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DeepRetention:A Deep Learning Approach for Intron Retention Detection

Big Data Mining and Analytics 2023年第2期6卷 115-126页

作者： Zhenpeng Wu Jiantao Zheng Jiashu Liu Cuixiang Lin Hong-Dong Li Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China

As the least understood mode of alternative splicing,Intron Retention(IR)is emerging as an interesting area and has attracted more and more attention in the field of gene regulation and disease *** methods detect IR exclusively based on one or a few predefined metrics describing local or summarized characteristics of retained *** metrics are not able to describe the pattern of sequencing depth of intronic reads,which is an intuitive and informative characteristic of retained *** hypothesize that incorporating the distribution pattern of intronic reads will improve the accuracy of IR *** we present DeepRetention,a novel approach for IR detection by modeling the pattern of sequencing depth of *** to the lack of a gold standard dataset of IR,we first compare DeepRetention with two state-of-the-art methods,*** and IRFinder,on simulated RNA-seq datasets with retained *** results show that DeepRetention outperforms these two ***,DeepRetention performs well when it is applied to third-generation long-read RNA-seq data,while IRFinder and iREAD are not applicable to detecting IR from the third-generation sequencing ***,we show that IRs predicted by DeepRetention are biologically meaningful on an RNA-seq dataset from Alzheimer’s Disease(AD)*** differential IRs are found to be significantly associated with AD based on statistical evaluation of an AD-specific functional gene *** parent genes of differential IRs are enriched in AD-related *** summary,DeepRetention detects IR from a new angle of view,providing a valuable tool for IR analysis.

关键词： Alternative Splicing(AS) Intron Retention(IR) intronic reads distribution pattern RNA-seq

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A Comparison of Computational Approaches for Intron Retention Detection

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Big Data Mining and Analytics 2022年第1期5卷 15-31页

作者： Jiantao Zheng Cuixiang Lin Zhenpeng Wu Hong-Dong Li the Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China

Intron Retention(IR)is an alternative splicing mode through which introns are retained in mature RNAs rather than being spliced in most *** has been gaining increasing attention in recent years because of its recognized association with gene expression regulation and complex *** efforts have been dedicated to the development of IR detection *** methods differ in their metrics to quantify retention propensity,performance to detect IR events,functional enrichment of detected IRs,and computational speed.A systematic experimental comparison would be valuable to the selection and use of existing *** this work,we conduct an experimental comparison of existing IR detection *** the unavailability of a gold standard dataset of intron retention,we compare the IR detection performance on simulation ***,we compare the IR detection results with real RNA-Seq *** also describe the use of differential analysis methods to identify disease-associated IRs and compare differential IRs along with their Gene Ontology enrichment,which is illustrated on an Alzheimer’s disease RNA-Seq *** discuss key principles and features of existing approaches and outline their *** systematic analysis provides helpful guidance for interrogating transcriptomic data from the point of view of IR.

关键词： alternative splicing intron retention gene expression RNA-Seq

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Hippocampal Segmentation in Brain MRI Images Using Machine Learning Methods: A Survey

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Chinese Journal of Electronics 2021年第5期30卷 793-814页

作者： PAN Yi LIU Jin TIAN Xu LAN Wei GUO Rui Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South University School of Computer Electronics and Information Guangxi University

The hippocampus is closely related to many brain diseases, such as Alzheimer's disease. Accurate measurement of the hippocampus is helpful for clinicians in identifying lesions and then diagnosing and treating the related brain diseases. Therefore, accurate segmentation of the hippocampus is of vital significance for the indepth study of many brain diseases. However, the accurate measurement of the hippocampus depends on its accurate segmentation, and hippocampal segmentation has always been a challenging problem due to the small size,irregular shape, and fuzzy boundaries with surrounding tissues of the hippocampus. With the development of machine learning, many innovative methods have been proposed to segment the hippocampus. The purpose of this survey is to provide a comprehensive overview of hippocampal segmentation in brain MRI images using machine learning methods. First, a brief introduction to hippocampal segmentation in brain MRI images is given. Then, common evaluation metrics of hippocampal segmentation are introduced. Next, brain hippocampal segmentation methods based on traditional machine learning and deep learning are described. Subsequently,some common open datasets and toolkits applied to brain hippocampal segmentation are presented. Finally,objective conclusions regarding hippocampal segmentation in brain MRI images using machine learning methods are drawn, and future developments and trends are identified for brain hippocampal segmentation.

关键词： biomedical MRI brain image segmentation machine learning methods brain hippocampal segmentation methods medical image processing neurophysiology related brain diseases diseases learning (artificial intelligence) hippocampus brain MRI images

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Fusion Model for Tentative Diagnosis Inference Based on Clinical Narratives

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Tsinghua science and Technology 2023年第4期28卷 686-695页

作者： Ying Yu Junwen Duan Min Li School of Computer Science and Engineering Central South UniversityChangsha 410083Chinaand also with the School of Computer ScienceUniversity of South ChinaHengyang 421001China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China

In general,physicians make a preliminary diagnosis based on patients’admission narratives and admission conditions,largely depending on their experiences and professional *** automatic and accurate tentative diagnosis based on clinical narratives would be of great importance to physicians,particularly in the shortage of medical *** its great value,little work has been conducted on this diagnosis ***,in this study,we propose a fusion model that integrates the semantic and symptom features contained in the clinical *** semantic features of the input text are initially captured by an attention-based Bidirectional Long Short-Term Memory(BiLSTM)*** symptom concepts,recognized from the input text,are then vectorized by using the term frequency-inverse document frequency method based on the relations between symptoms and ***,two fusion strategies are utilized to recommend the most potential candidate for the international classification of diseases *** training and evaluation are performed on a public clinical *** results show that both fusion strategies achieved a promising performance,in which the best performance obtained a top-3 accuracy of 0.7412.

关键词： tentative diagnosis clinical narrative Bidirectional Long Short-Term Memory(BiLSTM) Term FrequencyInverse Document Frequency(TF-IDF) fusion strategy

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A Data-Driven Clustering Recommendation Method for Single-Cell RNA-Sequencing Data

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Tsinghua science and Technology 2021年第5期26卷 772-789页

作者： Yu Tian Ruiqing Zheng Zhenlan Liang Suning Li Fang-Xiang Wu Min Li the Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityHunan 410083China the Division of Biomedical Engineering and Department of Mechanical Engineering University of SaskatchewanSKS7N5A9Canada

Recently,the emergence of single-cell RNA-sequencing(scRNA-seq)technology makes it possible to solve biological problems at the single-cell *** of the critical steps in cellular heterogeneity analysis is the cell type *** scRNA-seq clustering methods have been proposed to partition cells into *** all the methods,hierarchical clustering and spectral clustering are the most popular approaches in the downstream clustering analysis with different preprocessing strategies such as similarity learning,dropout imputation,and dimensionality *** this study,we carry out a comprehensive analysis by combining different strategies with these two categories of clustering methods on scRNA-seq datasets under different biological *** analysis results show that the methods with spectral clustering tend to perform better on datasets with continuous shapes in two-dimension,while those with hierarchical clustering achieve better results on datasets with obvious boundaries between clusters in *** by this finding,a new strategy,called QRS,is developed to quantitatively evaluate the latent representative shape of a dataset to distinguish whether it has clear boundaries or ***,a data-driven clustering recommendation method,called DDCR,is proposed to recommend hierarchical clustering or spectral clustering for scRNA-seq *** perform DDCR on two typical single cell clustering methods,SC3 and RAFSIL,and the results show that DDCR recommends a more suitable downstream clustering method for different scRNA-seq datasets and obtains more robust and accurate results.

关键词： single-cel RNA-sequencing(sc RNA-seq) cel ular heterogeneity cel type identification data latent shape clustering

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FBC: Fusing Bi-Encoder and Cross-Encoder for Long-Form Text Matching 26

FBC: Fusing Bi-Encoder and Cross-Encoder for Long-Form Text ...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Liao, Jianbo Jia, Mingyi Duan, Junwen Wang, Jianxin Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University China

ISBN: (纸本)9781643684369

Semantic text matching has a wide range of applications in natural language processing. Recently proposed models that have achieved excellent results on short text matching tasks are not well suited to long-form text matching problems due to input length limitations and increased noise. On the other hand, long-form texts contain a large amount of information at different granularities after encoding, which cannot be fully interacted and utilized by existing methods. To address above issues, we propose a novel long-form text-matching framework which fuses Bi-Encoder and Cross-Encoder (FBC). Specially, it first employs an entity-driven key sentence extraction method to obtain the crucial content of the text and filter out noise. Subsequently, it integrates Bi-Encoder and Cross-Encoder to better capture semantic features and matching signals. Extensive experiments on several publicly available datasets demonstrate the effectiveness of our approach, compared with strong baselines. Furthermore, our model exhibits greater stability and accuracy in determining the matching relationship between documents describing the same event, which outperforms previously established approaches. The code is released at https://***/CSU-NLP-Group/FBC. © 2023 The Authors.

关键词： Semantics

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KL-RF: Predicting disease-gene associations with model fusion

KL-RF: Predicting disease-gene associations with model fusio...

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2022 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2022

作者： Liu, Hai Li, Hong-Dong Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Hunan ChangSha China

ISBN: (纸本)9781665468190

Appropriate disease gene prediction methods are not only critical for the diagnosis of many genotypic diseases, but well-positioned to help scientists discover the links between complex diseases and disease-causing genes. As the development of high-throughput biotechnology, a great quantity of biomolecular interaction networks and omics data emerge rapidly. Because of the capacity of biomolecular networks to indicate complex interactions between elements in disease, they are one of the most frequently used data in the field of disease-gene associations prediction. Fusing these biomolecular networks effectively is hopeful to improve the performance of computational methods. Here, we develop a method to predict the associations between disease and gene by fusing the information of heterogeneous datasets. This method first learns the latent feature representation from disease-gene similarity networks via graph representation learning methods, then utilizes a Beyesian approach to obtain the posterior distribution of the feature matrix, and subsequently fuses the posterior distribution based on KL divergence and predicts the associations by random forest (KL-RF) finally. We compared KL-RF with several classic methods, and the results demonstrate that it performs better than the existing methods. Further analysis of the top-10 unknown disease genes and case study also reveals the ability of the proposed method. The source codes is available on https://***/genemine/KL-RF. © 2022 IEEE.

关键词： Association reactions

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Collaborative Matrix Factorization with Soft Regularization for Drug-Target Interaction Prediction

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Journal of computer science & Technology 2021年第2期36卷 310-322页

作者： Li-Gang Gao Meng-Yun Yang Jian-Xin Wang School of Computer Science and Engineering Central South UniversityChangsha 410083China Hunan Provincial Key Laboratory of Bioinformatics Central South UniversityChangsha 410083China School of Science Shaoyang UniversityShaoyang 422000China

Identifying the potential drug-target interactions(DTI)is critical in drug *** drug-target interaction prediction methods based on collaborative filtering have demonstrated attractive prediction ***,many corresponding models cannot accurately express the relationship between similarity features and DTI *** order to rationally represent the correlation,we propose a novel matrix factorization method,so-called collaborative matrix factorization with soft regularization(SRCMF).SRCMF improves the prediction performance by combining the drug and the target similarity information with matrix *** contrast to general collaborative matrix factorization,the fundamental idea of SRCMF is to make the similarity features and the potential features of DTI approximate,not ***,SRCMF obtains low-rank feature representations of drug similarity and target similarity,and then uses a soft regularization term to constrain the approximation between drug(target)similarity features and drug(target)potential features of *** comprehensively evaluate the prediction performance of SRCMF,we conduct cross-validation experiments under three different *** terms of the area under the precision-recall curve(AUPR),SRCMF achieves better prediction results than six state-of-the-art ***,under different noise levels of similarity data,the prediction performance of SRCMF is much better than that of collaborative matrix *** conclusion,SRCMF is robust leading to performance improvement in drug-target interaction prediction.

关键词： drug-target interaction collaborative matrix factorization soft regularization noisy data

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Domain-specific Knowledge Guided Self-supervised Learning for Pathological Image Segmentation

Domain-specific Knowledge Guided Self-supervised Learning fo...

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2023 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2023

作者： Wang, Chenhui Kuang, Hulin Liu, Jin Li, Junjian Yue, Hailin Wang, Jianxin Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Hunan Changsha China

ISBN: (纸本)9798350337488

Self-supervised learning provides a possible solution to extract effective visual representations from unlabeled pathological images. However, most of the existing methods either do not effectively utilize domain-specific information or are designed and optimized for image classification, resulting in these pre-trained models that may not be optimal for pathological image segmentation. In this paper, we propose DKSL: Domain-specific Knowledge guided Self-supervised Learning, which uses image reconstruction tasks to aid contrastive learning and exploits single-dye stained pathological images after stain separation as domain-specific knowledge to guide the model. Our method provides a novel way to exploit the domain-specific knowledge of pathological images. In contrastive learning, we add single-dye stained images as an expansion of the original positive samples to the contrastive learning process to preserve more global semantic information. In image reconstruction, the model is forced to focus on local image details relevant to downstream tasks by reconstructing single-dye stained images from the representation extracted by the encoder of contrastive learning. Finally, the encoder and decoder from the pre-training stage are fine-tuned by the downstream segmentation task. Fine-tuning experimental results demonstrate that DKSL outperforms state-of-the-art methods with Dices of 90.50% and 79.68% on two publicly available datasets, GlaS and MoNuSeg, respectively. © 2023 IEEE.

关键词： Contrastive learning Domain-specific knowledge Image reconstruction Pathological Image Segmentation

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BEA-SegNet: Body and Edge Aware Network for Medical Image Segmentation

BEA-SegNet: Body and Edge Aware Network for Medical Image Se...

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2021 IEEE International Conference on bioinformatics and Biomedicine, BIBM 2021

作者： Kuang, Hulin Liang, Yixiong Liu, Ning Liu, Jin Wang, Jianxin Central South University Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Hunan Changsha China

ISBN: (纸本)9781665401265

Medical image segmentation is a fundamental step for diagnosis and prognosis. This study proposes a new body and edge aware network for automated 2D medical image segmentation (called BEA-SegNet). The proposed BEA-SegNet consists of a shared encoder, a body and edge decouple (BEdecouple) module, two parallel decoders for body and edge segmentation. In the encoder and decoders, short-term multi-scale concatenation (STMSC) modules are utilized to implement multi-scale representation. We design a BEdecouple module to decouple the convolutional features into the body and edge features, making the proposed method be body and edge aware. The body and edge decoders utilize Bedecouple modules in each level to learn more effective features for the body and edge segmentation respectively, and their outputs are fused to generate the final segmentation. Besides, the body and edge supervision are applied to improve the final segmentation. The proposed BEA-SegNet is trained and evaluated on the International Skin Imaging Collaboration challenge 2018 dataset (ISIC2018). Experimental results show that the proposed BEA-SegNet achieves an average Dice similarity coefficient of 90.3% and an average Hausdorff distance of 15.9 for the skin lesion segmentation task and outperforms five benchmarks for skin lesion segmentation. © 2021 IEEE.

关键词： Convolutional neural networks

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