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-spec...
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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 recogniz...
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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.
Medical image denoising, as a part of medical image processing, is significant for the assessment and diagnosis of diseases. To improve the medical image denoising performance of existing deep learning methods, we pro...
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1 Introduction The process of complex diseases is closely linked to the disruption of key biological pathways,it is crucial to identify the dysfunctional pathways and quantify the degree of dysregulation at the indivi...
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1 Introduction The process of complex diseases is closely linked to the disruption of key biological pathways,it is crucial to identify the dysfunctional pathways and quantify the degree of dysregulation at the individual sample level[1].
During outbreaks of large infectious diseases like COVID-19, there is a strain on healthcare resources worldwide. To alleviate the burden on healthcare workers during the initial stages of the outbreak, there is an ur...
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International Classification of Diseases (ICD) coding is the task of assigning ICD diagnosis codes to clinical notes. This can be challenging given the large quantity of labels (nearly 9,000) and lengthy texts (up to ...
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Accurately diagnosing Alzheimer's disease is essential for improving elderly ***,accurate prediction of the mini-mental state examination score also can measure cognition impairment and track the progression of Al...
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Accurately diagnosing Alzheimer's disease is essential for improving elderly ***,accurate prediction of the mini-mental state examination score also can measure cognition impairment and track the progression of Alzheimer's ***,most of the existing methods perform Alzheimer's disease diagnosis and mini-mental state examination score prediction separately and ignore the relation between these two *** address this challenging problem,we propose a novel multi-task learning method,which uses feature interaction to explore the relationship between Alzheimer's disease diagnosis and minimental state examination score *** our proposed method,features from each task branch are firstly decoupled into candidate and non-candidate parts for ***,we propose feature sharing module to obtain shared features from candidate features and return shared features to task branches,which can promote the learning of each *** validate the effectiveness of our proposed method on multiple *** Alzheimer's disease neuroimaging initiative 1 dataset,the accuracy in diagnosis task and the root mean squared error in prediction task of our proposed method is 87.86%and 2.5,*** results show that our proposed method outperforms most state-of-the-art *** proposed method enables accurate Alzheimer's disease diagnosis and mini-mental state examination score ***,it can be used as a reference for the clinical diagnosis of Alzheimer's disease,and can also help doctors and patients track disease progression in a timely manner.
Electrocardiogram(ECG)is a low-cost,simple,fast,and non-invasive *** can reflect the heart’s electrical activity and provide valuable diagnostic clues about the health of the entire ***,ECG has been widely used in va...
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Electrocardiogram(ECG)is a low-cost,simple,fast,and non-invasive *** can reflect the heart’s electrical activity and provide valuable diagnostic clues about the health of the entire ***,ECG has been widely used in various biomedical applications such as arrhythmia detection,disease-specific detection,mortality prediction,and biometric *** recent years,ECG-related studies have been carried out using a variety of publicly available datasets,with many differences in the datasets used,data preprocessing methods,targeted challenges,and modeling and analysis *** we systematically summarize and analyze the ECGbased automatic analysis methods and ***,we first reviewed 22 commonly used ECG public datasets and provided an overview of data preprocessing *** we described some of the most widely used applications of ECG signals and analyzed the advanced methods involved in these ***,we elucidated some of the challenges in ECG analysis and provided suggestions for further research.
By generating massive gene transcriptome data and analyzing transcriptomic variations at the cell level, single-cell RNA-sequencing (scRNA-seq) technology has provided new way to explore cellular heterogeneity and fun...
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By generating massive gene transcriptome data and analyzing transcriptomic variations at the cell level, single-cell RNA-sequencing (scRNA-seq) technology has provided new way to explore cellular heterogeneity and functionality. Clustering scRNA-seq data could discover the hidden diversity and complexity of cell populations, which can aid to the identification of the disease mechanisms and biomarkers. In this paper, a novel method (DSINMF) is presented for single cell RNA sequencing data by using deep matrix factorization. Our proposed method comprises four steps: first, the feature selection is utilized to remove irrelevant features. Then, the dropout imputation is used to handle missing value problem. Further, the dimension reduction is employed to preserve data characteristics and reduce noise effects. Finally, the deep matrix factorization with bi-stochastic graph regularization is used to obtain cluster results from scRNA-seq data. We compare DSINMF with other state-of-the-art algorithms on nine datasets and the results show our method outperformances than other methods. IEEE
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 ge...
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