AIMC Topic: Machine Learning

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ColocML: machine learning quantifies co-localization between mass spectrometry images.

Bioinformatics (Oxford, England)
MOTIVATION: Imaging mass spectrometry (imaging MS) is a prominent technique for capturing distributions of molecules in tissue sections. Various computational methods for imaging MS rely on quantifying spatial correlations between ion images, referre...

Machine Learning Prediction of Postoperative Emergency Department Hospital Readmission.

Anesthesiology
BACKGROUND: Although prediction of hospital readmissions has been studied in medical patients, it has received relatively little attention in surgical patient populations. Published predictors require information only available at the moment of disch...

SANPolyA: a deep learning method for identifying Poly(A) signals.

Bioinformatics (Oxford, England)
MOTIVATION: Polyadenylation plays a regulatory role in transcription. The recognition of polyadenylation signal (PAS) motif sequence is an important step in polyadenylation. In the past few years, some statistical machine learning-based and deep lear...

A deep learning architecture for metabolic pathway prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Understanding the mechanisms and structural mappings between molecules and pathway classes are critical for design of reaction predictors for synthesizing new molecules. This article studies the problem of prediction of classes of metabol...

Generative and discriminative model-based approaches to microscopic image restoration and segmentation.

Microscopy (Oxford, England)
Image processing is one of the most important applications of recent machine learning (ML) technologies. Convolutional neural networks (CNNs), a popular deep learning-based ML architecture, have been developed for image processing applications. Howev...

Implementing machine learning methods for imaging flow cytometry.

Microscopy (Oxford, England)
In this review, we focus on the applications of machine learning methods for analyzing image data acquired in imaging flow cytometry technologies. We propose that the analysis approaches can be categorized into two groups based on the type of data, r...

How Machine Learning Will Transform Biomedicine.

Cell
This Perspective explores the application of machine learning toward improved diagnosis and treatment. We outline a vision for how machine learning can transform three broad areas of biomedicine: clinical diagnostics, precision treatments, and health...