Machine learning applications in cancer prognosis and prediction
about
Risk factors assessment and risk prediction models in lung cancer screening candidatesA call for biological data mining approaches in epidemiologyCross-platform normalization of microarray and RNA-seq data for machine learning applicationsSupport vector machine model of developmental brain gene expression data for prioritization of Autism risk gene candidates.A robust data scaling algorithm to improve classification accuracies in biomedical dataKnowledge about the presence or absence of miRNA isoforms (isomiRs) can successfully discriminate amongst 32 TCGA cancer typesMachine learning and microsimulation techniques on the prognosis of dementia: A systematic literature review.A machine learning strategy for predicting localization of post-translational modification sites in protein-protein interacting regionsSVM and SVM Ensembles in Breast Cancer Prediction.Multi-vehicle detection with identity awareness using cascade Adaboost and Adaptive Kalman filter for driver assistant systemPrediction of Early Recurrence of Liver Cancer by a Novel Discrete Bayes Decision Rule for Personalized Medicine.Learning the Relationship between the Primary Structure of HIV Envelope Glycoproteins and Neutralization Activity of Particular Antibodies by Using Artificial Neural Networks.A Hybrid Computer-aided-diagnosis System for Prediction of Breast Cancer Recurrence (HPBCR) Using Optimized Ensemble Learning.Interaction of MRE11 and Clinicopathologic Characteristics in Recurrence of Breast Cancer: Individual and Cumulated Receiver Operating Characteristic Analyses.Combinatorial Ranking of Gene Sets to Predict Disease Relapse: The Retinoic Acid Pathway in Early Prostate Cancer.Systematic evaluation of supervised classifiers for fecal microbiota-based prediction of colorectal cancer.PRODIGE: PRediction models in prOstate cancer for personalized meDIcine challenGE.A DEEP LEARNING APPROACH FOR CANCER DETECTION AND RELEVANT GENE IDENTIFICATION.Machine learning applications for prediction of relapse in childhood acute lymphoblastic leukemia.Metabolomics and Integrative Omics for the Development of Thai Traditional Medicine.Application of Fuzzy Logic in Oral Cancer Risk Assessment.'Omics' and endocrine-disrupting chemicals - new paths forward.Systems approaches in osteoarthritis: Identifying routes to novel diagnostic and therapeutic strategiesMachine Learning Approaches Toward Building Predictive Models for Small Molecule Modulators of miRNA and Its Utility in Virtual Screening of Molecular Databases.Use of emergency department electronic medical records for automated epidemiological surveillance of suicide attempts: a French pilot study.Optimization of the Production of Inactivated Clostridium novyi Type B Vaccine Using Computational Intelligence Techniques.Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models.Molecular classification of thyroid lesions by combined testing for miRNA gene expression and somatic gene alterations.Predicting local malaria exposure using a Lasso-based two-level cross validation algorithm.Pulmonary vasculature in dogs assessed by three-dimensional fractal analysis and chemometrics.Machine learning to detect signatures of disease in liquid biopsies - a user's guide.An introduction and overview of machine learning in neurosurgical care.The revolution of personalized psychiatry: will technology make it happen sooner?Incremental role of resting myocardial computed tomography perfusion for predicting physiologically significant coronary artery disease: A machine learning approach.Group analysis identifies differentially elevated biomarkers with distinct outcomes for advanced acute kidney injury in cardiac surgery.Comparison of Basic and Ensemble Data Mining Methods in Predicting 5-Year Survival of Colorectal Cancer Patients.The Ability of Different Imputation Methods to Preserve the Significant Genes and Pathways in Cancer.Mass Spectrometry Imaging: A Review of Emerging Advancements and Future Insights.Identification of prognostic signature in cancer based on DNA methylation interaction network.Identification of novel prognosis-related genes associated with cancer using integrative network analysis.
P2860
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P2860
Machine learning applications in cancer prognosis and prediction
description
2014 nî lūn-bûn
@nan
2014 թուականի Նոյեմբերին հրատարակուած գիտական յօդուած
@hyw
2014 թվականի նոյեմբերին հրատարակված գիտական հոդված
@hy
2014年の論文
@ja
2014年論文
@yue
2014年論文
@zh-hant
2014年論文
@zh-hk
2014年論文
@zh-mo
2014年論文
@zh-tw
2014年论文
@wuu
name
Machine learning applications in cancer prognosis and prediction
@ast
Machine learning applications in cancer prognosis and prediction
@en
type
label
Machine learning applications in cancer prognosis and prediction
@ast
Machine learning applications in cancer prognosis and prediction
@en
prefLabel
Machine learning applications in cancer prognosis and prediction
@ast
Machine learning applications in cancer prognosis and prediction
@en
P2093
P2860
P1476
Machine learning applications in cancer prognosis and prediction
@en
P2093
Dimitrios I Fotiadis
Konstantina Kourou
Konstantinos P Exarchos
Michalis V Karamouzis
Themis P Exarchos
P2860
P356
10.1016/J.CSBJ.2014.11.005
P577
2014-11-15T00:00:00Z