AIMC Topic: Supervised Machine Learning

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Integrated meta-analysis and machine learning approach identifies acyl-CoA thioesterase with other novel genes responsible for biofilm development in Staphylococcus aureus.

Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases
Biofilm forming Staphylococcus aureus is a major threat to the health-care industry. It is important to understand the differences between planktonic and biofilm growth forms in the pathogen since conventional treatments targeting the planktonic form...

An enhanced approach to the robust discriminant analysis and class sparsity based embedding.

Neural networks : the official journal of the International Neural Network Society
In recent times, feature extraction attracted much attention in machine learning and pattern recognition fields. This paper extends and improves a scheme for linear feature extraction that can be used in supervised multi-class classification problems...

Semi-supervised learning for somatic variant calling and peptide identification in personalized cancer immunotherapy.

BMC bioinformatics
BACKGROUND: Personalized cancer vaccines are emerging as one of the most promising approaches to immunotherapy of advanced cancers. However, only a small proportion of the neoepitopes generated by somatic DNA mutations in cancer cells lead to tumor r...

Active contour regularized semi-supervised learning for COVID-19 CT infection segmentation with limited annotations.

Physics in medicine and biology
Infection segmentation on chest CT plays an important role in the quantitative analysis of COVID-19. Developing automatic segmentation tools in a short period with limited labelled images has become an urgent need. Pseudo label-based semi-supervised ...

Quantifying influence of human choice on the automated detection of Drosophila behavior by a supervised machine learning algorithm.

PloS one
Automated quantification of behavior is increasingly prevalent in neuroscience research. Human judgments can influence machine-learning-based behavior classification at multiple steps in the process, for both supervised and unsupervised approaches. S...

In Situ Classification of Cell Types in Human Kidney Tissue Using 3D Nuclear Staining.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
To understand the physiology and pathology of disease, capturing the heterogeneity of cell types within their tissue environment is fundamental. In such an endeavor, the human kidney presents a formidable challenge because its complex organizational ...

Semi-supervised disentangled framework for transferable named entity recognition.

Neural networks : the official journal of the International Neural Network Society
Named entity recognition (NER) for identifying proper nouns in unstructured text is one of the most important and fundamental tasks in natural language processing. However, despite the widespread use of NER models, they still require a large-scale la...

Using supervised machine learning on neuropsychological data to distinguish OCD patients with and without sensory phenomena from healthy controls.

The British journal of clinical psychology
OBJECTIVES: While theoretical models link obsessive-compulsive disorder (OCD) with executive function deficits, empirical findings from the neuropsychological literature remain mixed. These inconsistencies are likely exacerbated by the challenge of h...

Systematic evaluation of supervised machine learning for sample origin prediction using metagenomic sequencing data.

Biology direct
BACKGROUND: The advent of metagenomic sequencing provides microbial abundance patterns that can be leveraged for sample origin prediction. Supervised machine learning classification approaches have been reported to predict sample origin accurately wh...