Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project

iRSpot-EL: identify recombination spots with an ensemble learning approach

(2016) Bin Liu et al.   BIOINFORMATICS

iDHS-EL: identifying DNase I hypersensitive sites by fusing three different modes of pseudo nucleotide composition into an ensemble learning framework

(2016) Bin Liu et al.   BIOINFORMATICS

Identification of DNA-Binding Proteins by Combining Auto-Cross Covariance Transformation and Ensemble Learning

(2016) Bin Liu et al.   IEEE TRANSACTIONS ON NANOBIOSCIENCE

Cardiorespiratory Fitness and Incident Diabetes: The FIT (Henry Ford ExercIse Testing) Project

(2015) Stephen P. Juraschek et al.   DIABETES CARE

Prevalence and correlates of diagnosed and undiagnosed type 2 diabetes mellitus and pre-diabetes in older adults: Findings from the Irish Longitudinal Study on Ageing (TILDA)

(2015) S. Leahy et al.   DIABETES RESEARCH AND CLINICAL PRACTICE

imDC: an ensemble learning method for imbalanced classification with miRNA data

(2015) C.Y. Wang et al.   GENETICS AND MOLECULAR RESEARCH

iMiRNA-PseDPC: microRNA precursor identification with a pseudo distance-pair composition approach

(2015) Bin Liu et al.   JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS

Mortality rates and the causes of death related to diabetes mellitus in Shanghai Songjiang District: an 11-year retrospective analysis of death certificates

(2015) Meiying Zhu et al.   BMC Endocrine Disorders

Identification of Real MicroRNA Precursors with a Pseudo Structure Status Composition Approach

(2015) Bin Liu et al.   PLoS One

nDNA-prot: identification of DNA-binding proteins based on unbalanced classification

(2014) Li Song et al.   BMC BIOINFORMATICS

Rationale and Design of the Henry Ford ExercIse Testing Project (The FIT Project)

(2014) Mouaz H. Al-Mallah et al.   CLINICAL CARDIOLOGY

Predictive models to assess risk of type 2 diabetes, hypertension and comorbidity: machine-learning algorithms and validation using national health data from Kuwait—a cohort study

(2013) Bassam Farran et al.   BMJ Open

Training and assessing classification rules with imbalanced data

(2012) Giovanna Menardi et al.   DATA MINING AND KNOWLEDGE DISCOVERY

Comparison of three data mining models for predicting diabetes or prediabetes by risk factors

(2012) Xue-Hui Meng et al.   KAOHSIUNG JOURNAL OF MEDICAL SCIENCES

Prevalence of Type 2 Diabetes in the States of The Co-Operation Council for the Arab States of the Gulf: A Systematic Review

(2012) Layla Alhyas et al.   PLoS One

Estimating classification error rate: Repeated cross-validation, repeated hold-out and bootstrap

(2009) Ji-Hyun Kim   COMPUTATIONAL STATISTICS & DATA ANALYSIS

Learning from Imbalanced Data

(2009) Haibo He et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods

(2008) Clifford R. Jack et al.   JOURNAL OF MAGNETIC RESONANCE IMAGING

Vigorous Exercise, Fitness and Incident Hypertension, High Cholesterol, and Diabetes

(2008) PAUL T. WILLIAMS   MEDICINE AND SCIENCE IN SPORTS AND EXERCISE