A PHP Error was encountered

Severity: Warning

Message: fopen(/var/lib/php/sessions/ci_sessionbuen5vecg7nqohin65hcg16ivk86ifqo): Failed to open stream: No space left on device

Filename: drivers/Session_files_driver.php

Line Number: 177

Backtrace:

File: /var/www/html/index.php
Line: 316
Function: require_once

A PHP Error was encountered

Severity: Warning

Message: session_start(): Failed to read session data: user (path: /var/lib/php/sessions)

Filename: Session/Session.php

Line Number: 137

Backtrace:

File: /var/www/html/index.php
Line: 316
Function: require_once

Noise factor analysis for cDNA microarrays. | LitMetric

Noise factor analysis for cDNA microarrays.

J Biomed Opt

Texas A&M University, Department of Electrical Engineering, 111D Zachry, College Station, Texas 77843-3128, USA.

Published: January 2005

A microarray-image model is used that takes into account many factors, including spot morphology, signal strength, background fluorescent noise, and shape and surface degradation. The model yields synthetic images whose appearance and quality reflect that of real microarray images. The model is used to link noise factors to the fidelity of signal extraction with respect to a standard image-extraction algorithm. Of particular interest is the identification of the noise factors and their interactions that significantly degrade the ability to accurately detect the true gene-expression signal. This study uses statistical criteria in conjunction with the simulation of various noise conditions to better understand the noise influence on signal extraction for cDNA microarray images. It proposes a paradigm that is implemented in software. It specifically considers certain kinds of noise in the noise model and sets these at certain levels; however, one can choose other types of noise or use different noise levels. In sum, it develops a statistical package that can work in conjunction with the existing image simulation toolbox.

Download full-text PDF

Source
http://dx.doi.org/10.1117/1.1755232DOI Listing

Publication Analysis

Top Keywords

noise
10
microarray images
8
noise factors
8
signal extraction
8
noise noise
8
noise factor
4
factor analysis
4
analysis cdna
4
cdna microarrays
4
microarrays microarray-image
4

Similar Publications

Optimizing fractionation schedules for de-escalation radiotherapy in head and neck cancers using deep reinforcement learning.

Radiother Oncol

March 2025

Department of Radiation Oncology, The First Affiliated Hospital, Zhejiang University School of Medicine, No. 79 Qingchun Road, Hangzhou, 310003, Zhejiang, China; Cancer Center, Zhejiang University, 866 Yuhangtang Road, Hangzhou, 310027, Zhejiang, China. Electronic address:

Purpose: Patients with locally-advanced head and neck squamous cell carcinomas(HNSCCs), particularly those related to human papillomavirus(HPV), often achieve good locoregional control(LRC), yet they suffer significant toxicities from standard chemoradiotherapy. This study aims to optimize the daily dose fractionation based on individual responses to radiotherapy(RT), minimizing toxicity while maintaining a low risk of LRC failure.

Method: A virtual environment was developed to simulate tumor dynamics under RT for optimizing dose schedules.

View Article and Find Full Text PDF

Spectral detection based on spectrophotometry is an important multi-component concentration detection method. At present, commonly used machine learning methods in the field of spectral analysis can only be used for prediction and cannot analyze how the concentration of each component affects the spectrum. In addition, for common spectral parallel drift in spectrophotometry, traditional derivative preprocessing methods are susceptible to noise and cannot reverse restore the original spectrum.

View Article and Find Full Text PDF

Background: Quantitative susceptibility mapping (QSM) is a post-processing magnetic resonance imaging (MRI) technique that extracts the distribution of tissue susceptibilities and holds significant promise in the study of neurological diseases. However, the ill-conditioned nature of dipole inversion often results in noise and artifacts during QSM reconstruction from the tissue field. Deep learning methods have shown great potential in addressing these issues; however, most existing approaches rely on basic U-net structures, leading to limited performances and reconstruction artifacts sometimes.

View Article and Find Full Text PDF

Background: Panel detectors have the potential to provide a flexible, modular approach to Positron Emission Tomography (PET), enabling customization to meet patient-specific needs and scan objectives. The panel design allows detectors to be positioned close to the patient, aiming to enhance sensitivity and spatial resolution through improved geometric coverage and reduced noncollinearity blurring. Parallax error can be mitigated using depth of interaction (DOI) information.

View Article and Find Full Text PDF

The high-sensitivity capabilities of laser-induced fluorescence (LIF) detection continuously promote the development of various labels with different fluorescence properties. However, this strategy also requires the adaptation of existing detection systems to suit the excitation and emission characteristics of novel fluorescent tags. In this study, we adapted the LIF detector of the commercial capillary electrophoresis instrument to the specific fluorescence spectra of 2-aminoacridone labeled human milk oligosaccharides.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!