AIMC Topic: Machine Learning

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Conditional asymptotic inference for the kernel association test.

Bioinformatics (Oxford, England)
MOTIVATION: The kernel association test (KAT) is popular in biological studies for its ability to combine weak effects potentially of opposite direction. Its P-value is typically assessed via its (unconditional) asymptotic distribution. However, such...

Machine learning: novel bioinformatics approaches for combating antimicrobial resistance.

Current opinion in infectious diseases
PURPOSE OF REVIEW: Antimicrobial resistance (AMR) is a threat to global health and new approaches to combating AMR are needed. Use of machine learning in addressing AMR is in its infancy but has made promising steps. We reviewed the current literatur...

Intravoxel Incoherent Motion: Model-Free Determination of Tissue Type in Abdominal Organs Using Machine Learning.

Investigative radiology
PURPOSE: For diffusion data sets including low and high b-values, the intravoxel incoherent motion model is commonly applied to characterize tissue. The aim of the present study was to show that machine learning allows a model-free approach to determ...

Points of Significance: Machine learning: a primer.

Nature methods
Machine learning extracts general principles from observed examples without explicit instructions.

Integrated pillar scatterers for speeding up classification of cell holograms.

Optics express
The computational power required to classify cell holograms is a major limit to the throughput of label-free cell sorting based on digital holographic microscopy. In this work, a simple integrated photonic stage comprising a collection of silica pill...

The value of prior knowledge in machine learning of complex network systems.

Bioinformatics (Oxford, England)
MOTIVATION: Our overall goal is to develop machine-learning approaches based on genomics and other relevant accessible information for use in predicting how a patient will respond to a given proposed drug or treatment. Given the complexity of this pr...

An introduction to deep learning on biological sequence data: examples and solutions.

Bioinformatics (Oxford, England)
MOTIVATION: Deep neural network architectures such as convolutional and long short-term memory networks have become increasingly popular as machine learning tools during the recent years. The availability of greater computational resources, more data...

CAPi: Computational Model for Apicoplast Inhibitors Prediction Against Plasmodium Parasite.

Current computer-aided drug design
BACKGROUND: Discovery of apicoplast as a drug target offers a new direction in the development of novel anti-malarial compounds, especially against the drug-resistant strains. Drugs such as azithromycin were reported to block the apicoplast developme...

Plant phenomics: an overview of image acquisition technologies and image data analysis algorithms.

GigaScience
The study of phenomes or phenomics has been a central part of biology. The field of automatic phenotype acquisition technologies based on images has seen an important advance in the last years. As with other high-throughput technologies, it addresses...

DeepLoc: prediction of protein subcellular localization using deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: The prediction of eukaryotic protein subcellular localization is a well-studied topic in bioinformatics due to its relevance in proteomics research. Many machine learning methods have been successfully applied in this task, but in most of...