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Genome-wide epigenomic datasets allow us to validate the biological function of motifs and understand the regulatory mechanisms more comprehensively. How different motifs determine whether transcription factors (TFs) can bind to DNA at a specific position is a critical research question. In this project, we apply computational techniques that were used in Natural Language Processing (NLP) to predict the Transcription Factor Bound Regions (TFBRs) given motif instances. Most existing motif prediction methods using deep neural network apply base sequences with one-hot encoding as an input feature to realize TFBRs identification, contributing to low-resolution and indirect binding mechanisms. However, how the collective effect of motifs on binding sites is complicated to figure out. In our pipeline, we apply Word2Vec algorithm, with names of motifs as an input to predict TFBRs utilizing Convolutional Neural Network (CNN) to realize binary classification, based on the ENCODE dataset. In this regard, we consider different types of motifs as separate “words”, and their corresponding TFBR as the meanings of “sentences”. One “sentence” itself is merely the combination of these motifs, and all “sentences” compose of the whole “passage”. For each binding site, we do the binary classification within different cell types to show the performance of our model in different binding sites and cell types. Each “word” has a corresponding vector in high dimensions, and the distances between each vector can be figured out, so we can extract the similarity between each motif, and the explicit binding mechanism from our model. We apply Convolutional Neural Network (CNN) to extract features in the process of mapping and pooling from motif vectors extracted by Word2Vec Algorithm and gain the result of 87% accuracy at the peak.

With the rapid development of computer technology, Internet technology and artificial intelligence technology, the amount of global data has exploded. However, the single-machine serial mode of traditional data mining cannot be directly transplanted to the cloud platform. Only by parallelizing and improving many classic data mining algorithms can the cloud computing platform and data mining be effectively combined. Therefore, it is of great significance to the research and implementation of parallel algorithm technology for time series data mining. The purpose of this paper is to study the research and implementation of parallel algorithm technology for time series data mining. This paper adopts the method of literature data, mathematical statistics, logic analysis and other research methods to study the parallel algorithm technology research and realization of time series data mining, mainly to make useful explorations of time series data mining and visualization technology. It embodies the design ideas of big data analysis tools, and finally reflects the power and market value of data analysis tools through the display of the platform. Research shows that running in the same data set and the same experimental environment, the improved parallel collaborative filtering algorithm ACF in this paper has higher time running efficiency than the parallel algorithm MCF based on the cooccurrence matrix, and in the case of larger data sets, the more obvious the time difference.

Locality-constrained Linear Coding (LLC) shows superior image classification performance due to its underlying properties of local smooth sparsity and good construction. It encodes the visual features in remote sensing images and realizes the process of modeling human visual perception of an image through a computer. However, it ignores the consideration of saliency preprocessing in the human visual system. Saliency detection preprocessing can effectively enhance a computer&rsquo, s perception of remote sensing images. To better implement the task of remote sensing image scene classification, this paper proposes a new approach by combining saliency detection preprocessing and LLC. This saliency detection preprocessing approach is realized using spatial pyramid Gaussian kernel density estimation. Experiments show that the proposed method achieved a better performance for remote sensing scene classification tasks.