AIMC Topic: Machine Learning

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Quality of clinical brain tumor MR spectra judged by humans and machine learning tools.

Magnetic resonance in medicine
PURPOSE: To investigate and compare human judgment and machine learning tools for quality assessment of clinical MR spectra of brain tumors.

Visualizing deep neural network by alternately image blurring and deblurring.

Neural networks : the official journal of the International Neural Network Society
Visualization from trained deep neural networks has drawn massive public attention in recent. One of the visualization approaches is to train images maximizing the activation of specific neurons. However, directly maximizing the activation would lead...

A dictionary learning approach for human sperm heads classification.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: To diagnose infertility in men, semen analysis is conducted in which sperm morphology is one of the factors that are evaluated. Since manual assessment of sperm morphology is time-consuming and subjective, automatic classifi...

Reconstructing cancer drug response networks using multitask learning.

BMC systems biology
BACKGROUND: Translating in vitro results to clinical tests is a major challenge in systems biology. Here we present a new Multi-Task learning framework which integrates thousands of cell line expression experiments to reconstruct drug specific respon...

Predicting Organ Toxicity Using in Vitro Bioactivity Data and Chemical Structure.

Chemical research in toxicology
Animal testing alone cannot practically evaluate the health hazard posed by tens of thousands of environmental chemicals. Computational approaches making use of high-throughput experimental data may provide more efficient means to predict chemical to...

Development of A Machine Learning Algorithm to Classify Drugs Of Unknown Fetal Effect.

Scientific reports
Many drugs commonly prescribed during pregnancy lack a fetal safety recommendation - called FDA 'category C' drugs. This study aims to classify these drugs into harmful and safe categories using knowledge gained from chemoinformatics (i.e., pharmacol...

Systematic Identification of Machine-Learning Models Aimed to Classify Critical Residues for Protein Function from Protein Structure.

Molecules (Basel, Switzerland)
Protein structure and protein function should be related, yet the nature of this relationship remains unsolved. Mapping the critical residues for protein function with protein structure features represents an opportunity to explore this relationship,...

Self-Taught Learning Based on Sparse Autoencoder for E-Nose in Wound Infection Detection.

Sensors (Basel, Switzerland)
For an electronic nose (E-nose) in wound infection distinguishing, traditional learning methods have always needed large quantities of labeled wound infection samples, which are both limited and expensive; thus, we introduce self-taught learning comb...

Learning ensemble classifiers for diabetic retinopathy assessment.

Artificial intelligence in medicine
Diabetic retinopathy is one of the most common comorbidities of diabetes. Unfortunately, the recommended annual screening of the eye fundus of diabetic patients is too resource-consuming. Therefore, it is necessary to develop tools that may help doct...