[HTML][HTML] A machine-learning-based prediction method for hypertension outcomes based on medical data
W Chang, Y Liu, Y Xiao, X Yuan, X Xu, S Zhang… - Diagnostics, 2019 - mdpi.com
The outcomes of hypertension refer to the death or serious complications (such as
myocardial infarction or stroke) that may occur in patients with hypertension. The outcomes …
myocardial infarction or stroke) that may occur in patients with hypertension. The outcomes …
[HTML][HTML] IoT data analytic algorithms on edge-cloud infrastructure: A review
The adoption of Internet of Things (IoT) sensing devices is growing rapidly due to their ability
to provide real-time services. However, it is constrained by limited data storage and …
to provide real-time services. However, it is constrained by limited data storage and …
[HTML][HTML] Selecting feature subsets based on SVM-RFE and the overlapping ratio with applications in bioinformatics
X Lin, C Li, Y Zhang, B Su, M Fan, H Wei - Molecules, 2017 - mdpi.com
Feature selection is an important topic in bioinformatics. Defining informative features from
complex high dimensional biological data is critical in disease study, drug development, etc …
complex high dimensional biological data is critical in disease study, drug development, etc …
Analysis of lncRNA-associated ceRNA network reveals potential lncRNA biomarkers in human colon adenocarcinoma
Z Zhang, W Qian, S Wang, D Ji, Q Wang, J Li… - Cellular Physiology and …, 2018 - karger.com
Abstract Background/Aims: Long non-coding RNAs (lncRNAs) acting as competing
endogenous RNAs (ceRNAs) play significant roles in the development of tumors, but the …
endogenous RNAs (ceRNAs) play significant roles in the development of tumors, but the …
Machine learning methods in the computational biology of cancer
M Vidyasagar - Proceedings of the Royal Society A …, 2014 - royalsocietypublishing.org
The objectives of this Perspective paper are to review some recent advances in sparse
feature selection for regression and classification, as well as compressed sensing, and to …
feature selection for regression and classification, as well as compressed sensing, and to …
[HTML][HTML] sigFeature: Novel Significant Feature Selection Method for Classification of Gene Expression Data Using Support Vector Machine and t Statistic
Biological data are accumulating at a faster rate, but interpreting them still remains a
problem. Classifying biological data into distinct groups is the first step in understanding …
problem. Classifying biological data into distinct groups is the first step in understanding …
A gene signature for breast cancer prognosis using support vector machine
Breast cancer is a common disease in elderly women. With the development of microarray
technique, discovering gene signature became a powerful approach in predicting survival of …
technique, discovering gene signature became a powerful approach in predicting survival of …
[HTML][HTML] Prediction of chronic periodontitis severity using machine learning models based on salivary bacterial copy number
EH Kim, S Kim, HJ Kim, H Jeong, J Lee… - Frontiers in Cellular …, 2020 - frontiersin.org
Periodontitis is a widespread chronic inflammatory disease caused by interactions between
periodontal bacteria and homeostasis in the host. We aimed to investigate the performance …
periodontal bacteria and homeostasis in the host. We aimed to investigate the performance …
Diagnosis of periodontal diseases using different classification algorithms: a preliminary study
FO Ozden, O Ozgonenel, B Ozden… - Nigerian journal of clinical …, 2015 - ajol.info
Objective: The purpose of the proposed study was to develop an identification unit for
classifying periodontal diseases using support vector machine (SVM), decision tree (DT) …
classifying periodontal diseases using support vector machine (SVM), decision tree (DT) …
[HTML][HTML] Hybrid multivariate pattern analysis combined with extreme learning machine for Alzheimer's dementia diagnosis using multi-measure rs-fMRI spatial patterns
Background Early diagnosis of Alzheimer's disease (AD) and Mild Cognitive Impairment
(MCI) is essential for timely treatment. Machine learning and multivariate pattern analysis …
(MCI) is essential for timely treatment. Machine learning and multivariate pattern analysis …