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State-sensitive convolutional sparse coding for potential biomarker identification in brain signals

Science China(Information Sciences) 2024年第5期67卷 270-286页

作者： Puli WANG Yu QI Gang PAN College of Computer Science and Technology Zhejiang University State Key Laboratory of Brain-Machine Intelligence Zhejiang University MOE Frontier Science Center for Brain Science and Brain-machine Integration Zhejiang University Affiliated Mental Health Center & Hangzhou Seventh People's Hospital Zhejiang University School of Medicine

The identification of prototypical waveforms, such as sleep spindles and epileptic spikes, is crucial for the diagnosis of neurological disorders. These prototypical waveforms are usually recurrently presented in certain brain states, serving as potential biomarkers for clinical evaluations. Convolutional sparse coding(CSC) approaches have demonstrated strength in identifying recurrent patterns in time-series. However,existing CSC approaches do not explicitly explore state-specific patterns, making it difficult to identify state-related biomarkers. To address this problem, we propose state-sensitive CSC to learn state-specific prototypical waveforms. Specifically, we model signals of a certain state with specific waveforms that only appear frequently in this state and background waveforms that are independent of states. Based on this,state-sensitive CSC separates state-specific waveforms from background ones explicitly by incorporating incoherence constraints into optimizations. Experiments with epilepsy brain signals demonstrate that our approach can effectively identify prototypical waveforms in pre-ictal states, providing potential biomarkers for seizure prediction. Our approach provides a promising tool for automatic biomarker candidate identification.

关键词： convolutional sparse coding prototypical waveforms state-specific dictionary biomarker identification neural signal processing

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Sparse discriminant analysis for breast cancer biomarker identification and classification

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Progress in Natural Science:Materials International 2009年第11期19卷 1635-1641页

作者： Yu Shi~(a,b),Daoqing Dai~(a,*),Chaochun Liu~(a,b),Hong Yan~(b,c) a Center for Computer Vision and Department of Mathematics,Sun Yat-Sen(Zhongshan) University,Guangzhou 510275,China b Department of Electric Engineering,City University of Hong Kong,83 Tat Chee Avenue,Kowloon,Hong Kong,China c School of Electrical and Information Engineering,University of Sydney,NSW 2006,Australia Center for Computer Vision and Department of Mathematics Sun Yat-Sen (Zhongshan) University Guangzhou 510275 China Department of Electric Engineering City University of Hong Kong 83 Tat Chee Avenue Kowloon Hong Kong China School of Electrical and Information Engineering University of Sydney NSW 2006 Australia

biomarker identification and cancer classification are two important procedures in microarray data *** propose a novel unified method to carry out both *** first preselect biomarker candidates by eliminating unrelated genes through the BSS/WSS ratio filter to reduce computational cost,and then use a sparse discriminant analysis method for simultaneous biomarker identification and cancer ***,we give a mathematical justification about automatic biomarker *** results show that the proposed method can identify key genes that have been verified in biochemical or biomedical research and classify the breast cancer type correctly.

关键词： biomarker identification Cancer classification Discriminant analysis Maximum penalized likelihood Microarray data analysis

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biomarkers for pancreatic cancer:Recent achievements in proteomics and genomics through classical and multivariate statistical methods

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World Journal of Gastroenterology 2014年第37期20卷 13325-13342页

作者： Emilio Marengo Elisa Robotti Department of Sciences and Technological Innovation University of Piemonte Orientale

Pancreatic cancer(PC) is one of the most aggressive and lethal neoplastic diseases. A valid alternative to the usual invasive diagnostic tools would certainly be the determination of biomarkers in peripheral fluids to provide less invasive tools for early diagnosis. Nowadays, biomarkers are generally investigated mainly in peripheral blood and tissues through high-throughput omics techniques comparing control vs pathological samples. The results can be evaluated by two main strategies:(1) classical methods in which the identification of significant biomarkers is accomplished by monovariate statistical tests where each biomarker is considered as independent from the others; and(2) multivariate methods, taking into consideration the correlations existing among the biomarkers themselves. This last approach is very powerful since it allows the identification of pools of biomarkers with diagnostic and prognostic performances which are superior to single markers in terms of sensitivity, specificity and robustness. Multivariate techniques are usually applied with variable selection procedures to provide a restricted set of biomarkers with the best predictive ability; however, standard selection methods are usually aimed at the identification of the smallest set of variables with the best predictive ability and exhaustivity is usually neglected. The exhaustive search for biomarkers is instead an important alternative to standard variable selection since it can provide information about the etiology of the pathology by producing a comprehensive set of markers. In this review, the most recent applications of the omics techniques(proteomics, genomics and metabolomics) to the identification of exploratory biomarkers for PC will be presented with particular regard to the statistical methods adopted for their identification. The basic theory related to classical and multivariate methods for identification of biomarkers is presented and then, the most recent applications in this f

关键词： Pancreatic cancer biomarker identification Multivariate analysis Principal component analysis Ranking principal component analysis

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