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Bio Imaging and Signal Processing

BACTERIAL IMAGE ANALYSIS AND SINGLE-CELL ANALYTICS TO DECIPHER THE BEHAVIOR OF LARGE MICROBIAL COMMUNITIES


Time-lapse microscopy provides 4D imaging data for monitoring and studying down to single-cell, the stochastic processes involved as bacterial colonies grow and interact under different stress conditions. Two main factors prevent high throughput analysis: a) cell segmentation and tracking are very time-consuming and error-prone and b) analytics tools are lacking to interpret the plethora of features extracted from a complex “cell-movie.” To address both limitations, we have recently developed a multi-resolution Bio-image Analysis & Single-Cell Analytics framework, called BaSCA.

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Authors:
Athanasios D. Balomenos, Victoria Stefanou, Elias S. Manolakos
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8 October 2018 - 9:46am
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BACTERIAL IMAGE ANALYSIS AND SINGLE-CELL ANALYTICS TO DECIPHER THE BEHAVIOR OF LARGE MICROBIAL COMMUNITIES POSTER

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[1] Athanasios D. Balomenos, Victoria Stefanou, Elias S. Manolakos, "BACTERIAL IMAGE ANALYSIS AND SINGLE-CELL ANALYTICS TO DECIPHER THE BEHAVIOR OF LARGE MICROBIAL COMMUNITIES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3634. Accessed: Nov. 17, 2018.
@article{3634-18,
url = {http://sigport.org/3634},
author = {Athanasios D. Balomenos; Victoria Stefanou; Elias S. Manolakos },
publisher = {IEEE SigPort},
title = {BACTERIAL IMAGE ANALYSIS AND SINGLE-CELL ANALYTICS TO DECIPHER THE BEHAVIOR OF LARGE MICROBIAL COMMUNITIES},
year = {2018} }
TY - EJOUR
T1 - BACTERIAL IMAGE ANALYSIS AND SINGLE-CELL ANALYTICS TO DECIPHER THE BEHAVIOR OF LARGE MICROBIAL COMMUNITIES
AU - Athanasios D. Balomenos; Victoria Stefanou; Elias S. Manolakos
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3634
ER -
Athanasios D. Balomenos, Victoria Stefanou, Elias S. Manolakos. (2018). BACTERIAL IMAGE ANALYSIS AND SINGLE-CELL ANALYTICS TO DECIPHER THE BEHAVIOR OF LARGE MICROBIAL COMMUNITIES. IEEE SigPort. http://sigport.org/3634
Athanasios D. Balomenos, Victoria Stefanou, Elias S. Manolakos, 2018. BACTERIAL IMAGE ANALYSIS AND SINGLE-CELL ANALYTICS TO DECIPHER THE BEHAVIOR OF LARGE MICROBIAL COMMUNITIES. Available at: http://sigport.org/3634.
Athanasios D. Balomenos, Victoria Stefanou, Elias S. Manolakos. (2018). "BACTERIAL IMAGE ANALYSIS AND SINGLE-CELL ANALYTICS TO DECIPHER THE BEHAVIOR OF LARGE MICROBIAL COMMUNITIES." Web.
1. Athanasios D. Balomenos, Victoria Stefanou, Elias S. Manolakos. BACTERIAL IMAGE ANALYSIS AND SINGLE-CELL ANALYTICS TO DECIPHER THE BEHAVIOR OF LARGE MICROBIAL COMMUNITIES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3634

ASSESSINGTHEIMPACTOFTHEDECEIVEDNONLOCALMEANSFILTERASA PREPROCESSINGSTAGEINACONVOLUTIONALNEURALNETWORKBASED APPROACHFORAGEESTIMATIONUSINGDIGITALHANDX-RAYIMAGES


In this work we analyze the impact of denoising, contrast and edge enhancement using the Deceived Non Local Means (DNLM) filter in a Convolutional Neural Network (CNN) based approach for age estimation using digital X-ray images from hands. The DNLM filter contains two parameters which control edge enhancement and denoising. Increasing levels were tested to assess the impact of both contrast enhancement and denoising in the CNN based model regression accuracy.

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Authors:
S. Calderon?, F. Fallas†, M. Zumbado‡, P. N. Tyrrell±, H. Stark, Z. Emersic§, B. Medeno, M. Solis+
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6 October 2018 - 8:15am
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[1] S. Calderon?, F. Fallas†, M. Zumbado‡, P. N. Tyrrell±, H. Stark, Z. Emersic§, B. Medeno, M. Solis+, "ASSESSINGTHEIMPACTOFTHEDECEIVEDNONLOCALMEANSFILTERASA PREPROCESSINGSTAGEINACONVOLUTIONALNEURALNETWORKBASED APPROACHFORAGEESTIMATIONUSINGDIGITALHANDX-RAYIMAGES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3573. Accessed: Nov. 17, 2018.
@article{3573-18,
url = {http://sigport.org/3573},
author = {S. Calderon?; F. Fallas†; M. Zumbado‡; P. N. Tyrrell±; H. Stark; Z. Emersic§; B. Medeno; M. Solis+ },
publisher = {IEEE SigPort},
title = {ASSESSINGTHEIMPACTOFTHEDECEIVEDNONLOCALMEANSFILTERASA PREPROCESSINGSTAGEINACONVOLUTIONALNEURALNETWORKBASED APPROACHFORAGEESTIMATIONUSINGDIGITALHANDX-RAYIMAGES},
year = {2018} }
TY - EJOUR
T1 - ASSESSINGTHEIMPACTOFTHEDECEIVEDNONLOCALMEANSFILTERASA PREPROCESSINGSTAGEINACONVOLUTIONALNEURALNETWORKBASED APPROACHFORAGEESTIMATIONUSINGDIGITALHANDX-RAYIMAGES
AU - S. Calderon?; F. Fallas†; M. Zumbado‡; P. N. Tyrrell±; H. Stark; Z. Emersic§; B. Medeno; M. Solis+
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3573
ER -
S. Calderon?, F. Fallas†, M. Zumbado‡, P. N. Tyrrell±, H. Stark, Z. Emersic§, B. Medeno, M. Solis+. (2018). ASSESSINGTHEIMPACTOFTHEDECEIVEDNONLOCALMEANSFILTERASA PREPROCESSINGSTAGEINACONVOLUTIONALNEURALNETWORKBASED APPROACHFORAGEESTIMATIONUSINGDIGITALHANDX-RAYIMAGES. IEEE SigPort. http://sigport.org/3573
S. Calderon?, F. Fallas†, M. Zumbado‡, P. N. Tyrrell±, H. Stark, Z. Emersic§, B. Medeno, M. Solis+, 2018. ASSESSINGTHEIMPACTOFTHEDECEIVEDNONLOCALMEANSFILTERASA PREPROCESSINGSTAGEINACONVOLUTIONALNEURALNETWORKBASED APPROACHFORAGEESTIMATIONUSINGDIGITALHANDX-RAYIMAGES. Available at: http://sigport.org/3573.
S. Calderon?, F. Fallas†, M. Zumbado‡, P. N. Tyrrell±, H. Stark, Z. Emersic§, B. Medeno, M. Solis+. (2018). "ASSESSINGTHEIMPACTOFTHEDECEIVEDNONLOCALMEANSFILTERASA PREPROCESSINGSTAGEINACONVOLUTIONALNEURALNETWORKBASED APPROACHFORAGEESTIMATIONUSINGDIGITALHANDX-RAYIMAGES." Web.
1. S. Calderon?, F. Fallas†, M. Zumbado‡, P. N. Tyrrell±, H. Stark, Z. Emersic§, B. Medeno, M. Solis+. ASSESSINGTHEIMPACTOFTHEDECEIVEDNONLOCALMEANSFILTERASA PREPROCESSINGSTAGEINACONVOLUTIONALNEURALNETWORKBASED APPROACHFORAGEESTIMATIONUSINGDIGITALHANDX-RAYIMAGES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3573

MR-SRNET: TRANSFORMATION OF LOW FIELD MR IMAGES TO HIGH FIELD MR IMAGES

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Authors:
Prabhjot Kaur, Aditya Sharma, Aditya Nigam, Arnav Bhavsar
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5 October 2018 - 12:09pm
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[1] Prabhjot Kaur, Aditya Sharma, Aditya Nigam, Arnav Bhavsar, "MR-SRNET: TRANSFORMATION OF LOW FIELD MR IMAGES TO HIGH FIELD MR IMAGES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3545. Accessed: Nov. 17, 2018.
@article{3545-18,
url = {http://sigport.org/3545},
author = {Prabhjot Kaur; Aditya Sharma; Aditya Nigam; Arnav Bhavsar },
publisher = {IEEE SigPort},
title = {MR-SRNET: TRANSFORMATION OF LOW FIELD MR IMAGES TO HIGH FIELD MR IMAGES},
year = {2018} }
TY - EJOUR
T1 - MR-SRNET: TRANSFORMATION OF LOW FIELD MR IMAGES TO HIGH FIELD MR IMAGES
AU - Prabhjot Kaur; Aditya Sharma; Aditya Nigam; Arnav Bhavsar
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3545
ER -
Prabhjot Kaur, Aditya Sharma, Aditya Nigam, Arnav Bhavsar. (2018). MR-SRNET: TRANSFORMATION OF LOW FIELD MR IMAGES TO HIGH FIELD MR IMAGES. IEEE SigPort. http://sigport.org/3545
Prabhjot Kaur, Aditya Sharma, Aditya Nigam, Arnav Bhavsar, 2018. MR-SRNET: TRANSFORMATION OF LOW FIELD MR IMAGES TO HIGH FIELD MR IMAGES. Available at: http://sigport.org/3545.
Prabhjot Kaur, Aditya Sharma, Aditya Nigam, Arnav Bhavsar. (2018). "MR-SRNET: TRANSFORMATION OF LOW FIELD MR IMAGES TO HIGH FIELD MR IMAGES." Web.
1. Prabhjot Kaur, Aditya Sharma, Aditya Nigam, Arnav Bhavsar. MR-SRNET: TRANSFORMATION OF LOW FIELD MR IMAGES TO HIGH FIELD MR IMAGES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3545

SIPAKMED: A NEW DATASET FOR FEATURE AND IMAGE BASED CLASSIFICATION OF NORMAL AND PATHOLOGICAL CERVICAL CELLS IN PAP SMEAR IMAGES

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Authors:
Marina E. Plissiti, P. Dimitrakopoulos, G. Sfikas, Christophoros Nikou, O. Krikoni, A. Charchanti
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5 October 2018 - 4:35am
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[1] Marina E. Plissiti, P. Dimitrakopoulos, G. Sfikas, Christophoros Nikou, O. Krikoni, A. Charchanti , "SIPAKMED: A NEW DATASET FOR FEATURE AND IMAGE BASED CLASSIFICATION OF NORMAL AND PATHOLOGICAL CERVICAL CELLS IN PAP SMEAR IMAGES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3520. Accessed: Nov. 17, 2018.
@article{3520-18,
url = {http://sigport.org/3520},
author = {Marina E. Plissiti; P. Dimitrakopoulos; G. Sfikas; Christophoros Nikou; O. Krikoni; A. Charchanti },
publisher = {IEEE SigPort},
title = {SIPAKMED: A NEW DATASET FOR FEATURE AND IMAGE BASED CLASSIFICATION OF NORMAL AND PATHOLOGICAL CERVICAL CELLS IN PAP SMEAR IMAGES},
year = {2018} }
TY - EJOUR
T1 - SIPAKMED: A NEW DATASET FOR FEATURE AND IMAGE BASED CLASSIFICATION OF NORMAL AND PATHOLOGICAL CERVICAL CELLS IN PAP SMEAR IMAGES
AU - Marina E. Plissiti; P. Dimitrakopoulos; G. Sfikas; Christophoros Nikou; O. Krikoni; A. Charchanti
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3520
ER -
Marina E. Plissiti, P. Dimitrakopoulos, G. Sfikas, Christophoros Nikou, O. Krikoni, A. Charchanti . (2018). SIPAKMED: A NEW DATASET FOR FEATURE AND IMAGE BASED CLASSIFICATION OF NORMAL AND PATHOLOGICAL CERVICAL CELLS IN PAP SMEAR IMAGES. IEEE SigPort. http://sigport.org/3520
Marina E. Plissiti, P. Dimitrakopoulos, G. Sfikas, Christophoros Nikou, O. Krikoni, A. Charchanti , 2018. SIPAKMED: A NEW DATASET FOR FEATURE AND IMAGE BASED CLASSIFICATION OF NORMAL AND PATHOLOGICAL CERVICAL CELLS IN PAP SMEAR IMAGES. Available at: http://sigport.org/3520.
Marina E. Plissiti, P. Dimitrakopoulos, G. Sfikas, Christophoros Nikou, O. Krikoni, A. Charchanti . (2018). "SIPAKMED: A NEW DATASET FOR FEATURE AND IMAGE BASED CLASSIFICATION OF NORMAL AND PATHOLOGICAL CERVICAL CELLS IN PAP SMEAR IMAGES." Web.
1. Marina E. Plissiti, P. Dimitrakopoulos, G. Sfikas, Christophoros Nikou, O. Krikoni, A. Charchanti . SIPAKMED: A NEW DATASET FOR FEATURE AND IMAGE BASED CLASSIFICATION OF NORMAL AND PATHOLOGICAL CERVICAL CELLS IN PAP SMEAR IMAGES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3520

INTRA-RETINAL LAYER SEGMENTATION OF OPTICAL COHERENCE TOMOGRAPHY USING 3D FULLY CONVOLUTIONAL NETWORKS


Optical coherence tomography (OCT) is a powerful method for imaging the retinal layers. In this paper, we develop a novel 3D fully convolutional deep architecture for automated segmentation of retinal layers in OCT scans. This model extracts features from both the spatial and the inter-frame dimensions by performing 3D convolutions, thereby capturing the information encoded in multiple adjacent frames.

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5 October 2018 - 3:33am
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[1] , "INTRA-RETINAL LAYER SEGMENTATION OF OPTICAL COHERENCE TOMOGRAPHY USING 3D FULLY CONVOLUTIONAL NETWORKS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3511. Accessed: Nov. 17, 2018.
@article{3511-18,
url = {http://sigport.org/3511},
author = { },
publisher = {IEEE SigPort},
title = {INTRA-RETINAL LAYER SEGMENTATION OF OPTICAL COHERENCE TOMOGRAPHY USING 3D FULLY CONVOLUTIONAL NETWORKS},
year = {2018} }
TY - EJOUR
T1 - INTRA-RETINAL LAYER SEGMENTATION OF OPTICAL COHERENCE TOMOGRAPHY USING 3D FULLY CONVOLUTIONAL NETWORKS
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3511
ER -
. (2018). INTRA-RETINAL LAYER SEGMENTATION OF OPTICAL COHERENCE TOMOGRAPHY USING 3D FULLY CONVOLUTIONAL NETWORKS. IEEE SigPort. http://sigport.org/3511
, 2018. INTRA-RETINAL LAYER SEGMENTATION OF OPTICAL COHERENCE TOMOGRAPHY USING 3D FULLY CONVOLUTIONAL NETWORKS. Available at: http://sigport.org/3511.
. (2018). "INTRA-RETINAL LAYER SEGMENTATION OF OPTICAL COHERENCE TOMOGRAPHY USING 3D FULLY CONVOLUTIONAL NETWORKS." Web.
1. . INTRA-RETINAL LAYER SEGMENTATION OF OPTICAL COHERENCE TOMOGRAPHY USING 3D FULLY CONVOLUTIONAL NETWORKS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3511

Deep Tree Models for ‘Big’ Biological Data


The identification of useful temporal dependence structure in discrete time series data is an important component of algorithms applied to many tasks in statistical inference and machine learning, and used in a wide variety of problems across the spectrum of biological studies. Most of the early statistical approaches were ineffective in practice, because the amount of data required for reliable modelling grew exponentially with memory length.

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Lambros Mertzanis, Athina Panotopoulou, Maria Skoularidou
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24 June 2018 - 9:36am
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[1] Lambros Mertzanis, Athina Panotopoulou, Maria Skoularidou, "Deep Tree Models for ‘Big’ Biological Data", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3320. Accessed: Nov. 17, 2018.
@article{3320-18,
url = {http://sigport.org/3320},
author = {Lambros Mertzanis; Athina Panotopoulou; Maria Skoularidou },
publisher = {IEEE SigPort},
title = {Deep Tree Models for ‘Big’ Biological Data},
year = {2018} }
TY - EJOUR
T1 - Deep Tree Models for ‘Big’ Biological Data
AU - Lambros Mertzanis; Athina Panotopoulou; Maria Skoularidou
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3320
ER -
Lambros Mertzanis, Athina Panotopoulou, Maria Skoularidou. (2018). Deep Tree Models for ‘Big’ Biological Data. IEEE SigPort. http://sigport.org/3320
Lambros Mertzanis, Athina Panotopoulou, Maria Skoularidou, 2018. Deep Tree Models for ‘Big’ Biological Data. Available at: http://sigport.org/3320.
Lambros Mertzanis, Athina Panotopoulou, Maria Skoularidou. (2018). "Deep Tree Models for ‘Big’ Biological Data." Web.
1. Lambros Mertzanis, Athina Panotopoulou, Maria Skoularidou. Deep Tree Models for ‘Big’ Biological Data [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3320

Invisible Geo-Location Signature in a Single Image


Geo-tagging images of interest is increasingly important to law enforcement, national security, and journalism. Many images today do not carry location tags that are trustworthy and resilient to tampering; and the landmark-based visual clues may not be readily present in every image, especially in those taken indoors. In this paper, we exploit an invisible signature from the power grid, the Electric Network Frequency (ENF) signal, which can be inherently recorded in a sensing stream at the time of capturing and carries useful location information.

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27 April 2018 - 6:28pm
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[1] , "Invisible Geo-Location Signature in a Single Image", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3186. Accessed: Nov. 17, 2018.
@article{3186-18,
url = {http://sigport.org/3186},
author = { },
publisher = {IEEE SigPort},
title = {Invisible Geo-Location Signature in a Single Image},
year = {2018} }
TY - EJOUR
T1 - Invisible Geo-Location Signature in a Single Image
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3186
ER -
. (2018). Invisible Geo-Location Signature in a Single Image. IEEE SigPort. http://sigport.org/3186
, 2018. Invisible Geo-Location Signature in a Single Image. Available at: http://sigport.org/3186.
. (2018). "Invisible Geo-Location Signature in a Single Image." Web.
1. . Invisible Geo-Location Signature in a Single Image [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3186

Paper 3671-QUANTITATIVE LUNG NODULE ANALYSIS SYSTEM (NAS) FROM CHEST CT Poster

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Authors:
Amal Farag, Salwa Elshazly, Asem Ali, Islam Alkabbany, Albert Seow and Aly Farag
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15 April 2018 - 12:20am
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[1] Amal Farag, Salwa Elshazly, Asem Ali, Islam Alkabbany, Albert Seow and Aly Farag, "Paper 3671-QUANTITATIVE LUNG NODULE ANALYSIS SYSTEM (NAS) FROM CHEST CT Poster", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2875. Accessed: Nov. 17, 2018.
@article{2875-18,
url = {http://sigport.org/2875},
author = {Amal Farag; Salwa Elshazly; Asem Ali; Islam Alkabbany; Albert Seow and Aly Farag },
publisher = {IEEE SigPort},
title = {Paper 3671-QUANTITATIVE LUNG NODULE ANALYSIS SYSTEM (NAS) FROM CHEST CT Poster},
year = {2018} }
TY - EJOUR
T1 - Paper 3671-QUANTITATIVE LUNG NODULE ANALYSIS SYSTEM (NAS) FROM CHEST CT Poster
AU - Amal Farag; Salwa Elshazly; Asem Ali; Islam Alkabbany; Albert Seow and Aly Farag
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2875
ER -
Amal Farag, Salwa Elshazly, Asem Ali, Islam Alkabbany, Albert Seow and Aly Farag. (2018). Paper 3671-QUANTITATIVE LUNG NODULE ANALYSIS SYSTEM (NAS) FROM CHEST CT Poster. IEEE SigPort. http://sigport.org/2875
Amal Farag, Salwa Elshazly, Asem Ali, Islam Alkabbany, Albert Seow and Aly Farag, 2018. Paper 3671-QUANTITATIVE LUNG NODULE ANALYSIS SYSTEM (NAS) FROM CHEST CT Poster. Available at: http://sigport.org/2875.
Amal Farag, Salwa Elshazly, Asem Ali, Islam Alkabbany, Albert Seow and Aly Farag. (2018). "Paper 3671-QUANTITATIVE LUNG NODULE ANALYSIS SYSTEM (NAS) FROM CHEST CT Poster." Web.
1. Amal Farag, Salwa Elshazly, Asem Ali, Islam Alkabbany, Albert Seow and Aly Farag. Paper 3671-QUANTITATIVE LUNG NODULE ANALYSIS SYSTEM (NAS) FROM CHEST CT Poster [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2875

Fast dictionary-based approach for mass spectrometry data analysis


Mass spectrometry (MS) is a fundamental technology of analytical chemistry for measuring the structure of molecules, with many application fields such as clinical biomarker analysis or pharmacokinetics. In the context of proteomic analysis with MS, the superposition of the isotopic patterns of different proteins, in various charge-states produces MS spectra difficult to decipher. The complexity of the pattern models and the large size of the data again increase the difficulty of the analysis step.

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Authors:
Chouzenoux Emilie, Delsuc Marc André
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20 April 2018 - 12:12pm
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[1] Chouzenoux Emilie, Delsuc Marc André, "Fast dictionary-based approach for mass spectrometry data analysis", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2779. Accessed: Nov. 17, 2018.
@article{2779-18,
url = {http://sigport.org/2779},
author = {Chouzenoux Emilie; Delsuc Marc André },
publisher = {IEEE SigPort},
title = {Fast dictionary-based approach for mass spectrometry data analysis},
year = {2018} }
TY - EJOUR
T1 - Fast dictionary-based approach for mass spectrometry data analysis
AU - Chouzenoux Emilie; Delsuc Marc André
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2779
ER -
Chouzenoux Emilie, Delsuc Marc André. (2018). Fast dictionary-based approach for mass spectrometry data analysis. IEEE SigPort. http://sigport.org/2779
Chouzenoux Emilie, Delsuc Marc André, 2018. Fast dictionary-based approach for mass spectrometry data analysis. Available at: http://sigport.org/2779.
Chouzenoux Emilie, Delsuc Marc André. (2018). "Fast dictionary-based approach for mass spectrometry data analysis." Web.
1. Chouzenoux Emilie, Delsuc Marc André. Fast dictionary-based approach for mass spectrometry data analysis [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2779

Driver estimation in non-linear autoregressive models


In non-linear autoregressive models, the time dependency of coefficients is often driven by a particular time-series which is not given and thus has to be estimated from the data. To allow model evaluation on a validation set, we describe a parametric approach for such driver estimation. After estimating the driver as a weighted sum of potential drivers, we use it in a non-linear autoregressive model with a polynomial parametrization. Using gradient descent, we optimize the linear filter extracting the driver, outperforming a typical grid-search on predefined filters.

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Authors:
Tom Dupre la Tour, Yves Grenier, Alexandre Gramfort
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13 April 2018 - 5:21am
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[1] Tom Dupre la Tour, Yves Grenier, Alexandre Gramfort, "Driver estimation in non-linear autoregressive models", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2666. Accessed: Nov. 17, 2018.
@article{2666-18,
url = {http://sigport.org/2666},
author = {Tom Dupre la Tour; Yves Grenier; Alexandre Gramfort },
publisher = {IEEE SigPort},
title = {Driver estimation in non-linear autoregressive models},
year = {2018} }
TY - EJOUR
T1 - Driver estimation in non-linear autoregressive models
AU - Tom Dupre la Tour; Yves Grenier; Alexandre Gramfort
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2666
ER -
Tom Dupre la Tour, Yves Grenier, Alexandre Gramfort. (2018). Driver estimation in non-linear autoregressive models. IEEE SigPort. http://sigport.org/2666
Tom Dupre la Tour, Yves Grenier, Alexandre Gramfort, 2018. Driver estimation in non-linear autoregressive models. Available at: http://sigport.org/2666.
Tom Dupre la Tour, Yves Grenier, Alexandre Gramfort. (2018). "Driver estimation in non-linear autoregressive models." Web.
1. Tom Dupre la Tour, Yves Grenier, Alexandre Gramfort. Driver estimation in non-linear autoregressive models [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2666

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