AIMC Topic: Machine Learning

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Glycowork: A Python package for glycan data science and machine learning.

Glycobiology
While glycans are crucial for biological processes, existing analysis modalities make it difficult for researchers with limited computational background to include these diverse carbohydrates into workflows. Here, we present glycowork, an open-source...

Humanization of antibodies using a machine learning approach on large-scale repertoire data.

Bioinformatics (Oxford, England)
MOTIVATION: Monoclonal antibody (mAb) therapeutics are often produced from non-human sources (typically murine), and can therefore generate immunogenic responses in humans. Humanization procedures aim to produce antibody therapeutics that do not elic...

CyAnno: a semi-automated approach for cell type annotation of mass cytometry datasets.

Bioinformatics (Oxford, England)
MOTIVATION: For immune system monitoring in large-scale studies at the single-cell resolution using CyTOF, (semi-)automated computational methods are applied for annotating live cells of mixed cell types. Here, we show that the live cell pool can be ...

Minding the gaps: The importance of navigating holes in protein fitness landscapes.

Cell systems
Machine-learning-guided protein design is rapidly emerging as a strategy to find high-fitness multi-mutant variants. In this issue of Cell Systems, Wittman et al. analyze the impact of design decisions for machine-learning-assisted directed evolution...

Machine learning-driven identification of early-life air toxic combinations associated with childhood asthma outcomes.

The Journal of clinical investigation
Air pollution is a well-known contributor to asthma. Air toxics are hazardous air pollutants that cause or may cause serious health effects. Although individual air toxics have been associated with asthma, only a limited number of studies have specif...

Artificial Neural Networks Predict 30-Day Mortality After Hip Fracture: Insights From Machine Learning.

The Journal of the American Academy of Orthopaedic Surgeons
OBJECTIVES: Accurately stratifying patients in the preoperative period according to mortality risk informs treatment considerations and guides adjustments to bundled reimbursements. We developed and compared three machine learning models to determine...

Digital surgery for gastroenterological diseases.

World journal of gastroenterology
Advances in machine learning, computer vision and artificial intelligence methods, in combination with those in processing and cloud computing capability, portend the advent of true decision support during interventions in real-time and soon perhaps ...

A Correspondence Between Normalization Strategies in Artificial and Biological Neural Networks.

Neural computation
A fundamental challenge at the interface of machine learning and neuroscience is to uncover computational principles that are shared between artificial and biological neural networks. In deep learning, normalization methods such as batch normalizatio...