AIMC Topic: ROC Curve
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XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data.
Briefings in bioinformatics
Nov 5, 2021
The lack of explainability is one of the most prominent disadvantages of deep learning applications in omics. This 'black box' problem can undermine the credibility and limit the practical implementation of biomedical deep learning models. Here we pr...
AniAMPpred: artificial intelligence guided discovery of novel antimicrobial peptides in animal kingdom.
Briefings in bioinformatics
Nov 5, 2021
With advancements in genomics, there has been substantial reduction in the cost and time of genome sequencing and has resulted in lot of data in genome databases. Antimicrobial host defense proteins provide protection against invading microbes. But c...
Predicting MHC class I binder: existing approaches and a novel recurrent neural network solution.
Briefings in bioinformatics
Nov 5, 2021
Major histocompatibility complex (MHC) possesses important research value in the treatment of complex human diseases. A plethora of computational tools has been developed to predict MHC class I binders. Here, we comprehensively reviewed 27 up-to-date...
Deep learning model reveals potential risk genes for ADHD, especially Ephrin receptor gene EPHA5.
Briefings in bioinformatics
Nov 5, 2021
Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder. Although genome-wide association studies (GWAS) identify the risk ADHD-associated variants and genes with significant P-values, they may neglect the combined eff...
Multi-view Multichannel Attention Graph Convolutional Network for miRNA-disease association prediction.
Briefings in bioinformatics
Nov 5, 2021
MOTIVATION: In recent years, a growing number of studies have proved that microRNAs (miRNAs) play significant roles in the development of human complex diseases. Discovering the associations between miRNAs and diseases has become an important part of...
Training with Small Medical Data: Robust Bayesian Neural Networks for Colon Cancer Overall Survival Prediction.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Nov 1, 2021
Fast and accurate cancer prognosis stratification models are essential for treatment designs. Large labeled patient data can power advanced deep learning models to obtain precise predictions. However, since fully labeled patient data are hard to acqu...
PhacoTrainer: A Multicenter Study of Deep Learning for Activity Recognition in Cataract Surgical Videos.
Translational vision science & technology
Nov 1, 2021
PURPOSE: To build and evaluate deep learning models for recognizing cataract surgical steps from whole-length surgical videos with minimal preprocessing, including identification of routine and complex steps.
Performance of a Convolutional Neural Network and Explainability Technique for 12-Lead Electrocardiogram Interpretation.
JAMA cardiology
Nov 1, 2021
IMPORTANCE: Millions of clinicians rely daily on automated preliminary electrocardiogram (ECG) interpretation. Critical comparisons of machine learning-based automated analysis against clinically accepted standards of care are lacking.
Development and Validation of Image-Based Deep Learning Models to Predict Surgical Complexity and Complications in Abdominal Wall Reconstruction.
JAMA surgery
Oct 1, 2021
IMPORTANCE: Image-based deep learning models (DLMs) have been used in other disciplines, but this method has yet to be used to predict surgical outcomes.
Corneal Edema Visualization With Optical Coherence Tomography Using Deep Learning: Proof of Concept.
Cornea
Oct 1, 2021
PURPOSE: Optical coherence tomography (OCT) is essential for the diagnosis and follow-up of corneal edema, but assessment can be challenging in minimal or localized edema. The objective was to develop and validate a novel automated tool to detect and...
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