AIMC Topic: Sample Size

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Detecting reliable non interacting proteins (NIPs) significantly enhancing the computational prediction of protein-protein interactions using machine learning methods.

Molecular bioSystems Mar 1, 2016
Protein-protein interactions (PPIs) play a vital role in most biological processes. Hence their comprehension can promote a better understanding of the mechanisms underlying living systems. However, besides the cost and the time limitation involved i...
Machine Learning Reproducibility of Results ROC Curve Protein Binding Computational Biology Databases, Protein Sample Size Probability Protein Interaction Mapping
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Restricted Boltzmann machines based oversampling and semi-supervised learning for false positive reduction in breast CAD.

Bio-medical materials and engineering Jan 1, 2015
The false-positive reduction (FPR) is a crucial step in the computer aided detection system for the breast. The issues of imbalanced data distribution and the limitation of labeled samples complicate the classification procedure. To overcome these ch...
Humans Neural Networks, Computer Algorithms Computer Simulation Female Reproducibility of Results Sensitivity and Specificity Breast Neoplasms Models, Statistical Sample Size Data Interpretation, Statistical Radiographic Image Interpretation, Computer-Assisted Supervised Machine Learning Pattern Recognition, Automated False Positive Reactions Stochastic Processes Radiographic Image Enhancement
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Sampling from Determinantal Point Processes for Scalable Manifold Learning.

Information processing in medical imaging : proceedings of the ... conference Jan 1, 2015
High computational costs of manifold learning prohibit its application for large datasets. A common strategy to overcome this problem is to perform dimensionality reduction on selected landmarks and to successively embed the entire dataset with the N...
Humans Artificial Intelligence Algorithms Computer Simulation Tomography, X-Ray Computed Reproducibility of Results Sensitivity and Specificity Imaging, Three-Dimensional Head and Neck Neoplasms Models, Statistical Sample Size Data Interpretation, Statistical Signal Processing, Computer-Assisted Radiographic Image Interpretation, Computer-Assisted Pattern Recognition, Automated Radiographic Image Enhancement
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