AIMC Topic: Bayes Theorem

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Approximate Bayesian MLP regularization for regression in the presence of noise.

Neural networks : the official journal of the International Neural Network Society
We present a novel regularization method for a multilayer perceptron (MLP) that learns a regression function in the presence of noise regardless of how smooth the function is. Unlike general MLP regularization methods assuming that a regression funct...

An approach for deciphering patient-specific variations with application to breast cancer molecular expression profiles.

Journal of biomedical informatics
Several studies have successfully used molecular expression profiling in conjunction with classification techniques for discerning distinct disease groups. However, a majority of these studies do not provide sufficient insights into potential patient...

Dynamic Bayesian Network for Accurate Detection of Peptides from Tandem Mass Spectra.

Journal of proteome research
A central problem in mass spectrometry analysis involves identifying, for each observed tandem mass spectrum, the corresponding generating peptide. We present a dynamic Bayesian network (DBN) toolkit that addresses this problem by using a machine lea...

ADMET Evaluation in Drug Discovery. 16. Predicting hERG Blockers by Combining Multiple Pharmacophores and Machine Learning Approaches.

Molecular pharmaceutics
Blockade of human ether-à-go-go related gene (hERG) channel by compounds may lead to drug-induced QT prolongation, arrhythmia, and Torsades de Pointes (TdP), and therefore reliable prediction of hERG liability in the early stages of drug design is qu...

Machine Learning Approach to Automated Quality Identification of Human Induced Pluripotent Stem Cell Colony Images.

Computational and mathematical methods in medicine
The focus of this research is on automated identification of the quality of human induced pluripotent stem cell (iPSC) colony images. iPS cell technology is a contemporary method by which the patient's cells are reprogrammed back to stem cells and ar...

Extracting Information from Electronic Medical Records to Identify the Obesity Status of a Patient Based on Comorbidities and Bodyweight Measures.

Journal of medical systems
Obesity is a chronic disease with an increasing impact on the world's population. In this work, we present a method of identifying obesity automatically using text mining techniques and information related to body weight measures and obesity comorbid...

Using machine learning methods for predicting inhospital mortality in patients undergoing open repair of abdominal aortic aneurysm.

Journal of biomedical informatics
An abdominal aortic aneurysm is an abnormal dilatation of the aortic vessel at abdominal level. This disease presents high rate of mortality and complications causing a decrease in the quality of life and increasing the cost of treatment. To estimate...

Using automatically extracted information from mammography reports for decision-support.

Journal of biomedical informatics
OBJECTIVE: To evaluate a system we developed that connects natural language processing (NLP) for information extraction from narrative text mammography reports with a Bayesian network for decision-support about breast cancer diagnosis. The ultimate g...

Machine Learning Model Analysis and Data Visualization with Small Molecules Tested in a Mouse Model of Mycobacterium tuberculosis Infection (2014-2015).

Journal of chemical information and modeling
The renewed urgency to develop new treatments for Mycobacterium tuberculosis (Mtb) infection has resulted in large-scale phenotypic screening and thousands of new active compounds in vitro. The next challenge is to identify candidates to pursue in a ...

Classification of patients with MCI and AD from healthy controls using directed graph measures of resting-state fMRI.

Behavioural brain research
Brain network alterations in patients with Alzheimer's disease (AD) has been the subject of much investigation, but the biological mechanisms underlying these alterations remain poorly understood. Here, we aim to identify the changes in brain network...