AIMC Topic: Sample Size

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Synthetic observations from deep generative models and binary omics data with limited sample size.

Briefings in bioinformatics Jul 20, 2021
Deep generative models can be trained to represent the joint distribution of data, such as measurements of single nucleotide polymorphisms (SNPs) from several individuals. Subsequently, synthetic observations are obtained by drawing from this distrib...
Neural Networks, Computer Polymorphism, Single Nucleotide Models, Genetic Sample Size
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Machine Learning Predictive Models Can Improve Efficacy of Clinical Trials for Alzheimer's Disease.

Journal of Alzheimer's disease : JAD Jan 1, 2020
BACKGROUND: The ideal participants for Alzheimer's disease (AD) clinical trials would show cognitive decline in the absence of treatment (i.e., placebo arm) and also would be responsive to the therapeutic intervention being studied (i.e., drug arm). ...
Sample Size Reproducibility of Results Alzheimer Disease Aged Sensitivity and Specificity Research Design Neuroimaging Aged, 80 and over Male Databases, Factual Follow-Up Studies Cognitive Dysfunction Female Magnetic Resonance Imaging Predictive Value of Tests Humans Clinical Trials as Topic Machine Learning Clinical Trials, Phase III as Topic Algorithms Mental Status and Dementia Tests
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Breast Cancer Diagnosis in Digital Breast Tomosynthesis: Effects of Training Sample Size on Multi-Stage Transfer Learning Using Deep Neural Nets.

IEEE transactions on medical imaging Mar 1, 2019
In this paper, we developed a deep convolutional neural network (CNN) for the classification of malignant and benign masses in digital breast tomosynthesis (DBT) using a multi-stage transfer learning approach that utilized data from similar auxiliary...
Humans Neural Networks, Computer Area Under Curve Sample Size Michigan Machine Learning Breast Neoplasms Mammography
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piMGM: incorporating multi-source priors in mixed graphical models for learning disease networks.

Bioinformatics (Oxford, England) Sep 1, 2018
MOTIVATION: Learning probabilistic graphs over mixed data is an important way to combine gene expression and clinical disease data. Leveraging the existing, yet imperfect, information in pathway databases for mixed graphical model (MGM) learning is a...
Disease Databases, Factual Reproducibility of Results Sample Size Humans Machine Learning Software
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Semi-supervised network inference using simulated gene expression dynamics.

Bioinformatics (Oxford, England) Apr 1, 2018
MOTIVATION: Inferring the structure of gene regulatory networks from high-throughput datasets remains an important and unsolved problem. Current methods are hampered by problems such as noise, low sample size, and incomplete characterizations of regu...
Models, Biological Supervised Machine Learning Gene Expression Regulation Gene Regulatory Networks Computational Biology Sample Size Computer Simulation
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Finding causative genes from high-dimensional data: an appraisal of statistical and machine learning approaches.

Statistical applications in genetics and molecular biology Aug 1, 2016
Modern biological experiments often involve high-dimensional data with thousands or more variables. A challenging problem is to identify the key variables that are related to a specific disease. Confounding this task is the vast number of statistical...
Sample Size Data Interpretation, Statistical Humans Machine Learning Computer Simulation Male Neoplasms Models, Genetic
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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 Sample Size Protein Binding Probability Computational Biology Protein Interaction Mapping Databases, Protein
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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...
Stochastic Processes Sensitivity and Specificity Supervised Machine Learning Radiographic Image Enhancement Breast Neoplasms Humans Pattern Recognition, Automated Computer Simulation Models, Statistical Sample Size Neural Networks, Computer False Positive Reactions Female Data Interpretation, Statistical Algorithms Reproducibility of Results Radiographic Image Interpretation, Computer-Assisted
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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...
Radiographic Image Interpretation, Computer-Assisted Pattern Recognition, Automated Radiographic Image Enhancement Sample Size Data Interpretation, Statistical Signal Processing, Computer-Assisted Imaging, Three-Dimensional Humans Head and Neck Neoplasms Models, Statistical Tomography, X-Ray Computed Artificial Intelligence Reproducibility of Results Algorithms Sensitivity and Specificity Computer Simulation
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