For hyperspectral images (HSIs), it is a challenging task to select discriminative bands due to the lack of labeled samples and complex noise. In this article, we present a novel local-view-assisted discriminative band selection method with hypergraph autolearning (LvaHAl) to solve these problems from both local and global perspectives. Specifically, the whole band space is first randomly divided into several subspaces (LVs) of different dimensions, where each LV denotes a set of low-dimensional representations of training samples consisting of bands associated with it. Then, for different LVs, a robust hinge loss function for isolated pixels regularized by the row-sparsity is adopted to measure the importance of the corresponding bands. In order to simultaneously reduce the bias of LVs and encode the complementary information between them, samples from all LVs are further projected into the label space. Subsequently, a hypergraph model that automatically learns the hyperedge weights is presented. In this way, the local manifold structure of these projections can be preserved, ensuring that samples of the same class have a small distance. Finally, a consensus matrix is used to integrate the importance of bands corresponding to different LVs, resulting in the optimal selection of expected bands from a global perspective. The classification experiments on three HSI data sets show that our method is competitive with other comparison methods.

BACKGROUND: Subcellular localization prediction of protein is an important component of bioinformatics, which has great importance for drug design and other applications. A multitude of computational tools for proteins subcellular location have been developed in the recent decades, however, existing methods differ in the protein sequence representation techniques and classification algorithms adopted. RESULTS: In this paper, we firstly introduce two kinds of protein sequences encoding schemes: dipeptide information with space and Gapped k-mer information. Then, the Gapped k-mer calculation method which is based on quad-tree is also introduced. CONCLUSIONS: >From the prediction results, this method not only reduces the dimension, but also improves the prediction precision of protein subcellular localization.

For applications based on hyperspectral imagery (HSI), selecting informative and representative bands without the degradation of performance is a challenging task in the context of big data. In this paper, an unsupervised band selection method, scalable one-pass self-representation learning (SOP-SRL), is proposed to address this problem by processing data in a streaming fashion without storing the entire data. SOP-SRL embeds band selection into a scalable self-representation learning, which is formulated as an adaptive linear combination of regression-based loss functions, with the row-sparsity constraint. To further enhance the representativeness of bands, the local similarity between samples constructed by the selected bands is dynamically measured by means of graph-based regularization term in the embedded space. Moreover, a cache with memory function that reflects the quality of bands in the historical data is designed to keep the consistency between data coming at different times and guide subsequent band selection. An efficient algorithm is developed to optimize the SOP-SRL model. The HSI classification is conducted on three public data sets, and the experimental results validate the superiority of SOP-SRL in terms of performance and time when compared with other state-of-the-art band selection methods.

For hyperspectral image classification, high-dimensional spectral features not only increase the computational and storage burden but also degrade the classification accuracy due to the Hughes phenomenon. Band selection is an important technique to solve these issues without destroying the interpretation of data. This paper presents a matrix-based margin-maximization method of band selection with data-driven diversity. In particular, the matrices composed of adjacent pixels in space are fed to the hinge loss function with a row-sparse constraint. This constraint is used to select the expected bands and preserve the spatial structure information simultaneously while maximizing the margin between the classes. In consideration of the continuity of bands in the spectral dimension, a novel regularization term that is continually updated according to the current context is added to promote the differential expression of dissimilar bands. Finally, the one-against-all parallel mechanism is used to learn a coefficient matrix for each class, and class-related bands are then carefully selected by a partitioning strategy (e.g., k-means clustering) based on the learned coefficient matrix. Experiments are conducted on three hyperspectral data sets and four widely used classifiers. The experimental results have shown that our proposed method is superior to several state-of-the-art methods, especially when the number of selected bands is relatively small.

MOTIVATION: Many forms of variations exist in the human genome including single nucleotide polymorphism, small insert/deletion (DEL) (indel) and structural variation (SV). Somatically acquired SV may regulate the expression of tumor-related genes and result in cell proliferation and uncontrolled growth, eventually inducing tumor formation. Virus integration with host genome sequence is a type of SV that causes the related gene instability and normal cells to transform into tumor cells. Cancer SVs and viral integration sites must be discovered in a genome-wide scale for clarifying the mechanism of tumor occurrence and development. RESULTS: In this paper, we propose a new tool called seeksv to detect somatic SVs and viral integration events. Seeksv simultaneously uses split read signal, discordant paired-end read signal, read depth signal and the fragment with two ends unmapped. Seeksv can detect DEL, insertion, inversion and inter-chromosome transfer at single-nucleotide resolution. Different types of sequencing data, such as single-end sequencing data or paired-end sequencing data can accommodate to detect SV. Seeksv develops a rescue model for SV with breakpoints located in sequence homology regions. Results on simulated and real data from the 1000 Genomes Project and esophageal squamous cell carcinoma samples show that seeksv has higher efficiency and precision compared with other similar software in detecting SVs. For the discovery of hepatitis B virus integration sites from probe capture data, the verified experiments show that more than 90% viral integration sequences detected by seeksv are true. AVAILABILITY AND IMPLEMENTATION: seeksv is implemented in C ++ and can be downloaded from https://github.com/qkl871118/seeksv CONTACT: : [email protected] information: Supplementary data are available at Bioinformatics online.

Identifying potential associations between drugs and targets is a critical prerequisite for modern drug discovery and repurposing. However, predicting these associations is difficult because of the limitations of existing computational methods. Most models only consider chemical structures and protein sequences, and other models are oversimplified. Moreover, datasets used for analysis contain only true-positive interactions, and experimentally validated negative samples are unavailable. To overcome these limitations, we developed a semi-supervised based learning framework called NormMulInf through collaborative filtering theory by using labeled and unlabeled interaction information. The proposed method initially determines similarity measures, such as similarities among samples and local correlations among the labels of the samples, by integrating biological information. The similarity information is then integrated into a robust principal component analysis model, which is solved using augmented Lagrange multipliers. Experimental results on four classes of drug-target interaction networks suggest that the proposed approach can accurately classify and predict drug-target interactions. Part of the predicted interactions are reported in public databases. The proposed method can also predict possible targets for new drugs and can be used to determine whether atropine may interact with alpha1B- and beta1- adrenergic receptors. Furthermore, the developed technique identifies potential drugs for new targets and can be used to assess whether olanzapine and propiomazine may target 5HT2B. Finally, the proposed method can potentially address limitations on studies of multitarget drugs and multidrug targets.

Efficient mining of high-throughput data has become one of the popular themes in the big data era. Existing biology-related feature ranking methods mainly focus on statistical and annotation information. In this study, two efficient feature ranking methods are presented. Multi-target regression and graph embedding are incorporated in an optimization framework, and feature ranking is achieved by introducing structured sparsity norm. Unlike existing methods, the presented methods have two advantages: (1) the feature subset simultaneously account for global margin information as well as locality manifold information. Consequently, both global and locality information are considered. (2) Features are selected by batch rather than individually in the algorithm framework. Thus, the interactions between features are considered and the optimal feature subset can be guaranteed. In addition, this study presents a theoretical justification. Empirical experiments demonstrate the effectiveness and efficiency of the two algorithms in comparison with some state-of-the-art feature ranking methods through a set of real-world gene expression data sets.

Gene selection based on microarray data, is highly important for classifying tumors accurately. Existing gene selection schemes are mainly based on ranking statistics. From manifold learning standpoint, local geometrical structure is more essential to characterize features compared with global information. In this study, we propose a supervised gene selection method called locality sensitive Laplacian score (LSLS), which incorporates discriminative information into local geometrical structure, by minimizing local within-class information and maximizing local between-class information simultaneously. In addition, variance information is considered in our algorithm framework. Eventually, to find more superior gene subsets, which is significant for biomarker discovery, a two-stage feature selection method that combines the LSLS and wrapper method (sequential forward selection or sequential backward selection) is presented. Experimental results of six publicly available gene expression profile data sets demonstrate the effectiveness of the proposed approach compared with a number of state-of-the-art gene selection methods.