AIMC Topic: Machine Learning

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Augmented intelligence in pediatric anesthesia and pediatric critical care.

Current opinion in anaesthesiology
PURPOSE OF REVIEW: Acute care technologies, including novel monitoring devices, big data, increased computing capabilities, machine-learning algorithms and automation, are converging. This enables the application of augmented intelligence for improve...

Artificial Intelligence in Hematology: Current Challenges and Opportunities.

Current hematologic malignancy reports
PURPOSE OF REVIEW: Artificial intelligence (AI), and in particular its subcategory machine learning, is finding an increasing number of applications in medicine, driven in large part by an abundance of data and powerful, accessible tools that have ma...

Redundancy-weighting the PDB for detailed secondary structure prediction using deep-learning models.

Bioinformatics (Oxford, England)
MOTIVATION: The Protein Data Bank (PDB), the ultimate source for data in structural biology, is inherently imbalanced. To alleviate biases, virtually all structural biology studies use nonredundant (NR) subsets of the PDB, which include only a fracti...

Prevalence of Machine Learning in Craniofacial Surgery.

The Journal of craniofacial surgery
Machine learning (ML) revolves around the concept of using experience to teach computer-based programs to reliably perform specific tasks. Healthcare setting is an ideal environment for adaptation of ML applications given the multiple specific tasks ...

A multitask multiple kernel learning formulation for discriminating early- and late-stage cancers.

Bioinformatics (Oxford, England)
MOTIVATION: Genomic information is increasingly being used in diagnosis, prognosis and treatment of cancer. The severity of the disease is usually measured by the tumor stage. Therefore, identifying pathways playing an important role in progression o...

HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation.

Bioinformatics (Oxford, England)
MOTIVATION: Therapeutic peptides failing at clinical trials could be attributed to their toxicity profiles like hemolytic activity, which hamper further progress of peptides as drug candidates. The accurate prediction of hemolytic peptides (HLPs) and...

DNA4mC-LIP: a linear integration method to identify N4-methylcytosine site in multiple species.

Bioinformatics (Oxford, England)
MOTIVATION: DNA N4-methylcytosine (4mC) is a crucial epigenetic modification. However, the knowledge about its biological functions is limited. Effective and accurate identification of 4mC sites will be helpful to reveal its biological functions and ...

Automated Cardiovascular Pathology Assessment Using Semantic Segmentation and Ensemble Learning.

Journal of digital imaging
Cardiac magnetic resonance imaging provides high spatial resolution, enabling improved extraction of important functional and morphological features for cardiovascular disease staging. Segmentation of ventricular cavities and myocardium in cardiac ci...