AIMC Topic: Machine Learning

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Quantitative ultrasound assessment of breast tumor response to chemotherapy using a multi-parameter approach.

Oncotarget
PURPOSE: This study demonstrated the ability of quantitative ultrasound (QUS) parameters in providing an early prediction of tumor response to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer (LABC).

The power of data mining in diagnosis of childhood pneumonia.

Journal of the Royal Society, Interface
Childhood pneumonia is the leading cause of death of children under the age of 5 years globally. Diagnostic information on the presence of infection, severity and aetiology (bacterial versus viral) is crucial for appropriate treatment. However, the d...

A mathematical framework for virtual IMRT QA using machine learning.

Medical physics
PURPOSE: It is common practice to perform patient-specific pretreatment verifications to the clinical delivery of IMRT. This process can be time-consuming and not altogether instructive due to the myriad sources that may produce a failing result. The...

Comparison of Feature Selection Techniques in Machine Learning for Anatomical Brain MRI in Dementia.

Neuroinformatics
We present a comparative split-half resampling analysis of various data driven feature selection and classification methods for the whole brain voxel-based classification analysis of anatomical magnetic resonance images. We compared support vector ma...

What time is it? Deep learning approaches for circadian rhythms.

Bioinformatics (Oxford, England)
MOTIVATION: Circadian rhythms date back to the origins of life, are found in virtually every species and every cell, and play fundamental roles in functions ranging from metabolism to cognition. Modern high-throughput technologies allow the measureme...

Classifying and segmenting microscopy images with deep multiple instance learning.

Bioinformatics (Oxford, England)
MOTIVATION: High-content screening (HCS) technologies have enabled large scale imaging experiments for studying cell biology and for drug screening. These systems produce hundreds of thousands of microscopy images per day and their utility depends on...

PHOCOS: inferring multi-feature phenotypic crosstalk networks.

Bioinformatics (Oxford, England)
MOTIVATION: Quantification of cellular changes to perturbations can provide a powerful approach to infer crosstalk among molecular components in biological networks. Existing crosstalk inference methods conduct network-structure learning based on a s...

Novel applications of multitask learning and multiple output regression to multiple genetic trait prediction.

Bioinformatics (Oxford, England)
UNLABELLED: Given a set of biallelic molecular markers, such as SNPs, with genotype values encoded numerically on a collection of plant, animal or human samples, the goal of genetic trait prediction is to predict the quantitative trait values by simu...

Fast metabolite identification with Input Output Kernel Regression.

Bioinformatics (Oxford, England)
MOTIVATION: An important problematic of metabolomics is to identify metabolites using tandem mass spectrometry data. Machine learning methods have been proposed recently to solve this problem by predicting molecular fingerprint vectors and matching t...