Constructing the boundary between potent and ineffective siRNAs by MG-algorithm with C-features.

BACKGROUND: In siRNA based antiviral therapeutics, selection of potent siRNAs is an indispensable step, but these commonly used features are unable to construct the boundary between potent and ineffective siRNAs. RESULTS: Here, we select potent siRNAs by removing ineffective ones, where these conditions for removals are constructed by C-features of siRNAs, C-features are generated by MG-algorithm, Icc-cluster and the different combinations of some commonly used features, MG-algorithm and Icc-cluster are two different algorithms to search the nearest siRNA neighbors. For the ineffective siRNAs in test data, they are removed from test data by I-iteration, where I-iteration continually updates training data by adding these successively removed siRNAs. Furthermore, the efficacy of siRNAs of test data is predicted by their nearest neighbors of training data. CONCLUSIONS: By siRNAs of Hencken dataset, results show that our algorithm removes almost ineffective siRNAs from test data, gives the clear boundary between potent and ineffective siRNAs, and accurately predicts the efficacy of siRNAs also. We suggest that our algorithm can provide new insights for selecting the potent siRNAs.

Design of Sports Training Simulation System for Children Based on Improved Deep Neural Network.

With the development of AI technology, human-computer interaction technology is no longer the traditional mouse and keyboard interaction. AI and VR have been widely used in early childhood education. In the process of the slow development and application of voice interaction, visual interaction, action interaction, and other technologies, multimodal interaction technology system has become a research hotspot. In this paper, dynamic image capture and recognition technology is integrated into early childhood physical education for intelligent interaction. According to the basic movement process and final node matching in children's sports training to judge children's physical behavior ability, attention is paid to identify the accuracy and safety of movement. The input images and questions are from the abstract clipart dataset of dynamic image recognition and the self-made 3D dataset of Web3D dynamic motion scene with the same style, which is similar to the action content in the actual preschool training teaching. Therefore, according to the idea of process capture and target recognition, on the basis of the original conditions of the recognition model, a new recognition model is developed through Zheng's target detector. The modified model is characterized by higher accuracy. Weapons need to combine process recognition and result recognition. The experimental results show that the improved model has the obvious advantages of high precision and fast speed, which provides a new research idea for the development of children's physical training simulation.

Analyzing the similarity of samples and genes by MG-PCC algorithm, t-SNE-SS and t-SNE-SG maps.

BACKGROUND: For analyzing these gene expression data sets under different samples, clustering and visualizing samples and genes are important methods. However, it is difficult to integrate clustering and visualizing techniques when the similarities of samples and genes are defined by PCC(Person correlation coefficient) measure. RESULTS: Here, for rare samples of gene expression data sets, we use MG-PCC (mini-groups that are defined by PCC) algorithm to divide them into mini-groups, and use t-SNE-SSP maps to display these mini-groups, where the idea of MG-PCC algorithm is that the nearest neighbors should be in the same mini-groups, t-SNE-SSP map is selected from a series of t-SNE(t-statistic Stochastic Neighbor Embedding) maps of standardized samples, and these t-SNE maps have different perplexity parameter. Moreover, for PCC clusters of mass genes, they are displayed by t-SNE-SGI map, where t-SNE-SGI map is selected from a series of t-SNE maps of standardized genes, and these t-SNE maps have different initialization dimensions. Here, t-SNE-SSP and t-SNE-SGI maps are selected by A-value, where A-value is modeled from areas of clustering projections, and t-SNE-SSP and t-SNE-SGI maps are such t-SNE map that has the smallest A-value. CONCLUSIONS: From the analysis of cancer gene expression data sets, we demonstrate that MG-PCC algorithm is able to put tumor and normal samples into their respective mini-groups, and t-SNE-SSP(or t-SNE-SGI) maps are able to display the relationships between mini-groups(or PCC clusters) clearly. Furthermore, t-SNE-SS(m)(or t-SNE-SG(n)) maps are able to construct independent tree diagrams of the nearest sample(or gene) neighbors, where each tree diagram is corresponding to a mini-group of samples(or genes).

Multiple-cumulative probabilities used to cluster and visualize transcriptomes.

Analysis of gene expression data by clustering and visualizing played a central role in obtaining biological knowledge. Here, we used Pearson's correlation coefficient of multiple-cumulative probabilities (PCC-MCP) of genes to define the similarity of gene expression behaviors. To answer the challenge of the high-dimensional MCPs, we used icc-cluster, a clustering algorithm that obtained solutions by iterating clustering centers, with PCC-MCP to group genes. We then used t-statistic stochastic neighbor embedding (t-SNE) of KC-data to generate optimal maps for clusters of MCP (t-SNE-MCP-O maps). From the analysis of several transcriptome data sets, we demonstrated clear advantages for using icc-cluster with PCC-MCP over commonly used clustering methods. t-SNE-MCP-O was also shown to give clearly projecting boundaries for clusters of PCC-MCP, which made the relationships between clusters easy to visualize and understand.

The biological knowledge discovery by PCCF measure and PCA-F projection.

In the process of biological knowledge discovery, PCA is commonly used to complement the clustering analysis, but PCA typically gives the poor visualizations for most gene expression data sets. Here, we propose a PCCF measure, and use PCA-F to display clusters of PCCF, where PCCF and PCA-F are modeled from the modified cumulative probabilities of genes. From the analysis of simulated and experimental data sets, we demonstrate that PCCF is more appropriate and reliable for analyzing gene expression data compared to other commonly used distances or similarity measures, and PCA-F is a good visualization technique for identifying clusters of PCCF, where we aim at such data sets that the expression values of genes are collected at different time points.

Mini-clusters with mean probabilities for identifying effective siRNAs.

BACKGROUND: The distinction between the effective siRNAs and the ineffective ones is in high demand for gene knockout technology. To design effective siRNAs, many approaches have been proposed. Those approaches attempt to classify the siRNAs into effective and ineffective classes but they are difficult to decide the boundary between these two classes. FINDINGS: Here, we try to split effective and ineffective siRNAs into many smaller subclasses by RMP-MiC(the relative mean probabilities of siRNAs with the mini-clusters algorithm). The relative mean probabilities of siRNAs are the modified arithmetic mean value of three probabilities, which come from three Markov chain of effective siRNAs. The mini-clusters algorithm is a modified version of micro-cluster algorithm. CONCLUSIONS: When the RMP-MiC was applied to the experimental siRNAs, the result shows that all effective siRNAs can be identified correctly, and no more than 9% ineffective siRNAs are misidentified as effective ones. We observed that the efficiency of those misidentified ineffective siRNAs exceed 70%, which is very closed to the used efficiency threshold. From the analysis of the siRNAs data, we suggest that the mini-clusters algorithm with relative mean probabilities can provide new insights to the applications for distinguishing effective siRNAs from ineffective ones.