2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718)最新文献

Computational decomposition of molecular signatures based on blind source separation of non-negative dependent sources with NMF 基于NMF的非负相关源盲分离的分子特征计算分解

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718) Pub Date : 2004-08-02 DOI: 10.1109/NNSP.2003.1318040

Junying Zhang, Le Wei, Y. Wang

{"title":"Computational decomposition of molecular signatures based on blind source separation of non-negative dependent sources with NMF","authors":"Junying Zhang, Le Wei, Y. Wang","doi":"10.1109/NNSP.2003.1318040","DOIUrl":"https://doi.org/10.1109/NNSP.2003.1318040","url":null,"abstract":"As a common feature in microarray profiling, gene expression profiles represent a composite of more than one distinct sources, which can severely decrease the sensitivity and specificity for the measurement of molecular signatures associated with different disease processes. Independent component analysis (ICA) is a widely applicable approach in blind source separation (BSS) but with limitations that the sources are independent, while a more common situation, which still happens in microarray profiles, is BSS where sources are not statistically independent. A novel idea of BSS is presented: it is a matrix factorization problem without enforcement of statistical characteristics on sources, while blind independent source separation is in fact matrix factorization, to factorize the observation matrix into a mixing matrix and a source matrix where the sources are independent. Since non-negative sources are meaningful in many applications including microarray profiling, we presented that blind non-negative source separation is essentially a matrix factorization, to factorize the observation matrix into a non-negative mixing matrix and a non-negative source matrix where the sources may be dependent. Non-negative matrix factorization (NMF) technique is applied to this non-negative source separation and is proven by a large number of computer simulations and by partial volume correction (PVC) experiments for real microarray data that it is effective when the sources are dependent with each other and/or are Gaussian distributed.","PeriodicalId":315958,"journal":{"name":"2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127447990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Fast error whitening algorithms for system identification and control 用于系统识别和控制的快速错误白化算法

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718) Pub Date : 2003-09-17 DOI: 10.1109/NNSP.2003.1318030

Y. Rao, Deniz Erdoğmuş, G. Y. Rao, J. Príncipe

引用次数: 6

Neuro-variational inversion of ocean color imagery 海洋色彩图像的神经变分反演

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718) Pub Date : 2003-09-17 DOI: 10.1109/NNSP.2003.1318009

C. Jamet, S. Thiria, C. Moulin, M. Crépon

引用次数: 0

Iterated extended Kalman smoothing with expectation-propagation 迭代扩展卡尔曼平滑与期望传播

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718) Pub Date : 2003-09-17 DOI: 10.1109/NNSP.2003.1318021

A. Ypma, T. Heskes

引用次数: 4

Bayesian processing of microarray images 微阵列图像的贝叶斯处理

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718) Pub Date : 2003-09-17 DOI: 10.1109/NNSP.2003.1318005

Neil D. Lawrence, M. Milo, M. Niranjan, P. Rashbass, S. Soullier

{"title":"Bayesian processing of microarray images","authors":"Neil D. Lawrence, M. Milo, M. Niranjan, P. Rashbass, S. Soullier","doi":"10.1109/NNSP.2003.1318005","DOIUrl":"https://doi.org/10.1109/NNSP.2003.1318005","url":null,"abstract":"Gene expression measurements quantify the level of mRNA produced from each gene. Two principal methods exist for producing slides for extracting these levels: photolithography and spotted arrays. One difficulty with the spotted array format is determining the size and location of the spots on the array. In this paper we present a Bayesian approach to processing images produced by these arrays that seeks posterior distributions over the size and positions of the spots. This enables us to estimate expression ratios and their variances. Exact inference for the model we specify is intractable; we develop an approximate inference technique, which combines importance sampling with variational inference. Our technique has already been shown to be more consistent than both manual processing and another automated technique [N. D. Lawrence, et al., \"Reducing the Variability in cDNA Microarray Image Processing by Inference\"]. Here we present large-scale results for twenty-four microarray slides each representing 5760 genes and show the dramatic effects of incorporating variance in our downstream analysis. Software based on this algorithm is available for academic use.","PeriodicalId":315958,"journal":{"name":"2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718)","volume":"291 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124182227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Blind equalization using matrix momentum and natural gradient adaptation 利用矩阵动量和自然梯度自适应进行盲均衡

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718) Pub Date : 2003-09-17 DOI: 10.1109/NNSP.2003.1318043

G. Morison, T. Durrani

引用次数: 0

Partially-independent component analysis for tissue heterogeneity correction in microarray gene expression analysis 微阵列基因表达分析中组织异质性校正的部分独立成分分析

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718) Pub Date : 2003-09-17 DOI: 10.1109/NNSP.2003.1318001

Y. Wang, Junying Zhang, Javed I. Khan, R. Clarke, Zhiping Gu

{"title":"Partially-independent component analysis for tissue heterogeneity correction in microarray gene expression analysis","authors":"Y. Wang, Junying Zhang, Javed I. Khan, R. Clarke, Zhiping Gu","doi":"10.1109/NNSP.2003.1318001","DOIUrl":"https://doi.org/10.1109/NNSP.2003.1318001","url":null,"abstract":"Gene microarray technologies provide powerful tools for the large scale analysis of gene expression in cancer research. Clinical applications often aim to facilitate a molecular classification of cancers based on discriminatory genes associated with different clinical stages or outcomes. However, gene expression profiles often represent a composite of more than one distinct source due to tissue heterogeneity, and could result in extracting signatures reflecting the proportion of stromal contamination in the sample, rather than underlying tumor biology. We therefore wish to introduce a computational approach, which allows for a blind decomposition of gene expression profiles from mixed cell populations. The algorithm is based on a linear latent variable model, whose parameters are estimated using partially-independent component analysis, supported by a subset of differentially-expressed genes. We demonstrate the principle of the approach on the data sets derived from mixed cell lines of small round blue cell tumors. Because accurate source separation can be achieved blindly and numerically, we anticipate that computational correction of tissue heterogeneity would be useful in a wide variety of gene microarray studies.","PeriodicalId":315958,"journal":{"name":"2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130229688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Architecture of an intelligent beacon for wireless sensor networks 无线传感器网络智能信标的体系结构

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718) Pub Date : 2003-09-17 DOI: 10.1109/NNSP.2003.1318013

P. Garda, O. Romain, B. Granado, A. Pinna, D. Faura, K. Hachicha

引用次数: 9

Independent components analysis for fetal electrocardiogram extraction: a case for the data efficient Mermaid algorithm 胎儿心电图提取的独立分量分析:以数据高效的Mermaid算法为例

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718) Pub Date : 2003-09-17 DOI: 10.1109/NNSP.2003.1318039

Dorothee E. Marossero, Deniz Erdoğmuş, N. Euliano, J. Príncipe, K. Hild

{"title":"Independent components analysis for fetal electrocardiogram extraction: a case for the data efficient Mermaid algorithm","authors":"Dorothee E. Marossero, Deniz Erdoğmuş, N. Euliano, J. Príncipe, K. Hild","doi":"10.1109/NNSP.2003.1318039","DOIUrl":"https://doi.org/10.1109/NNSP.2003.1318039","url":null,"abstract":"Fetal heart rate (FHR) monitoring is currently the primary methodology for antenatal determination of fetal well-being. Currently, the FHR can be detected with ultrasonography, but the additional information from fetal electrocardiogram (FECG) is only available via an invasive scalp electrode. A cost effective noninvasive monitoring through standard ECG electrodes could be used on nearly every patient in lieu of the ultrasound monitors. In this method, a number of electrodes are positioned on the abdomen of the mother to collect, simultaneously, various combinations of the signals including the heartbeats of the mother and the fetus. For accurate fetal heart-rate estimation, a clean FECG must be extracted from the collected mixtures. It is well known that this can be achieved using blind source separation (BSS) techniques. In this paper, the performance of the Mermaid algorithm, which is based on minimizing Renyi's mutual information, is evaluated on this problem of great practical importance. The effectiveness and data efficiency of Mermaid and its superiority over alternative information theoretic BSS algorithms are illustrated using artificially mixed ECG signals as well as fetal heart rate estimates in real ECG mixtures.","PeriodicalId":315958,"journal":{"name":"2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125610215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 41

Modulation transfer function and noise measurement using neural networks 调制传递函数与神经网络噪声测量

2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718) Pub Date : 2003-09-17 DOI: 10.1109/NNSP.2003.1318011

J. Delvit, D. Léger, S. Roques, C. Valorge

引用次数: 3