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  1. Article ; Online: How Deepbics Quantifies Intensities of Transcription Factor-DNA Binding and Facilitates Prediction of Single Nucleotide Variant Pathogenicity With a Deep Learning Model Trained On ChIP-Seq Data Sets.

    Quan, Lijun / Chu, Xiaomin / Sun, Xiaoyu / Wu, Tingfang / Lyu, Qiang

    IEEE/ACM transactions on computational biology and bioinformatics

    2023  Volume 20, Issue 2, Page(s) 1594–1599

    Abstract: The binding of DNA sequences to cell type-specific transcription factors is essential for regulating gene expression in all organisms. Many variants occurring in these binding regions play crucial roles in human disease by disrupting the cis-regulation ... ...

    Abstract The binding of DNA sequences to cell type-specific transcription factors is essential for regulating gene expression in all organisms. Many variants occurring in these binding regions play crucial roles in human disease by disrupting the cis-regulation of gene expression. We first implemented a sequence-based deep learning model called deepBICS to quantify the intensity of transcription factors-DNA binding. The experimental results not only showed the superiority of deepBICS on ChIP-seq data sets but also suggested deepBICS as a language model could help the classification of disease-related and neutral variants. We then built a language model-based method called deepBICS4SNV to predict the pathogenicity of single nucleotide variants. The good performance of deepBICS4SNV on 2 tests related to Mendelian disorders and viral diseases shows the sequence contextual information derived from language models can improve prediction accuracy and generalization capability.
    MeSH term(s) Humans ; Chromatin Immunoprecipitation Sequencing ; Deep Learning ; Virulence ; Binding Sites/genetics ; DNA/genetics ; Transcription Factors/genetics ; Transcription Factors/metabolism ; Nucleotides
    Chemical Substances DNA (9007-49-2) ; Transcription Factors ; Nucleotides
    Language English
    Publishing date 2023-04-03
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1557-9964
    ISSN (online) 1557-9964
    DOI 10.1109/TCBB.2022.3170343
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: SemanticCAP: Chromatin Accessibility Prediction Enhanced by Features Learning from a Language Model.

    Zhang, Yikang / Chu, Xiaomin / Jiang, Yelu / Wu, Hongjie / Quan, Lijun

    Genes

    2022  Volume 13, Issue 4

    Abstract: A large number of inorganic and organic compounds are able to bind DNA and form complexes, among which drug-related molecules are important. Chromatin accessibility changes not only directly affect drug-DNA interactions, but they can promote or inhibit ... ...

    Abstract A large number of inorganic and organic compounds are able to bind DNA and form complexes, among which drug-related molecules are important. Chromatin accessibility changes not only directly affect drug-DNA interactions, but they can promote or inhibit the expression of the critical genes associated with drug resistance by affecting the DNA binding capacity of TFs and transcriptional regulators. However, the biological experimental techniques for measuring it are expensive and time-consuming. In recent years, several kinds of computational methods have been proposed to identify accessible regions of the genome. Existing computational models mostly ignore the contextual information provided by the bases in gene sequences. To address these issues, we proposed a new solution called SemanticCAP. It introduces a gene language model that models the context of gene sequences and is thus able to provide an effective representation of a certain site in a gene sequence. Basically, we merged the features provided by the gene language model into our chromatin accessibility model. During the process, we designed methods called SFA and SFC to make feature fusion smoother. Compared to DeepSEA, gkm-SVM, and k-mer using public benchmarks, our model proved to have better performance, showing a 1.25% maximum improvement in auROC and a 2.41% maximum improvement in auPRC.
    MeSH term(s) Chromatin/genetics ; Chromatin Immunoprecipitation ; DNA/genetics ; Language ; Transcription Factors/genetics
    Chemical Substances Chromatin ; Transcription Factors ; DNA (9007-49-2)
    Language English
    Publishing date 2022-03-23
    Publishing country Switzerland
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2527218-4
    ISSN 2073-4425 ; 2073-4425
    ISSN (online) 2073-4425
    ISSN 2073-4425
    DOI 10.3390/genes13040568
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Book ; Online: Topic Shift Detection in Chinese Dialogues

    Lin, Jiangyi / Fan, Yaxin / Jiang, Feng / Chu, Xiaomin / Li, Peifeng

    Corpus and Benchmark

    2023  

    Abstract: Dialogue topic shift detection is to detect whether an ongoing topic has shifted or should shift in a dialogue, which can be divided into two categories, i.e., response-known task and response-unknown task. Currently, only a few investigated the latter, ... ...

    Abstract Dialogue topic shift detection is to detect whether an ongoing topic has shifted or should shift in a dialogue, which can be divided into two categories, i.e., response-known task and response-unknown task. Currently, only a few investigated the latter, because it is still a challenge to predict the topic shift without the response information. In this paper, we first annotate a Chinese Natural Topic Dialogue (CNTD) corpus consisting of 1308 dialogues to fill the gap in the Chinese natural conversation topic corpus. And then we focus on the response-unknown task and propose a teacher-student framework based on hierarchical contrastive learning to predict the topic shift without the response. Specifically, the response at high-level teacher-student is introduced to build the contrastive learning between the response and the context, while the label contrastive learning is constructed at low-level student. The experimental results on our Chinese CNTD and English TIAGE show the effectiveness of our proposed model.
    Keywords Computer Science - Computation and Language ; Computer Science - Machine Learning
    Publishing date 2023-05-02
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: Multi-Granularity Prompts for Topic Shift Detection in Dialogue

    Lin, Jiangyi / Fan, Yaxin / Chu, Xiaomin / Li, Peifeng / Zhu, Qiaoming

    2023  

    Abstract: The goal of dialogue topic shift detection is to identify whether the current topic in a conversation has changed or needs to change. Previous work focused on detecting topic shifts using pre-trained models to encode the utterance, failing to delve into ... ...

    Abstract The goal of dialogue topic shift detection is to identify whether the current topic in a conversation has changed or needs to change. Previous work focused on detecting topic shifts using pre-trained models to encode the utterance, failing to delve into the various levels of topic granularity in the dialogue and understand dialogue contents. To address the above issues, we take a prompt-based approach to fully extract topic information from dialogues at multiple-granularity, i.e., label, turn, and topic. Experimental results on our annotated Chinese Natural Topic Dialogue dataset CNTD and the publicly available English TIAGE dataset show that the proposed model outperforms the baselines. Further experiments show that the information extracted at different levels of granularity effectively helps the model comprehend the conversation topics.
    Keywords Computer Science - Computation and Language
    Publishing date 2023-05-23
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: SemanticCAP

    Zhang, Yikang / Chu, Xiaomin / Jiang, Yelu / Wu, Hongjie / Quan, Lijun

    Chromatin Accessibility Prediction Enhanced by Features Learning from a Language Model

    2022  

    Abstract: A large number of inorganic and organic compounds are able to bind DNA and form complexes, among which drug-related molecules are important. Chromatin accessibility changes not only directly affects drug-DNA interactions, but also promote or inhibit the ... ...

    Abstract A large number of inorganic and organic compounds are able to bind DNA and form complexes, among which drug-related molecules are important. Chromatin accessibility changes not only directly affects drug-DNA interactions, but also promote or inhibit the expression of critical genes associated with drug resistance by affecting the DNA binding capacity of TFs and transcriptional regulators. However, Biological experimental techniques for measuring it are expensive and time consuming. In recent years, several kinds of computational methods have been proposed to identify accessible regions of the genome. Existing computational models mostly ignore the contextual information of bases in gene sequences. To address these issues, we proposed a new solution named SemanticCAP. It introduces a gene language model which models the context of gene sequences, thus being able to provide an effective representation of a certain site in gene sequences. Basically, we merge the features provided by the gene language model into our chromatin accessibility model. During the process, we designed some methods to make feature fusion smoother. Compared with other systems under public benchmarks, our model proved to have better performance.
    Keywords Quantitative Biology - Genomics ; Computer Science - Machine Learning
    Subject code 612
    Publishing date 2022-04-05
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

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  6. Book ; Online: Advancing Topic Segmentation and Outline Generation in Chinese Texts

    Jiang, Feng / Liu, Weihao / Chu, Xiaomin / Li, Peifeng / Zhu, Qiaoming / Li, Haizhou

    The Paragraph-level Topic Representation, Corpus, and Benchmark

    2023  

    Abstract: Topic segmentation and outline generation strive to divide a document into coherent topic sections and generate corresponding subheadings. Such a process unveils the discourse topic structure of a document that benefits quickly grasping and understanding ...

    Abstract Topic segmentation and outline generation strive to divide a document into coherent topic sections and generate corresponding subheadings. Such a process unveils the discourse topic structure of a document that benefits quickly grasping and understanding the overall context of the document from a higher level. However, research and applications in this field have been restrained due to the lack of proper paragraph-level topic representations and large-scale, high-quality corpora in Chinese compared to the success achieved in English. Addressing these issues, we introduce a hierarchical paragraph-level topic structure representation with title, subheading, and paragraph that comprehensively models the document discourse topic structure. In addition, we ensure a more holistic representation of topic distribution within the document by using sentences instead of keywords to represent sub-topics. Following this representation, we construct the largest Chinese Paragraph-level Topic Structure corpus (CPTS), four times larger than the previously largest one. We also employ a two-stage man-machine collaborative annotation method to ensure the high quality of the corpus both in form and semantics. Finally, we validate the computability of CPTS on two fundamental tasks (topic segmentation and outline generation) by several strong baselines, and its efficacy has been preliminarily confirmed on the downstream task: discourse parsing. The representation, corpus, and benchmark we established will provide a solid foundation for future studies.
    Keywords Computer Science - Computation and Language ; Computer Science - Artificial Intelligence
    Subject code 410
    Publishing date 2023-05-24
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

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