AIMC Topic: ROC Curve
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Identification and transfer of spatial transcriptomics signatures for cancer diagnosis.
Breast cancer research : BCR
Jan 13, 2020
BACKGROUND: Distinguishing ductal carcinoma in situ (DCIS) from invasive ductal carcinoma (IDC) regions in clinical biopsies constitutes a diagnostic challenge. Spatial transcriptomics (ST) is an in situ capturing method, which allows quantification ...
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment.
Journal of visualized experiments : JoVE
Jan 11, 2020
Mild cognitive impairment (MCI) is the first sign of dementia among elderly populations and its early detection is crucial in our aging societies. Common MCI tests are time-consuming such that indiscriminate massive screening would not be cost-effect...
Polygenic risk scores outperform machine learning methods in predicting coronary artery disease status.
Genetic epidemiology
Jan 10, 2020
Coronary artery disease (CAD) is the leading global cause of mortality and has substantial heritability with a polygenic architecture. Recent approaches of risk prediction were based on polygenic risk scores (PRS) not taking possible nonlinear effect...
Machine Learning Supports Long Noncoding RNAs as Expression Markers for Endometrial Carcinoma.
BioMed research international
Jan 10, 2020
Uterine corpus endometrial carcinoma (UCEC) is the second most common type of gynecological tumor. Several research studies have recently shown the potential of different ncRNAs as biomarkers for prognostics and diagnosis in different types of cancer...
Evaluation of the predictive ability of ultrasound-based assessment of breast cancer using BI-RADS natural language reporting against commercial transcriptome-based tests.
PloS one
Jan 10, 2020
PURPOSE: The objective of this study was to assess the classification capability of Breast Imaging Reporting and Data System (BI-RADS) ultrasound feature descriptors targeting established commercial transcriptomic gene signatures that guide managemen...
A two-step automated quality assessment for liver MR images based on convolutional neural network.
European journal of radiology
Jan 9, 2020
PURPOSE: To propose an automatic approach based on a convolutional neural network (CNN) to evaluate the quality of T2-weighted liver magnetic resonance (MR) images as nondiagnostic (ND) or diagnostic (D).
Deep learning for detecting retinal detachment and discerning macular status using ultra-widefield fundus images.
Communications biology
Jan 8, 2020
Retinal detachment can lead to severe visual loss if not treated timely. The early diagnosis of retinal detachment can improve the rate of successful reattachment and the visual results, especially before macular involvement. Manual retinal detachmen...
Training and Validation of Deep Neural Networks for the Prediction of 90-Day Post-Liver Transplant Mortality Using UNOS Registry Data.
Transplantation proceedings
Jan 8, 2020
Prediction models of post-liver transplant mortality are crucial so that donor organs are not allocated to recipients with unreasonably high probabilities of mortality. Machine learning algorithms, particularly deep neural networks (DNNs), can often ...
Deep-Channel uses deep neural networks to detect single-molecule events from patch-clamp data.
Communications biology
Jan 7, 2020
Single-molecule research techniques such as patch-clamp electrophysiology deliver unique biological insight by capturing the movement of individual proteins in real time, unobscured by whole-cell ensemble averaging. The critical first step in analysi...
A new concordant partial AUC and partial c statistic for imbalanced data in the evaluation of machine learning algorithms.
BMC medical informatics and decision making
Jan 6, 2020
BACKGROUND: In classification and diagnostic testing, the receiver-operator characteristic (ROC) plot and the area under the ROC curve (AUC) describe how an adjustable threshold causes changes in two types of error: false positives and false negative...
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