AIMC Topic: Machine Learning

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Using computer-vision and machine learning to automate facial coding of positive and negative affect intensity.

PloS one
Facial expressions are fundamental to interpersonal communication, including social interaction, and allow people of different ages, cultures, and languages to quickly and reliably convey emotional information. Historically, facial expression researc...

Novel Regularization Method for Biomarker Selection and Cancer Classification.

IEEE/ACM transactions on computational biology and bioinformatics
Variable selection has attracted more attention in big data and machine learning fields. In high dimensional data analysis, many relevant variables or variable groups are widely found. For example, people pay more interests to biological pathway or r...

Severe Dengue Prognosis Using Human Genome Data and Machine Learning.

IEEE transactions on bio-medical engineering
UNLABELLED: Dengue has become one of the most important worldwide arthropod-borne diseases. Dengue phenotypes are based on laboratorial and clinical exams, which are known to be inaccurate.

An Intelligent Clinical Decision Support System for Preoperative Prediction of Lymph Node Metastasis in Gastric Cancer.

Journal of the American College of Radiology : JACR
PURPOSE: The aim of this study was to develop and validate a computational clinical decision support system (DSS) on the basis of CT radiomics features for the prediction of lymph node (LN) metastasis in gastric cancer (GC) using machine learning-bas...

SAR study on inhibitors of GIIA secreted phospholipase A using machine learning methods.

Chemical biology & drug design
GIIA secreted phospholipase A (GIIA sPLA ) is a potent target for drug discovery. To distinguish the activity level of the inhibitors of GIIA sPLA , we built 24 classification models by three machine learning algorithms including support vector machi...

Machine learning algorithms for predicting scapular kinematics.

Medical engineering & physics
The goal of this study was to develop and validate a non-invasive approach to estimate scapular kinematics in individual patients. We hypothesized that machine learning algorithms could be developed using motion capture data to accurately estimate dy...

Radiomics-based machine learning methods for isocitrate dehydrogenase genotype prediction of diffuse gliomas.

Journal of cancer research and clinical oncology
PURPOSE: Reliable and accurate predictive models are necessary to drive the success of radiomics. Our aim was to identify the optimal radiomics-based machine learning method for isocitrate dehydrogenase (IDH) genotype prediction in diffuse gliomas.

Skin Sensitization Testing-What's Next?

International journal of molecular sciences
There is an increasing demand for alternative in vitro methods to replace animal testing, and, to succeed, new methods are required to be at least as accurate as existing in vivo tests. However, skin sensitization is a complex process requiring coord...

Neurodevelopmental heterogeneity and computational approaches for understanding autism.

Translational psychiatry
In recent years, the emerging field of computational psychiatry has impelled the use of machine learning models as a means to further understand the pathogenesis of multiple clinical disorders. In this paper, we discuss how autism spectrum disorder (...

Using machine learning to optimize selection of elderly patients for endovascular thrombectomy.

Journal of neurointerventional surgery
BACKGROUND: Endovascular thrombectomy (ET) is the standard of care for treatment of acute ischemic stroke (AIS) secondary to large vessel occlusion. The elderly population has been under-represented in clinical trials on ET, and recent studies have r...