AIMC Topic: Machine Learning

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Application of machine learning in bacteriophage research.

BMC microbiology
Phages are one of the key components in the structure, dynamics, and interactions of microbial communities in different bins. It has a clear impact on human health and the food industry. Bacteriophage characterization using in vitro approaches are ti...

Risk prediction for delayed clearance of high-dose methotrexate in pediatric hematological malignancies by machine learning.

International journal of hematology
This study aimed to establish a predictive model to identify children with hematologic malignancy at high risk for delayed clearance of high-dose methotrexate (HD-MTX) based on machine learning. A total of 205 patients were recruited. Five variables ...

Chest radiographs and machine learning - Past, present and future.

Journal of medical imaging and radiation oncology
Despite its simple acquisition technique, the chest X-ray remains the most common first-line imaging tool for chest assessment globally. Recent evidence for image analysis using modern machine learning points to possible improvements in both the effi...

Review: Application of Artificial Intelligence in Phenomics.

Sensors (Basel, Switzerland)
Plant phenomics has been rapidly advancing over the past few years. This advancement is attributed to the increased innovation and availability of new technologies which can enable the high-throughput phenotyping of complex plant traits. The applicat...

Divide-and-conquer: machine-learning integrates mammalian and viral traits with network features to predict virus-mammal associations.

Nature communications
Our knowledge of viral host ranges remains limited. Completing this picture by identifying unknown hosts of known viruses is an important research aim that can help identify and mitigate zoonotic and animal-disease risks, such as spill-over from anim...

A spectrogram image based intelligent technique for automatic detection of autism spectrum disorder from EEG.

PloS one
Autism spectrum disorder (ASD) is a developmental disability characterized by persistent impairments in social interaction, speech and nonverbal communication, and restricted or repetitive behaviors. Currently Electroencephalography (EEG) is the most...

Predicting aptamer sequences that interact with target proteins using an aptamer-protein interaction classifier and a Monte Carlo tree search approach.

PloS one
Oligonucleotide-based aptamers, which have a three-dimensional structure with a single-stranded fragment, feature various characteristics with respect to size, toxicity, and permeability. Accordingly, aptamers are advantageous in terms of diagnosis a...

PIXE based, Machine-Learning (PIXEL) supported workflow for glass fragments classification.

Talanta
This paper presents a structured workflow for glass fragment analysis based on a combination of Elemental Analysis using PIXE and Machine Learning tools, with the ultimate goal of standardizing and helping forensic efforts. The proposed workflow was ...

Artificial intelligence for quality assurance in radiotherapy.

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
In radiotherapy, patient-specific quality assurance is very time-consuming and causes machine downtime. It consists of testing (using measurement with a phantom and detector) if a modulated plan is correctly delivered by a treatment unit. Artificial ...