Dataset Information

MGMSN: Multi-Granularity Matching Model Based on Siamese Neural Network.

ABSTRACT: Aiming to overcome the shortcomings of the existing text matching algorithms, in this research, we have studied the related technologies of sentence matching and dialogue retrieval and proposed a multi-granularity matching model based on Siamese neural networks. This method considers both deep semantic similarity and shallow semantic similarity of input sentences to completely mine similar information between sentences. Moreover, to alleviate the problem of out of vocabulary in sentences, we have combined both word and character granularity in deep semantic similarity to further learn information. Finally, comparative experiments were carried out on the Chinese data set LCQMC. The experimental results confirm the effectiveness and generalization ability of this method, and several ablation experiments also show the importance of each part of the model.

SUBMITTER: Wang X

PROVIDER: S-EPMC8996057 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

MGMSN: Multi-Granularity Matching Model Based on Siamese Neural Network.

Wang Xin X Yang Huimin H

Frontiers in bioengineering and biotechnology 20220328

Aiming to overcome the shortcomings of the existing text matching algorithms, in this research, we have studied the related technologies of sentence matching and dialogue retrieval and proposed a multi-granularity matching model based on Siamese neural networks. This method considers both deep semantic similarity and shallow semantic similarity of input sentences to completely mine similar information between sentences. Moreover, to alleviate the problem of out of vocabulary in sentences, we hav ...[more]

PMID: 35419356

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Project description:MotivationGene regulatory networks (GRNs) arise from the intricate interactions between transcription factors (TFs) and their target genes during the growth and development of organisms. The inference of GRNs can unveil the underlying gene interactions in living systems and facilitate the investigation of the relationship between gene expression patterns and phenotypic traits. Although several machine-learning models have been proposed for inferring GRNs from single-cell RNA sequencing (scRNA-seq) data, some of these models, such as Boolean and tree-based networks, suffer from sensitivity to noise and may encounter difficulties in handling the high noise and dimensionality of actual scRNA-seq data, as well as the sparse nature of gene regulation relationships. Thus, inferring large-scale information from GRNs remains a formidable challenge.ResultsThis study proposes a multilevel, multi-structure framework called a pseudo-Siamese GRN (PSGRN) for inferring large-scale GRNs from time-series expression datasets. Based on the pseudo-Siamese network, we applied a gated recurrent unit to capture the time features of each TF and target matrix and learn the spatial features of the matrices after merging by applying the DenseNet framework. Finally, we applied a sigmoid function to evaluate interactions. We constructed two maize sub-datasets, including gene expression levels and GRNs, using existing open-source maize multi-omics data and compared them to other GRN inference methods, including GENIE3, GRNBoost2, nonlinear ordinary differential equations, CNNC, and DGRNS. Our results show that PSGRN outperforms state-of-the-art methods. This study proposed a new framework: a PSGRN that allows GRNs to be inferred from scRNA-seq data, elucidating the temporal and spatial features of TFs and their target genes. The results show the model's robustness and generalization, laying a theoretical foundation for maize genotype-phenotype associations with implications for breeding work.

Dataset Information

MGMSN: Multi-Granularity Matching Model Based on Siamese Neural Network.

Publications

MGMSN: Multi-Granularity Matching Model Based on Siamese Neural Network.

Similar Datasets

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