Interpretable deep learning for automatic diagnosis of 12-lead electrocardiogram
Sign Up to like & getrecommendations! Published in 2021 at "iScience"
DOI: 10.1016/j.isci.2021.102373
Abstract: Summary Electrocardiogram (ECG) is a widely used reliable, non-invasive approach for cardiovascular disease diagnosis. With the rapid growth of ECG examinations and the insufficiency of cardiologists, accurate and automatic diagnosis of ECG signals has become… read more here.
Keywords: diagnosis; deep learning; interpretable deep; automatic diagnosis ... See more keywords
IDL-PPBopt: A Strategy for Prediction and Optimization of Human Plasma Protein Binding of Compounds via an Interpretable Deep Learning Method
Sign Up to like & getrecommendations! Published in 2022 at "Journal of chemical information and modeling"
DOI: 10.1021/acs.jcim.2c00297
Abstract: The prediction and optimization of pharmacokinetic properties are essential in lead optimization. Traditional strategies mainly depend on the empirical chemical rules from medicinal chemists. However, with the rising amount of data, it is getting more… read more here.
Keywords: prediction optimization; idl ppbopt; deep learning; interpretable deep ... See more keywords
DeepSOFA: A Continuous Acuity Score for Critically Ill Patients using Clinically Interpretable Deep Learning
Sign Up to like & getrecommendations! Published in 2019 at "Scientific Reports"
DOI: 10.1038/s41598-019-38491-0
Abstract: Traditional methods for assessing illness severity and predicting in-hospital mortality among critically ill patients require time-consuming, error-prone calculations using static variable thresholds. These methods do not capitalize on the emerging availability of streaming electronic health… read more here.
Keywords: ill patients; deep learning; critically ill; deepsofa ... See more keywords
Predicting protein phosphorylation sites in soybean using interpretable deep tabular learning network
Sign Up to like & getrecommendations! Published in 2022 at "Briefings in bioinformatics"
DOI: 10.1093/bib/bbac015
Abstract: Phosphorylation of proteins is one of the most significant post-translational modifications (PTMs) and plays a crucial role in plant functionality due to its impact on signaling, gene expression, enzyme kinetics, protein stability and interactions. Accurate… read more here.
Keywords: deep tabular; phosphorylation sites; tabular learning; sites soybean ... See more keywords
Displacement-agnostic coherent imaging through scatter with an interpretable deep neural network.
Sign Up to like & getrecommendations! Published in 2021 at "Optics express"
DOI: 10.1364/oe.411291
Abstract: Coherent imaging through scatter is a challenging task. Both model-based and data-driven approaches have been explored to solve the inverse scattering problem. In our previous work, we have shown that a deep learning approach can… read more here.
Keywords: deep neural; imaging scatter; interpretable deep; neural network ... See more keywords
Interpretable Deep Learning Model Reveals Subsequences of Various Functions for Long Non-Coding RNA Identification
Sign Up to like & getrecommendations! Published in 2022 at "Frontiers in Genetics"
DOI: 10.3389/fgene.2022.876721
Abstract: Long non-coding RNAs (lncRNAs) play crucial roles in many biological processes and are implicated in several diseases. With the next-generation sequencing technologies, substantial unannotated transcripts have been discovered. Classifying unannotated transcripts using biological experiments are… read more here.
Keywords: learning model; long non; coding rnas; deep learning ... See more keywords