On Efficient Feature Ranking Methods for High-Throughput Data Analysis

Object Detection in Medical Images Based on Hierarchical Transformer and Mask Mechanism

The object detection task in the medical field is challenging in terms of classification and regression. Due to its crucial applications in computer-aided diagnosis and computer-aided detection techniques, an increasing number of researchers are transferring the object detection techniques to the medical field. However, in existing work on object detection, researchers do not consider the low resolution of medical images, the high amount of noise, and the small size of the objects to be detected. Based on this, this paper proposes a new algorithmic model called the MS Transformer, where a self-supervised learning approach is used to perform a random mask on the input image to reconstruct the input features, learn a richer feature vector, and filter out excessive noise. To focus the model on the small objects that are being detected, the hierarchical transformer model is introduced in this paper, and a sliding window with a local self-attention mechanism is used to give a higher attention score to the small objects to be detected. Finally, a single-stage object detection framework is used to predict the sequence of sets at the location of the bounding box and the class of objects to be detected. On the DeepLesion and BCDD benchmark dataset, the model proposed in this paper achieves better performance improvement on multiple evaluation metric categories.

A disease-related gene mining method based on weakly supervised learning model

BACKGROUND

Predicting disease-related genes is helpful for understanding the disease pathology and the molecular mechanisms during the disease progression. However, traditional methods are not suitable for screening genes related to the disease development, because there are some samples with weak label information in the disease dataset and a small number of genes are known disease-related genes.

RESULTS

We designed a disease-related gene mining method based on the weakly supervised learning model in this paper. The method is separated into two steps. Firstly, the differentially expressed genes are screened based on the weakly supervised learning model. In the model, the strong and weak label information at different stages of the disease progression is fully utilized. The obtained differentially expressed gene set is stable and complete after the algorithm converges. Then, we screen disease-related genes in the obtained differentially expressed gene set using transductive support vector machine based on the difference kernel function. The difference kernel function can map the input space of the original Huntington's disease gene expression dataset to the difference space. The relation between the two genes can be evaluated more accurately in the difference space and the known disease-related gene information can be used effectively.

CONCLUSIONS

The experimental results show that the disease-related gene mining method based on the weakly supervised learning model can effectively improve the precision of the disease-related gene prediction compared with other excellent methods.

Flexible Non-Negative Matrix Factorization to Unravel Disease-Related Genes

Recently, non-negative matrix factorization (NMF) has been shown to perform well in the analysis of omics data. NMF assumes that the expression level of one gene is a linear additive composition of metagenes. The elements in metagene matrix represent the regulation effects and are restricted to non-negativity. However, according to the real biological meaning, there are two kinds of regulation effects, i.e., up-regulation and down-regulation. Few methods based on NMF have considered this biological meaning. Therefore, we designed a flexible non-negative matrix factorization (FNMF) algorithm by further considering the biological meaning of gene expression data. It allows negative numbers in the metagene matrix, and negative numbers represent down-regulation effects. We separated gene expression data into disease-driven gene expression and background gene expression. Subsequently, we computed disease-driven gene relative expression, and a ranked list of genes was obtained. The top ranked genes are considered to be involved in some disease-related biological processes. Experimental results on two real-world gene expression data demonstrate the feasibility and effectiveness of FNMF. Compared with conventional disease-related gene identification algorithms, FNMF has superior performance in analyzing gene expression data of diseases with complex pathology.

Improved Pre-miRNAs Identification Through Mutual Information of Pre-miRNA Sequences and Structures

Playing critical roles as post-transcriptional regulators, microRNAs (miRNAs) are a family of short non-coding RNAs that are derived from longer transcripts called precursor miRNAs (pre-miRNAs). Experimental methods to identify pre-miRNAs are expensive and time-consuming, which presents the need for computational alternatives. In recent years, the accuracy of computational methods to predict pre-miRNAs has been increasing significantly. However, there are still several drawbacks. First, these methods usually only consider base frequencies or sequence information while ignoring the information between bases. Second, feature extraction methods based on secondary structures usually only consider the global characteristics while ignoring the mutual influence of the local structures. Third, methods integrating high-dimensional feature information is computationally inefficient. In this study, we have proposed a novel mutual information-based feature representation algorithm for pre-miRNA sequences and secondary structures, which is capable of catching the interactions between sequence bases and local features of the RNA secondary structure. In addition, the feature space is smaller than that of most popular methods, which makes our method computationally more efficient than the competitors. Finally, we applied these features to train a support vector machine model to predict pre-miRNAs and compared the results with other popular predictors. As a result, our method outperforms others based on both 5-fold cross-validation and the Jackknife test.

Predicting Influenza Antigenicity by Matrix Completion With Antigen and Antiserum Similarity

The rapid mutation of influenza viruses especially on the two surface proteins hemagglutinin (HA) and neuraminidase (NA) has made them capable to escape from population immunity, which has become a key challenge for influenza vaccine design. Thus, it is crucial to predict influenza antigenic evolution and identify new antigenic variants in a timely manner. However, traditional experimental methods like hemagglutination inhibition (HI) assay to select vaccine strains are time and labor-intensive, while popular computational methods are less sensitive, which presents the need for more accurate algorithms. In this study, we have proposed a novel low-rank matrix completion model MCAAS to infer antigenic distances between antigens and antisera based on partially revealed antigenic distances, virus similarity based on HA protein sequences, and vaccine similarity based on vaccine strains. The model exploits the correlations of viruses and vaccines in serological tests as well as the ability of HAs from viruses and vaccine strains in inferring influenza antigenicity. We also compared the effects of comprehensive 65 amino acids substitution matrices in predicting influenza antigenicity. As a result, we applied MCAAS into H3N2 seasonal influenza virus data. Our model achieved a 10-fold cross validation root-mean-squared error (RMSE) of 0.5982, significantly outperformed existing computational methods like antigenic cartography, AntigenMap and BMCSI. We also constructed the antigenic map and studied the association between genetic and antigenic evolution of H3N2 influenza viruses. Finally, our analyses showed that homologous structure derived amino acid substitution matrix (HSDM) is most powerful in predicting influenza antigenicity, which is consistent with previous studies.

Differentially Coexpressed Disease Gene Identification Based on Gene Coexpression Network

Screening disease-related genes by analyzing gene expression data has become a popular theme. Traditional disease-related gene selection methods always focus on identifying differentially expressed gene between case samples and a control group. These traditional methods may not fully consider the changes of interactions between genes at different cell states and the dynamic processes of gene expression levels during the disease progression. However, in order to understand the mechanism of disease, it is important to explore the dynamic changes of interactions between genes in biological networks at different cell states. In this study, we designed a novel framework to identify disease-related genes and developed a differentially coexpressed disease-related gene identification method based on gene coexpression network (DCGN) to screen differentially coexpressed genes. We firstly constructed phase-specific gene coexpression network using time-series gene expression data and defined the conception of differential coexpression of genes in coexpression network. Then, we designed two metrics to measure the value of gene differential coexpression according to the change of local topological structures between different phase-specific networks. Finally, we conducted meta-analysis of gene differential coexpression based on the rank-product method. Experimental results demonstrated the feasibility and effectiveness of DCGN and the superior performance of DCGN over other popular disease-related gene selection methods through real-world gene expression data sets.