AIMC Topic: Machine Learning

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HAPPENN is a novel tool for hemolytic activity prediction for therapeutic peptides which employs neural networks.

Scientific reports
The growing prevalence of resistance to antibiotics motivates the search for new antibacterial agents. Antimicrobial peptides are a diverse class of well-studied membrane-active peptides which function as part of the innate host defence system, and f...

Application of machine learning algorithm and modified high resolution DNA melting curve analysis for molecular subtyping of Salmonella isolates from various epidemiological backgrounds in northern Thailand.

World journal of microbiology & biotechnology
Food poisoning from consumption of food contaminated with non-typhoidal Salmonella spp. is a global problem. A modified high resolution DNA melting curve analysis (m-HRMa) was introduced to provide effective discrimination among closely related HRM c...

Medical data science in rhinology: Background and implications for clinicians.

American journal of otolaryngology
BACKGROUND: An important challenge of big data is using complex information networks to provide useful clinical information. Recently, machine learning, and particularly deep learning, has enabled rapid advances in clinical practice. The application ...

Detecting rare diseases in electronic health records using machine learning and knowledge engineering: Case study of acute hepatic porphyria.

PloS one
BACKGROUND: With the growing adoption of the electronic health record (EHR) worldwide over the last decade, new opportunities exist for leveraging EHR data for detection of rare diseases. Rare diseases are often not diagnosed or delayed in diagnosis ...

Benchmarking machine learning models on multi-centre eICU critical care dataset.

PloS one
Progress of machine learning in critical care has been difficult to track, in part due to absence of public benchmarks. Other fields of research (such as computer vision and natural language processing) have established various competitions and publi...

Evaluation of marker selection methods and statistical models for chronological age prediction based on DNA methylation.

Legal medicine (Tokyo, Japan)
In forensic investigation, retrieving biological information from DNA evidence is a promising field of interest. One of the applications is on the estimation of the age of the donor based on DNA methylation. A large number of studies focused on age p...

Comparison study of classification methods of intramuscular electromyography data for non-human primate model of traumatic spinal cord injury.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
Traumatic spinal cord injury is a serious neurological disorder. Patients experience a plethora of symptoms that can be attributed to the nerve fiber tracts that are compromised. This includes limb weakness, sensory impairment, and truncal instabilit...

Metabolomics, machine learning and immunohistochemistry to predict succinate dehydrogenase mutational status in phaeochromocytomas and paragangliomas.

The Journal of pathology
Phaeochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumours with a hereditary background in over one-third of patients. Mutations in succinate dehydrogenase (SDH) genes increase the risk for PPGLs and several other tumours. Mutation...

The major effects of health-related quality of life on 5-year survival prediction among lung cancer survivors: applications of machine learning.

Scientific reports
The primary goal of this study was to evaluate the major roles of health-related quality of life (HRQOL) in a 5-year lung cancer survival prediction model using machine learning techniques (MLTs). The predictive performances of the models were compar...