Search

Alzheimer's disease (AD) is an incurable neurodegenerative disease, so it is important to intervene in the early stage of the disease. Brain imaging genetics is an effective technique to identify AD-related biomarkers, which can early diagnosis of AD patients once they are clinically verified. With the development of medical imaging and gene sequencing techniques, the association analysis between multi-modal imaging data and genetic data has garnered increasing attention. However, current imaging genetics studies have problem with non-intuitive data fusion. Meanwhile, the characteristics of multi-modal imaging genetics data are high-dimensional, non-linearity, and fewer subjects, so it is necessary to select effective features. In this paper, a multi-modal data fusion framework by deep auto-encoder and self-representation (MFASN) was proposed for early diagnosis of AD. First, a multi-modality brain network was constructed by combining information from the resting-state functional magnetic resonance imaging (fMRI) data and structural magnetic resonance imaging (sMRI) data. Then, we utilized the deep auto-encoder to achieve non-linear transformations and select the informative features. A sparse self-representation module was employed to capture the multi-subspaces structure of the latent representation. At last, a multi-task structured sparse association model was developed to fully mine correlations between the genetic data and multi-modal brain network features. Experiments on AD neuroimaging initiative datasets proved the superiority of the proposed method, while discovering discriminative biomarkers were strongly associated with AD. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • RPCA was introduced into the gene expression data as a processing method to achieve data denoising and reconstruction. • Influence by low-rank characteristics of RPCA, network construction was carried out on the reconstructed integrated data of multi-cancers, and some common cancer-related clues were detected. • The RPCA-based denoising network mining model is reliable and efficient. Abstract The noise problem of cancer sequencing data has been a problem that can't be ignored. Utilizing considerable way to reduce noise of these cancer data is an important issue in the analysis of gene co-expression network. In this paper, we apply a sparse and low-rank method which is Robust Principal Component Analysis (RPCA) to solve the noise problem for integrated data of multi-cancers from The Cancer Genome Atlas (TCGA). And then we build the gene co-expression network based on the integrated data after noise reduction. Finally, we perform nodes and pathways mining on the denoising networks. Experiments in this paper show that after denoising by RPCA, the gene expression data tend to be orderly and neat than before, and the constructed networks contain more pathway enrichment information than unprocessed data. Moreover, learning from the betweenness centrality of the nodes in the network, we find some abnormally expressed genes and pathways proven that are associated with many cancers from the denoised network. The experimental results indicate that our method is reasonable and effective, and we also find some candidate suspicious genes that may be linked to multi-cancers. [ABSTRACT FROM AUTHOR]

Many experiments have proved that long non-coding RNAs (lncRNAs) in humans have been implicated in disease development. The prediction of lncRNA-disease association is essential in promoting disease treatment and drug development. It is time-consuming and laborious to explore the relationship between lncRNA and diseases in the laboratory. The computation-based approach has clear advantages and has become a promising research direction. This paper proposes a new lncRNA disease association prediction algorithm BRWMC. Firstly, BRWMC constructed several lncRNA (disease) similarity networks based on different measurement angles and fused them into an integrated similarity network by similarity network fusion (SNF). In addition, the random walk method is used to preprocess the known lncRNA-disease association matrix and calculate the estimated scores of potential lncRNA-disease associations. Finally, the matrix completion method accurately predicts the potential lncRNA-disease associations. Under the framework of leave-one-out cross-validation and 5-fold cross-validation, the AUC values obtained by BRWMC are 0.9610 and 0.9739, respectively. In addition, case studies of three common diseases show that BRWMC is a reliable method for prediction. [Display omitted] • The lncRNAs (diseases) without any known association have learned new association information through the random walk method. • Many potential lncRNA-disease associations are mined by the random walk method. • The expression of the lncRNA-disease association strength in the known association matrix is more accurate. • BRWMC has constructed multiple lncRNA similarity networks and disease similarity networks in the preparation stage. [ABSTRACT FROM AUTHOR]

• Based on L 21 -norm, a robust Extreme Learning Machine method called L 21 -ELM is proposed. • Various benchmark datasets downloaded from the UCI database and some image datasets are used to train and test the model. • The proposed L 21 -ELM is applied to the classification of cancer samples and single-cell data. • The proposed method not only inherits the advantages of original ELM, such as easy implementation and fast speed, but also shows better generalization performance. With the development of cancer research, various gene expression datasets containing cancer information show an explosive growth trend. In addition, due to the continuous maturity of single-cell RNA sequencing (scRNA-seq) technology, the protein information and pedigree information of a single cell are also continuously mined. It is a technical problem of how to classify these high-dimensional data correctly. In recent years, Extreme Learning Machine (ELM) has been widely used in the field of supervised learning and unsupervised learning. However, the traditional ELM does not consider the robustness of the method. To improve the robustness of ELM, in this paper, a novel ELM method based on L 2 , 1 -norm named L 2 , 1 -Extreme Learning Machine (L 2 , 1 -ELM) has been proposed. The method introduces L 2 , 1 -norm on loss function to improve the robustness, and minimizes the influence of noise and outliers. Firstly, we evaluate the new method on five UCI datasets. The experiment results prove that our method can achieve competitive results. Next, the novel method is applied to the problem of classification of cancer samples and single-cell RNA sequencing datasets. The experimental results on The Cancer Genome Atlas (TCGA) datasets and scRNA-seq datasets prove that ELM and its variants has great potential in the classification of cancer samples. [ABSTRACT FROM AUTHOR]

It is of urgency to effectively identify differentially expressed genes from RNA-Seq data. In this paper, we proposed a novel method, joint-L 2,1 -norm-constraint-based semi-supervised feature extraction (L21SFE), to analyze RNA-Seq data. Our scheme was shown as follows. Firstly, we constructed a graph Laplacian matrix and refined it by using the labeled samples. Our graph construction method can make full use of a large number of unlabelled samples. Secondly, we found semi-supervised optimal maps by solving a generalized eigenvalue problem. Thirdly, we solved an optimal problem via the joint L 2,1 -norm constraint to obtain a projection matrix. It can diminish the impact of noises and outliers by using the L 2,1 -norm constraint and produce more precise results. Finally, we identified differentially expressed genes based on the projection matrix. The results on simulation and real RNA-Seq data sets demonstrated the feasibility and effectiveness of our method. [ABSTRACT FROM AUTHOR]

With the rapid development of DNA microarray technology and next-generation technology, a large number of genomic data were generated. So how to extract more differentially expressed genes from genomic data has become a matter of urgency. Because Low-Rank Representation (LRR) has the high performance in studying low-dimensional subspace structures, it has attracted a chunk of attention in recent years. However, it does not take into consideration the intrinsic geometric structures in data. In this paper, a new method named Laplacian regularized Low-Rank Representation (LLRR) has been proposed and applied on genomic data, which introduces graph regularization into LRR. By taking full advantages of the graph regularization, LLRR method can capture the intrinsic non-linear geometric information among the data. The LLRR method can decomposes the observation matrix of genomic data into a low rank matrix and a sparse matrix through solving an optimization problem. Because the significant genes can be considered as sparse signals, the differentially expressed genes are viewed as the sparse perturbation signals. Therefore, the differentially expressed genes can be selected according to the sparse matrix. Finally, we use the GO tool to analyze the selected genes and compare the P-values with other methods. The results on the simulation data and two real genomic data illustrate that this method outperforms some other methods: in differentially expressed gene selection. [ABSTRACT FROM AUTHOR]