Cargando…

A Machine Learned Classifier That Uses Gene Expression Data to Accurately Predict Estrogen Receptor Status

BACKGROUND: Selecting the appropriate treatment for breast cancer requires accurately determining the estrogen receptor (ER) status of the tumor. However, the standard for determining this status, immunohistochemical analysis of formalin-fixed paraffin embedded samples, suffers from numerous technic...

Descripción completa

Detalles Bibliográficos
Autores principales:	Bastani, Meysam, Vos, Larissa, Asgarian, Nasimeh, Deschenes, Jean, Graham, Kathryn, Mackey, John, Greiner, Russell
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Public Library of Science 2013
Materias:	Research Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3846850/ https://www.ncbi.nlm.nih.gov/pubmed/24312637 http://dx.doi.org/10.1371/journal.pone.0082144

Ejemplares similares

ADHD-200 Global Competition: diagnosing ADHD using personal characteristic data can outperform resting state fMRI measurements
por: Brown, Matthew R. G., et al.
Publicado: (2012)

Kernel Principal Component Analysis for dimensionality reduction in fMRI-based diagnosis of ADHD
por: Sidhu, Gagan S., et al.
Publicado: (2012)

ETHNOPRED: a novel machine learning method for accurate continental and sub-continental ancestry identification and population stratification correction
por: Hajiloo, Mohsen, et al.
Publicado: (2013)

Machine learning accurately classifies age of toddlers based on eye tracking
por: Dalrymple, Kirsten A., et al.
Publicado: (2019)

Effects of Sample Size on Differential Gene Expression, Rank Order and Prediction Accuracy of a Gene Signature
por: Stretch, Cynthia, et al.
Publicado: (2013)

Machine Learning Techniques Accurately Classify Microbial Communities by Bacterial Vaginosis Characteristics
por: Beck, Daniel, et al.
Publicado: (2014)

Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data
por: Alakwaa, Fadhl M., et al.
Publicado: (2017)

Machine learning models using mobile game play accurately classify children with autism
por: Deveau, Nicholas, et al.
Publicado: (2022)

A machine learning approach utilizing DNA methylation as an accurate classifier of COVID-19 disease severity
por: Bowler, Scott, et al.
Publicado: (2022)

Support Vector Machine Classifier for Estrogen Receptor Positive and Negative Early-Onset Breast Cancer
por: Upstill-Goddard, Rosanna, et al.
Publicado: (2013)

Short k-mer abundance profiles yield robust machine learning features and accurate classifiers for RNA viruses
por: Alam, Md. Nafis Ul, et al.
Publicado: (2020)

Fast, Simple and Accurate Handwritten Digit Classification by Training Shallow Neural Network Classifiers with the ‘Extreme Learning Machine’ Algorithm
por: McDonnell, Mark D., et al.
Publicado: (2015)

Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection
por: Hussain, Lal, et al.
Publicado: (2020)

Machine learning techniques for classifying dangerous asteroids
por: Malakouti, Seyed Matin, et al.
Publicado: (2023)

Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study
por: Visweswaran, Shyam, et al.
Publicado: (2020)

Machine Learning Reduced Gene/Non-Coding RNA Features That Classify Schizophrenia Patients Accurately and Highlight Insightful Gene Clusters
por: Liu, Yichuan, et al.
Publicado: (2021)

Using Machine Learning to Classify Individuals With Alcohol Use Disorder Based on Treatment Seeking Status
por: Lee, Mary R., et al.
Publicado: (2019)

An Introduction to Machine Learning Approaches for Biomedical Research
por: Jovel, Juan, et al.
Publicado: (2021)

Deep learning accurately classifies elbow joint effusion in adult and pediatric radiographs
por: Huhtanen, Jarno T., et al.
Publicado: (2022)

Performance of Machine Learning Classifiers in Classifying Stunting among Under-Five Children in Zambia
por: Chilyabanyama, Obvious Nchimunya, et al.
Publicado: (2022)

Using machine learning to classify temporal lobe epilepsy based on diffusion MRI
por: Del Gaizo, John, et al.
Publicado: (2017)

Supervised Machine Learning Applied to Wearable Sensor Data Can Accurately Classify Functional Fitness Exercises Within a Continuous Workout
por: Preatoni, Ezio, et al.
Publicado: (2020)

Machine learning accurately classifies neural responses to rhythmic speech vs. non-speech from 8-week-old infant EEG
por: Gibbon, Samuel, et al.
Publicado: (2021)

Metaverse and Healthcare: Machine Learning-Enabled Digital Twins of Cancer
por: Moztarzadeh, Omid, et al.
Publicado: (2023)

Classifying Drugs by their Arrhythmogenic Risk Using Machine Learning
por: Sahli-Costabal, Francisco, et al.
Publicado: (2020)

Framework for Testing Robustness of Machine Learning-Based Classifiers
por: Chuah, Joshua, et al.
Publicado: (2022)

Classifying grains using behaviour-informed machine learning
por: Laudari, Sudip, et al.
Publicado: (2022)

Machine learning magnetism classifiers from atomic coordinates
por: Merker, Helena A., et al.
Publicado: (2022)

Method for Classifying Schizophrenia Patients Based on Machine Learning
por: Soria, Carmen, et al.
Publicado: (2023)

Combining expert knowledge and machine-learning to classify herd types in livestock systems
por: Brock, Jonas, et al.
Publicado: (2021)

External validation of an opioid misuse machine learning classifier in hospitalized adult patients
por: Afshar, Majid, et al.
Publicado: (2021)

Passenger mutations accurately classify human tumors
por: Salvadores, Marina, et al.
Publicado: (2019)

GPCR_LigandClassify.py; a rigorous machine learning classifier for GPCR targeting compounds
por: Ahmed, Marawan, et al.
Publicado: (2021)

On the generalizability of resting-state fMRI machine learning classifiers
por: Huf, Wolfgang, et al.
Publicado: (2014)

Classifying kinase conformations using a machine learning approach
por: McSkimming, Daniel Ian, et al.
Publicado: (2017)

Prediction of plant lncRNA by ensemble machine learning classifiers
por: Simopoulos, Caitlin M. A., et al.
Publicado: (2018)

Multi-Level Comparison of Machine Learning Classifiers and Their Performance Metrics
por: Rácz, Anita, et al.
Publicado: (2019)

Risk-Aware Machine Learning Classifier for Skin Lesion Diagnosis
por: Mobiny, Aryan, et al.
Publicado: (2019)

Enhancing the Biological Relevance of Machine Learning Classifiers for Reverse Vaccinology
por: Heinson, Ashley I., et al.
Publicado: (2017)

A biochemically-interpretable machine learning classifier for microbial GWAS
por: Kavvas, Erol S., et al.
Publicado: (2020)

Cannot write session to /tmp/vufind_sessions/sess_q55l5i2q0cba2onc3qfi1t5e65