AIMC Topic: Machine Learning

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Prospective identification of hematopoietic lineage choice by deep learning.

Nature methods
Differentiation alters molecular properties of stem and progenitor cells, leading to changes in their shape and movement characteristics. We present a deep neural network that prospectively predicts lineage choice in differentiating primary hematopoi...

Feature Extraction and Classification of EHG between Pregnancy and Labour Group Using Hilbert-Huang Transform and Extreme Learning Machine.

Computational and mathematical methods in medicine
Preterm birth (PTB) is the leading cause of perinatal mortality and long-term morbidity, which results in significant health and economic problems. The early detection of PTB has great significance for its prevention. The electrohysterogram (EHG) rel...

HemoPred: a web server for predicting the hemolytic activity of peptides.

Future medicinal chemistry
AIM: Toxicity arising from hemolytic activity of peptides hinders its further progress as drug candidates.

Machine Learning for Medical Imaging.

Radiographics : a review publication of the Radiological Society of North America, Inc
Machine learning is a technique for recognizing patterns that can be applied to medical images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be misapplied. Machine learning typically begins with the machine lear...

Revealing the inventory of type III effectors in Pantoea agglomerans gall-forming pathovars using draft genome sequences and a machine-learning approach.

Molecular plant pathology
Pantoea agglomerans, a widespread epiphytic bacterium, has evolved into a hypersensitive response and pathogenicity (hrp)-dependent and host-specific gall-forming pathogen by the acquisition of a pathogenicity plasmid containing a type III secretion ...

Changing techniques in crop plant classification: molecularization at the National Institute of Agricultural Botany during the 1980s.

Annals of science
Modern methods of analysing biological materials, including protein and DNA sequencing, are increasingly the objects of historical study. Yet twentieth-century taxonomic techniques have been overlooked in one of their most important contexts: agricul...

Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning.

Radiological physics and technology
Half a century ago, the term "computer-aided diagnosis" (CAD) was introduced in the scientific literature. Pulmonary imaging, with chest radiography and computed tomography, has always been one of the focus areas in this field. In this study, I descr...

Identifying Future Drinkers: Behavioral Analysis of Monkeys Initiating Drinking to Intoxication is Predictive of Future Drinking Classification.

Alcoholism, clinical and experimental research
BACKGROUND: The Monkey Alcohol Tissue Research Resource (MATRR) is a repository and analytics platform for detailed data derived from well-documented nonhuman primate (NHP) alcohol self-administration studies. This macaque model has demonstrated cate...

SoilGrids250m: Global gridded soil information based on machine learning.

PloS one
This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June 2016 update). SoilGrids provides global predictions for standard numeric soil properties (...

Noise-robust unsupervised spike sorting based on discriminative subspace learning with outlier handling.

Journal of neural engineering
OBJECTIVE: Spike sorting is a fundamental preprocessing step for many neuroscience studies which rely on the analysis of spike trains. Most of the feature extraction and dimensionality reduction techniques that have been used for spike sorting give a...