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Flynet |
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miRAS |
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Multi-omics tools |
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DL-PER |
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Flynet |
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Flynet is a genomic resource for Drosophila melanogaster transcriptional regulatory networks. To support research on Drosophila melanogaster transcription regulation and make the genome wide in vivo protein-DNA interactions data available to the scientific community as a whole, we have developed a system called Flynet. Currently, Flynet contains 101 datasets for 38 transcription factors and chromatin regulator proteins in different experimental conditions. These factors exhibit different types of binding profiles ranging from sharp localized peaks to broad binding regions. The protein-DNA interaction data in Flynet was obtained from the analysis of chromatin immunoprecipitation experiments on one color and two color genomic tiling arrays as well as chromatin immunoprecipitation followed by massively parallel sequencing. A web-based interface, integrated with an AJAX based genome browser, has been built for queries and presenting analysis results. Flynet also makes available the cis-regulatory modules reported in literature, known and de novo identified sequence motifs across the genome, and other resources to study gene regulation. |
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miRAS |
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miRAS is a data processing system for miRNA expression profiling study. With miRAS, miRNAs and possible miRNA candidates contained in the submitted sequencing data were obtained together with their expression profile. The functions of known and predicted miRNAs were then analyzed by miRNA target prediction followed by target gene annotations. Finally, to extract the biological significance of the miRNAs in the samples, further annotations of the known miRNA and target genes were performed by collecting the public expression datasets of miRNA and target genes in normal and cancer tissues.We introduce a web-based analysis platform called miRNA Analysis System (miRAS), for processing and analyzing of the sequence data obtained from the total RNA clone method. The system was built on generalizing the study of a liver cancer cell line total RNA sequencing project. |
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Multi-omics tools |
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Multi-omics tools are a collection of tools for analyze multi-omics data. They are build on the architecture of python, perl and R shiny server. For example, using Differential Methylation Region Extractor (DMRE), users can upload the personalized data to extract differential methylation regions of interested. More are coming… |
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DL-PER |
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DL-PER is a Deep Learning Model for Chinese Prehospital Emergency Record Classification. Prehospital emergency records contain a large amount of information about prehospital emergency patients. Extracting important patient information from many records has become the focus of all prehospital emergency personnel. The key to solving this problem is to achieve the automatic classification of prehospital emergency records. In this study, we consider a deep learning-based pre-hospital emergency record classification model (DL-PER). The model uses a weighted text convolutional neural network to classify pre-hospital emergency records. First, we used prehospital emergency records to train a bidirectional encoder representation (BERT) model from a transformer and let BERT acquire contextual semantic information. Then, we used a bidirectional long and short-term memory (BiLSTM) model to obtain text features from a global perspective and improve the local text feature extraction capability of the model by a weighted text convolutional neural network (WTextCNN). We used activation functions instead of ReLu activation functions to improve the learning ability of the model. We conducted experiments using prehospital emergency records provided by the Handan Emergency Center. The results showed that the DL-PER model improved the F1 scores by up to 5.7%, 6.8%, 5.7%, and 4.9% on the four data sets, respectively, compared with the BiLSTM model. |
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