with all the members' requests, it will be the performance bottleneck. In this paper, we propose a new stateful multicast key distribution protocol based on identity-based encryption. This protocol can distribute the group keys safely and allows the group members to build dynamic conferences by themselves. It greatly reduces the group controller's workload. In addition, compared with traditional rekeying messages contain identifier and group key's version, our protocol can reduce the size of rekeying messages by customizing the public key. We provide three algorithms (INIT, JOIN, LEAVE) for rekeying when a group membership changes and a revocation algorithm for building dynamic conferences without the group controller. We also give the security proof of our protocol in a symbolic security model and the implementation of the protocol.

BACKGROUND: Current approaches of predicting protein functions from a protein-protein interaction (PPI) dataset are based on an assumption that the available functions of the proteins (a.k.a. annotated proteins) will determine the functions of the proteins whose functions are unknown yet at the moment (a.k.a. un-annotated proteins). Therefore, the protein function prediction is a mono-directed and one-off procedure, i.e. from annotated proteins to un-annotated proteins. However, the interactions between proteins are mutual rather than static and mono-directed, although functions of some proteins are unknown for some reasons at present. That means when we use the similarity-based approach to predict functions of un-annotated proteins, the un-annotated proteins, once their functions are predicted, will affect the similarities between proteins, which in turn will affect the prediction results. In other words, the function prediction is a dynamic and mutual procedure. This dynamic feature of protein interactions, however, was not considered in the existing prediction algorithms. RESULTS: In this paper, we propose a new prediction approach that predicts protein functions iteratively. This iterative approach incorporates the dynamic and mutual features of PPI interactions, as well as the local and global semantic influence of protein functions, into the prediction. To guarantee predicting functions iteratively, we propose a new protein similarity from protein functions. We adapt new evaluation metrics to evaluate the prediction quality of our algorithm and other similar algorithms. Experiments on real PPI datasets were conducted to evaluate the effectiveness of the proposed approach in predicting unknown protein functions. CONCLUSIONS: The iterative approach is more likely to reflect the real biological nature between proteins when predicting functions. A proper definition of protein similarity from protein functions is the key to predicting functions iteratively. The evaluation results demonstrated that in most cases, the iterative approach outperformed non-iterative ones with higher prediction quality in terms of prediction precision, recall and F-value.

This paper presents two hyperlink analysis-based algorithms to find relevant pages for a given Web page (URL). The first algorithm comes from the extended cocitation analysis of the Web pages. It is intuitive and easy to implement. The second one takes advantage of linear algebra theories to reveal deeper relationships among the Web pages and to identify relevant pages more precisely and effectively. The experimental results show the feasibility and effectiveness of the algorithms. These algorithms could be used for various Web applications, such as enhancing Web search. The ideas and techniques in this work would be helpful to other Web-related research.

The paper presents a prototype visualization system for text information retrieval and its technical details on algorithm design. The system supports the user in constructing an initial query type and further refining the query by visual interaction. Visualization of text information retrieval is an attractive research area in information retrieval. There are many mathematical algorithms used for information retrieval. The authors investigate an approach to construct visual representations based on singular value decomposition (SVD) of matrices and implement visual interfaces using Java. Experimental results show the feasibility of the proposed approach. The ideas in this approach can also be helpful in other visualization environments.