AIMC Topic: Machine Learning

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Computational aspects underlying genome to phenome analysis in plants.

The Plant journal : for cell and molecular biology
Recent advances in genomics technologies have greatly accelerated the progress in both fundamental plant science and applied breeding research. Concurrently, high-throughput plant phenotyping is becoming widely adopted in the plant community, promisi...

Automated and flexible identification of complex disease: building a model for systemic lupus erythematosus using noisy labeling.

Journal of the American Medical Informatics Association : JAMIA
UNLABELLED: Accurate and efficient identification of complex chronic conditions in the electronic health record (EHR) is an important but challenging task that has historically relied on tedious clinician review and oversimplification of the disease....

Recent Advances in Machine Learning Methods for Predicting Heat Shock Proteins.

Current drug metabolism
BACKGROUND: As molecular chaperones, Heat Shock Proteins (HSPs) not only play key roles in protein folding and maintaining protein stabilities, but are also linked with multiple kinds of diseases. Therefore, HSPs have been regarded as the focus of dr...

Machine Learning in Human Olfactory Research.

Chemical senses
The complexity of the human sense of smell is increasingly reflected in complex and high-dimensional data, which opens opportunities for data-driven approaches that complement hypothesis-driven research. Contemporary developments in computational and...

Online Machine Learning Audiometry.

Ear and hearing
OBJECTIVES: A confluence of recent developments in cloud computing, real-time web audio and machine learning psychometric function estimation has made wide dissemination of sophisticated turn-key audiometric assessments possible. The authors have com...

Machine Learning in Quantitative Protein-peptide Affinity Prediction: Implications for Therapeutic Peptide Design.

Current drug metabolism
BACKGROUND: Protein-peptide recognition plays an essential role in the orchestration and regulation of cell signaling networks, which is estimated to be responsible for up to 40% of biological interaction events in the human interactome and has recen...

The Development of Machine Learning Methods in Cell-Penetrating Peptides Identification: A Brief Review.

Current drug metabolism
BACKGROUND: Cell-penetrating Peptides (CPPs) are important short peptides that facilitate cellular intake or uptake of various molecules. CPPs can transport drug molecules through the plasma membrane and send these molecules to different cellular org...

Application of machine learning algorithms for the differential diagnosis of peroxisomal disorders.

Journal of biochemistry
We have established diagnostic thresholds of very long-chain fatty acids (VLCFA) for the differential diagnosis of peroxisomal disorders using the machine learning tools. The plasma samples of 131 controls and 90 cases were tested for VLCFA using gas...