AIMC Topic: Machine Learning

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Investigation of Low-Dose CT Lung Cancer Screening Scan "Over-Range" Issue Using Machine Learning Methods.

Journal of digital imaging
Low-dose computed tomography (CT) lung cancer screening is recommended by the US Preventive Services Task Force for high lung cancer-risk populations. In this study, we investigated an important factor affecting the CT dose-the scan length, for this ...

Development and Assessment of a Machine Learning Model to Help Predict Survival Among Patients With Oral Squamous Cell Carcinoma.

JAMA otolaryngology-- head & neck surgery
IMPORTANCE: Predicting survival of oral squamous cell carcinoma through the use of prediction modeling has been underused, and the development of prediction models would augment clinicians' ability to provide absolute risk estimates for individual pa...

Computer-Aided Diagnosis (CAD) of Pulmonary Nodule of Thoracic CT Image Using Transfer Learning.

Journal of digital imaging
Computer-aided diagnosis (CAD) has already been widely used in medical image processing. We recently make another trial to implement convolutional neural network (CNN) on the classification of pulmonary nodules of thoracic CT images. The biggest chal...

Artificial Intelligence and Machine Learning in Anesthesiology.

Anesthesiology
Commercial applications of artificial intelligence and machine learning have made remarkable progress recently, particularly in areas such as image recognition, natural speech processing, language translation, textual analysis, and self-learning. Pro...

Machine learning approach to identify a resting-state functional connectivity pattern serving as an endophenotype of autism spectrum disorder.

Brain imaging and behavior
Endophenotype refers to a measurable and heritable component between genetics and diagnosis, and the same endophenotype is present in both individuals with a diagnosis and their unaffected siblings. Determination of the neural correlates of an endoph...

Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework.

Briefings in bioinformatics
As a newly discovered post-translational modification (PTM), lysine malonylation (Kmal) regulates a myriad of cellular processes from prokaryotes to eukaryotes and has important implications in human diseases. Despite its functional significance, com...

Large-scale comparative assessment of computational predictors for lysine post-translational modification sites.

Briefings in bioinformatics
Lysine post-translational modifications (PTMs) play a crucial role in regulating diverse functions and biological processes of proteins. However, because of the large volumes of sequencing data generated from genome-sequencing projects, systematic id...

Machine learning meets genome assembly.

Briefings in bioinformatics
MOTIVATION: With the recent advances in DNA sequencing technologies, the study of the genetic composition of living organisms has become more accessible for researchers. Several advances have been achieved because of it, especially in the health scie...