Pub Date : 2011-12-27DOI: 10.1109/BIBMW.2011.6112548
S. A. Bukhari, G. Caetano-Anollés
Protein architecture refers to similar secondary structural arrangements irrespective of their connectivity. Here we aim to explore the evolution of protein architectures by benchmarking CATH and SCOP annotations. For example, we explore the appearance and diversification of protein architectures such as sandwiches, bundles, barrels, solenoids, ribbons, trefoils, prisms and propellers. Structural phylogenies generated at CATH “A”, “T” and “H” levels of structural abstraction revealed patterns of reductive evolution and three epochs in the evolution of protein world. Although CATH and SCOP differ significantly in their protein domain definitions and in the hierarchical partitioning of fold space, our findings strongly support the fact that both protein structural classification systems classify a protein on a very similar theoretical basis by taking into account their structural, functional and evolutionary roles. The tree of “A” showed that the 3-layer (aba) sandwich (3.40), the orthogonal bundle (1.10) and the alpha-beta complex (3.90) harbor simple secondary structure arrangements that are the most ancient, popular and abundant architectures in the protein world.
{"title":"Evolution of protein architectures inferred from phylogenomic analysis of CATH","authors":"S. A. Bukhari, G. Caetano-Anollés","doi":"10.1109/BIBMW.2011.6112548","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112548","url":null,"abstract":"Protein architecture refers to similar secondary structural arrangements irrespective of their connectivity. Here we aim to explore the evolution of protein architectures by benchmarking CATH and SCOP annotations. For example, we explore the appearance and diversification of protein architectures such as sandwiches, bundles, barrels, solenoids, ribbons, trefoils, prisms and propellers. Structural phylogenies generated at CATH “A”, “T” and “H” levels of structural abstraction revealed patterns of reductive evolution and three epochs in the evolution of protein world. Although CATH and SCOP differ significantly in their protein domain definitions and in the hierarchical partitioning of fold space, our findings strongly support the fact that both protein structural classification systems classify a protein on a very similar theoretical basis by taking into account their structural, functional and evolutionary roles. The tree of “A” showed that the 3-layer (aba) sandwich (3.40), the orthogonal bundle (1.10) and the alpha-beta complex (3.90) harbor simple secondary structure arrangements that are the most ancient, popular and abundant architectures in the protein world.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"132 12 1","pages":"1029-1031"},"PeriodicalIF":0.0,"publicationDate":"2011-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79621961","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}
Pub Date : 2011-12-27DOI: 10.1109/BIBMW.2011.6112528
Ahmed Sadeque, N. Serao, B. Southey, Zeeshan Fazal, S. Rodriguez-Zas
Exon expression platforms have allowed the detection of associations between alternative exon usage (AEU) and the proliferation of malignant cells in cancer. However, due to inadequate number of studies performed on AEU and the approaches utilized to detect AEU events, well established biomarkers for GBM are not available. The expression of exons corresponding to 25,403 genes was related to the survival of 328 patients diagnosed with Glioblastoma multiforme (GBM). An approach that takes exon expression into account was adopted to detect the association between exon expression and survival. Association between expression and survival were identified in 22 single-exon genes 248 genes with 2–25 exons, 1430 genes with >24 and <50 exons and 215 genes with >50 exons. Among the multiple-exon genes exhibiting AEU were epidermal growth factor (EGF) and nidogen2 (NID2) that have known association with GBM. These results are consistent with reports that these genes have 12 and 10 transcripts, respectively.
{"title":"Hierarchical modeling of alternative exon usage associations with survival","authors":"Ahmed Sadeque, N. Serao, B. Southey, Zeeshan Fazal, S. Rodriguez-Zas","doi":"10.1109/BIBMW.2011.6112528","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112528","url":null,"abstract":"Exon expression platforms have allowed the detection of associations between alternative exon usage (AEU) and the proliferation of malignant cells in cancer. However, due to inadequate number of studies performed on AEU and the approaches utilized to detect AEU events, well established biomarkers for GBM are not available. The expression of exons corresponding to 25,403 genes was related to the survival of 328 patients diagnosed with Glioblastoma multiforme (GBM). An approach that takes exon expression into account was adopted to detect the association between exon expression and survival. Association between expression and survival were identified in 22 single-exon genes 248 genes with 2–25 exons, 1430 genes with >24 and <50 exons and 215 genes with >50 exons. Among the multiple-exon genes exhibiting AEU were epidermal growth factor (EGF) and nidogen2 (NID2) that have known association with GBM. These results are consistent with reports that these genes have 12 and 10 transcripts, respectively.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"137 1","pages":"978-980"},"PeriodicalIF":0.0,"publicationDate":"2011-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80514477","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}
Pub Date : 2011-12-27DOI: 10.1109/BIBMW.2011.6112435
Alessio Pierluigi Placitelli, Luigi Gallo
Medical in-situ visualization deals with the display of patient specific imaging data at the location where they actually are. To be effective, it requires high end I/O devices, and computationally expensive and time-consuming algorithms. In this paper, we explore the potential simplifications derived from the use of 3D point cloud sensors in medical augmented reality applications by designing a low-cost system that takes advantage of depth data to apply medical imagery to live video streams of patients.
{"title":"3D point cloud sensors for low-cost medical in-situ visualization","authors":"Alessio Pierluigi Placitelli, Luigi Gallo","doi":"10.1109/BIBMW.2011.6112435","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112435","url":null,"abstract":"Medical in-situ visualization deals with the display of patient specific imaging data at the location where they actually are. To be effective, it requires high end I/O devices, and computationally expensive and time-consuming algorithms. In this paper, we explore the potential simplifications derived from the use of 3D point cloud sensors in medical augmented reality applications by designing a low-cost system that takes advantage of depth data to apply medical imagery to live video streams of patients.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"16 1 1","pages":"596-597"},"PeriodicalIF":0.0,"publicationDate":"2011-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90225374","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}
Pub Date : 2011-11-12DOI: 10.1109/BIBMW.2011.6112513
W. Hsu, J. Yeh, Yi-Chung Chang, M. Lo, Yi-Hsien Lin
Low contrast to noise ratio (CNR) of unenhanced computed tomography (CT) is sometimes hard to visualize by the clinical practice. In order to assist the clinical diagnosis, a computer aided for unenhanced CT image processing is introduced in detection of hepatocellular carcinoma (HCC). This study utilized the stochastic resonance (SR) filter by adjusting localized threshold range with adding random noise for enhancing the region of interest (ROI). The quantitative measurement by using the measure of enhancement or measure of improvement (EME) is applied on the series of original and enhanced images. The value of mean and standard deviation of EME values is 2.652 ± 2.167 for the original images and 6.260 ± 1.206 for enhanced images. Then k-mean clustering method played the role based on the cluster analysis with the nearest mean for the local segmentation. The diagnostic check for determining the number of clusters on each enhanced images is important for getting a better result. In fact, K = 10 is more appropriate for the data sets of enhanced images. Finally, the image fusion process is involved two sets of data, enhanced and post-processed of enhanced and clustering information, to provide relevant information. Using the T = 0.45 as the threshold value applied on clustering and enhanced images eliminates the stronger intensity of pixels. Though those processes, the unenhanced information could be extracted out as the reference information for the clinical diagnosis. HCC was well isolated on processed images. Our results demonstrated the utilization of the computer aided for image processing of CT images might help to detect the HCC.
{"title":"A computer aided for image processing of computed tomography in hepatocellular carcinoma","authors":"W. Hsu, J. Yeh, Yi-Chung Chang, M. Lo, Yi-Hsien Lin","doi":"10.1109/BIBMW.2011.6112513","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112513","url":null,"abstract":"Low contrast to noise ratio (CNR) of unenhanced computed tomography (CT) is sometimes hard to visualize by the clinical practice. In order to assist the clinical diagnosis, a computer aided for unenhanced CT image processing is introduced in detection of hepatocellular carcinoma (HCC). This study utilized the stochastic resonance (SR) filter by adjusting localized threshold range with adding random noise for enhancing the region of interest (ROI). The quantitative measurement by using the measure of enhancement or measure of improvement (EME) is applied on the series of original and enhanced images. The value of mean and standard deviation of EME values is 2.652 ± 2.167 for the original images and 6.260 ± 1.206 for enhanced images. Then k-mean clustering method played the role based on the cluster analysis with the nearest mean for the local segmentation. The diagnostic check for determining the number of clusters on each enhanced images is important for getting a better result. In fact, K = 10 is more appropriate for the data sets of enhanced images. Finally, the image fusion process is involved two sets of data, enhanced and post-processed of enhanced and clustering information, to provide relevant information. Using the T = 0.45 as the threshold value applied on clustering and enhanced images eliminates the stronger intensity of pixels. Though those processes, the unenhanced information could be extracted out as the reference information for the clinical diagnosis. HCC was well isolated on processed images. Our results demonstrated the utilization of the computer aided for image processing of CT images might help to detect the HCC.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"69 1","pages":"942-944"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74270930","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}
Pub Date : 2011-11-12DOI: 10.1109/BIBMW.2011.6112489
Zhao Wen-guang, Yu Chao-fan, Zhan Ruo-ting, H. Rui
Based on ideas and methods of organoleptic evaluation on agricultural commodities, the article establishes the quantitative indicators that can make effect evaluation and control of the level of Chinese herbal product specifications for the herb Amomum. Combined with IT technology, we analyze and modeling the experimental data to explore the generation of a practical, scientific and standardized method of Amomum organoleptic evaluation. The application of robust regression in the research to produce the prediction model achieved the classification forecast of Amomum product specifications.
{"title":"Research on data mining methods for organoleptic determination of Amomum villosum product","authors":"Zhao Wen-guang, Yu Chao-fan, Zhan Ruo-ting, H. Rui","doi":"10.1109/BIBMW.2011.6112489","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112489","url":null,"abstract":"Based on ideas and methods of organoleptic evaluation on agricultural commodities, the article establishes the quantitative indicators that can make effect evaluation and control of the level of Chinese herbal product specifications for the herb Amomum. Combined with IT technology, we analyze and modeling the experimental data to explore the generation of a practical, scientific and standardized method of Amomum organoleptic evaluation. The application of robust regression in the research to produce the prediction model achieved the classification forecast of Amomum product specifications.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"21 1","pages":"873-880"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73242134","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}
Min Liu, Hanchuan Peng, A. Roy-Chowdhury, E. Myers
This paper addresses the problem of 3D neuron tips detection in volumetric microscopy image stacks. We focus particularly on neuron tracing applications, where the detected 3D tips could be used as the seeding points. Most of the existing neuron tracing methods require a good choice of seeding points. In this paper, we propose an automated neuron tips detection method for volumetric microscopy image stacks. Our method is based on first detecting 2D tips using curvature information and a ray-shooting intensity distribution model, and then extending it to the 3D stack by rejecting false positives. We tested this method based on the V3D platform, which can reconstruct a neuron based on automated searching of the optimal ¡®paths' connecting those detected 3D tips. The experiments demonstrate the effectiveness of the proposed method in building a fully automatic neuron tracing system.
{"title":"3D Neuron Tip Detection in Volumetric Microscopy Images","authors":"Min Liu, Hanchuan Peng, A. Roy-Chowdhury, E. Myers","doi":"10.1109/BIBM.2011.126","DOIUrl":"https://doi.org/10.1109/BIBM.2011.126","url":null,"abstract":"This paper addresses the problem of 3D neuron tips detection in volumetric microscopy image stacks. We focus particularly on neuron tracing applications, where the detected 3D tips could be used as the seeding points. Most of the existing neuron tracing methods require a good choice of seeding points. In this paper, we propose an automated neuron tips detection method for volumetric microscopy image stacks. Our method is based on first detecting 2D tips using curvature information and a ray-shooting intensity distribution model, and then extending it to the 3D stack by rejecting false positives. We tested this method based on the V3D platform, which can reconstruct a neuron based on automated searching of the optimal ¡®paths' connecting those detected 3D tips. The experiments demonstrate the effectiveness of the proposed method in building a fully automatic neuron tracing system.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"48 1","pages":"366-371"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73426311","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}
Erin K. Hamilton, Seonghye Jeon, Pepa Ramírez-Cobo, K. Lee, B. Vidakovic
The aim of this paper is to present results from a comparative investigation into the diagnostic performance of several wavelet-based estimators of scaling, some from published literature and some newly proposed. These estimators are evaluated based on their ability to classify digitized mammogram images from a clinical database, for which the true disease status is known by biopsy. We found that Abry-Veitch and modified weighted Theil-type estimators provided the best classification rates, while the standard wavelet-based OLS estimator performed worst. The results are robust with respect to choice of wavelets (Haar wavelet being an exception) and are of potential clinical value. The diagnostic is based on the properties of image backgrounds (which is an unused diagnostic modality in Mammograms) and the best correct classification rates achieve 90%, varying slightly with the choice of basis, levels used, and size of training set.
{"title":"Diagnostic Classification of Digital Mammograms by Wavelet-Based Spectral Tools: A Comparative Study","authors":"Erin K. Hamilton, Seonghye Jeon, Pepa Ramírez-Cobo, K. Lee, B. Vidakovic","doi":"10.1109/BIBM.2011.44","DOIUrl":"https://doi.org/10.1109/BIBM.2011.44","url":null,"abstract":"The aim of this paper is to present results from a comparative investigation into the diagnostic performance of several wavelet-based estimators of scaling, some from published literature and some newly proposed. These estimators are evaluated based on their ability to classify digitized mammogram images from a clinical database, for which the true disease status is known by biopsy. We found that Abry-Veitch and modified weighted Theil-type estimators provided the best classification rates, while the standard wavelet-based OLS estimator performed worst. The results are robust with respect to choice of wavelets (Haar wavelet being an exception) and are of potential clinical value. The diagnostic is based on the properties of image backgrounds (which is an unused diagnostic modality in Mammograms) and the best correct classification rates achieve 90%, varying slightly with the choice of basis, levels used, and size of training set.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"18 1","pages":"384-389"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74970667","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}
Pub Date : 2011-11-12DOI: 10.1109/BIBMW.2011.6112396
Promita Bose, Xiaxia Yu, R. Harrison
The application of machine learning and datamining to the analysis and prediction of protein structure is a research area with potentially high impact in both computer science and biology. Proteins structures are inherently complicated objects with a mixture of crisp and fuzzy properties. Therefore developing effective representations for them is a research problem in itself, while quantifying and predicting properties and structure is of immediate importance in structural biology. This paper focuses on developing a compact, effective, efficient and accurate representation of protein structure that is compatible with widely used machine learning tools like the SVM. Graphs based on Delaunay triangulation are used to represent the structure, and then functions are constructed from these graphs to develop constant-size representations of protein structure that are tightly bound to the amino acid sequence. The representations preserve sufficient information to be valuable for model vs. experimental structure classification and regression analysis of model quality.
{"title":"Encoding protein structure with functions on graphs","authors":"Promita Bose, Xiaxia Yu, R. Harrison","doi":"10.1109/BIBMW.2011.6112396","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112396","url":null,"abstract":"The application of machine learning and datamining to the analysis and prediction of protein structure is a research area with potentially high impact in both computer science and biology. Proteins structures are inherently complicated objects with a mixture of crisp and fuzzy properties. Therefore developing effective representations for them is a research problem in itself, while quantifying and predicting properties and structure is of immediate importance in structural biology. This paper focuses on developing a compact, effective, efficient and accurate representation of protein structure that is compatible with widely used machine learning tools like the SVM. Graphs based on Delaunay triangulation are used to represent the structure, and then functions are constructed from these graphs to develop constant-size representations of protein structure that are tightly bound to the amino acid sequence. The representations preserve sufficient information to be valuable for model vs. experimental structure classification and regression analysis of model quality.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"1 1","pages":"338-344"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75038582","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}
Pub Date : 2011-11-12DOI: 10.1109/BIBMW.2011.6112477
Guang Zheng, M. Jiang, Cheng Lu, Hongtao Guo, Junping Zhan, A. Lu
In the theory of traditional Chinese medicine, deficiency pattern is a distinguished one among patterns in rheumatoid arthritis. As for the explanation of deficiency pattern in rheumatoid arthritis, traditional Chinese medicine explains the deficiency in organs of both liver and kidney. As for the modern medicine, no specific factor available to explain it. In this paper, we propose an approach through data mining to explore the biological basis of deficiency pattern in rheumatoid arthritis. In this approach, the first step is to find the formula in traditional Chinese medicine in the treatment of rheumatoid arthritis. Then, list out the top three diseases which can be regulated by this formula. After that, we can find the networks of biological basis existing among all these three diseases by data mining. By analyzing these networks, directly or not, the deficiency pattern in rheumatoid arthritis might be caused by the chronic inflammation.
{"title":"Exploring the biological basis of deficiency pattern in rheumatoid arthritis through text mining","authors":"Guang Zheng, M. Jiang, Cheng Lu, Hongtao Guo, Junping Zhan, A. Lu","doi":"10.1109/BIBMW.2011.6112477","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112477","url":null,"abstract":"In the theory of traditional Chinese medicine, deficiency pattern is a distinguished one among patterns in rheumatoid arthritis. As for the explanation of deficiency pattern in rheumatoid arthritis, traditional Chinese medicine explains the deficiency in organs of both liver and kidney. As for the modern medicine, no specific factor available to explain it. In this paper, we propose an approach through data mining to explore the biological basis of deficiency pattern in rheumatoid arthritis. In this approach, the first step is to find the formula in traditional Chinese medicine in the treatment of rheumatoid arthritis. Then, list out the top three diseases which can be regulated by this formula. After that, we can find the networks of biological basis existing among all these three diseases by data mining. By analyzing these networks, directly or not, the deficiency pattern in rheumatoid arthritis might be caused by the chronic inflammation.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"77 1","pages":"811-816"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76579046","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}
Pub Date : 2011-11-12DOI: 10.1109/BIBMW.2011.6112497
Donghoon Lee, Seungpyo Jung, Youngju Park, Jingzhe Xu, Jusung Park
Bio-signal processor platform system carries out the bio-signal processing extracted from array sensors. This system consists of 32-bit RISC processor, data converter circuit, array sensors and bio-signal processing algorithm. The designed specific processor includes CPU functional blocks and memory. Array sensors measure a variation of capacitance value by reaction with DNA, aptamer and protein. Processor reduces noise component from measured bio-signal and compares and detects disease by analyzing properties of bio-signals.
{"title":"Bio-signal procssor platform system for array sensors","authors":"Donghoon Lee, Seungpyo Jung, Youngju Park, Jingzhe Xu, Jusung Park","doi":"10.1109/BIBMW.2011.6112497","DOIUrl":"https://doi.org/10.1109/BIBMW.2011.6112497","url":null,"abstract":"Bio-signal processor platform system carries out the bio-signal processing extracted from array sensors. This system consists of 32-bit RISC processor, data converter circuit, array sensors and bio-signal processing algorithm. The designed specific processor includes CPU functional blocks and memory. Array sensors measure a variation of capacitance value by reaction with DNA, aptamer and protein. Processor reduces noise component from measured bio-signal and compares and detects disease by analyzing properties of bio-signals.","PeriodicalId":6345,"journal":{"name":"2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)","volume":"138 1","pages":"904-906"},"PeriodicalIF":0.0,"publicationDate":"2011-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77523017","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}
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