AIMC Topic: Support Vector Machine

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ADMET Evaluation in Drug Discovery. Part 17: Development of Quantitative and Qualitative Prediction Models for Chemical-Induced Respiratory Toxicity.

Molecular pharmaceutics
As a dangerous end point, respiratory toxicity can cause serious adverse health effects and even death. Meanwhile, it is a common and traditional issue in occupational and environmental protection. Pharmaceutical and chemical industries have a strong...

Predicting the Enzymatic Hydrolysis Half-lives of New Chemicals Using Support Vector Regression Models Based on Stepwise Feature Elimination.

Molecular informatics
The enzymatic hydrolysis of chemicals, which is important for in vitro drug metabolism assays, is an important indicator of drug stability profiles during drug discovery and development. Herein, we employed a stepwise feature elimination (SFE) method...

Towards affordable biomarkers of frontotemporal dementia: A classification study via network's information sharing.

Scientific reports
Developing effective and affordable biomarkers for dementias is critical given the difficulty to achieve early diagnosis. In this sense, electroencephalographic (EEG) methods offer promising alternatives due to their low cost, portability, and growin...

Bird sound spectrogram decomposition through Non-Negative Matrix Factorization for the acoustic classification of bird species.

PloS one
Feature extraction for Acoustic Bird Species Classification (ABSC) tasks has traditionally been based on parametric representations that were specifically developed for speech signals, such as Mel Frequency Cepstral Coefficients (MFCC). However, the ...

Feature fusion for lung nodule classification.

International journal of computer assisted radiology and surgery
PURPOSE: This article examines feature-based nodule description for the purpose of nodule classification in chest computed tomography scanning.

Classification of ECG heartbeats using nonlinear decomposition methods and support vector machine.

Computers in biology and medicine
Classifying electrocardiogram (ECG) heartbeats for arrhythmic risk prediction is a challenging task due to minute variations in the amplitude, duration and morphology of the ECG signal. In this paper, we propose two feature extraction approaches to c...

Fully automatic detection of lung nodules in CT images using a hybrid feature set.

Medical physics
PURPOSE: The aim of this study was to develop a novel technique for lung nodule detection using an optimized feature set. This feature set has been achieved after rigorous experimentation, which has helped in reducing the false positives significantl...

Machine learning based identification of protein-protein interactions using derived features of physiochemical properties and evolutionary profiles.

Artificial intelligence in medicine
Proteins are the central constitute of a cell or biological system. Proteins execute their functions by interacting with other molecules such as RNA, DNA and other proteins. The major functionality of protein-protein interactions (PPIs) is the execut...

Using multiclass classification to automate the identification of patient safety incident reports by type and severity.

BMC medical informatics and decision making
BACKGROUND: Approximately 10% of admissions to acute-care hospitals are associated with an adverse event. Analysis of incident reports helps to understand how and why incidents occur and can inform policy and practice for safer care. Unfortunately ou...