AIMC Topic: Neural Networks, Computer

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FLAN: feature-wise latent additive neural models for biological applications.

Briefings in bioinformatics
MOTIVATION: Interpretability has become a necessary feature for machine learning models deployed in critical scenarios, e.g. legal system, healthcare. In these situations, algorithmic decisions may have (potentially negative) long-lasting effects on ...

UniDL4BioPep: a universal deep learning architecture for binary classification in peptide bioactivity.

Briefings in bioinformatics
Identification of potent peptides through model prediction can reduce benchwork in wet experiments. However, the conventional process of model buildings can be complex and time consuming due to challenges such as peptide representation, feature selec...

End-to-end interpretable disease-gene association prediction.

Briefings in bioinformatics
Identifying disease-gene associations is a fundamental and critical biomedical task towards understanding molecular mechanisms, the diagnosis and treatment of diseases. It is time-consuming and expensive to experimentally verify causal links between ...

A review of enzyme design in catalytic stability by artificial intelligence.

Briefings in bioinformatics
The design of enzyme catalytic stability is of great significance in medicine and industry. However, traditional methods are time-consuming and costly. Hence, a growing number of complementary computational tools have been developed, e.g. ESMFold, Al...

Inter-domain distance prediction based on deep learning for domain assembly.

Briefings in bioinformatics
AlphaFold2 achieved a breakthrough in protein structure prediction through the end-to-end deep learning method, which can predict nearly all single-domain proteins at experimental resolution. However, the prediction accuracy of full-chain proteins is...

Deep Learning Method for Estimation of Morphological Parameters Based on CT Scans.

Studies in health technology and informatics
In this study, we propose a Convolutional Neural Network (CNN) with an assembly of non-linear fully connected layers for estimating body height and weight using a limited amount of data. This method can predict the parameters within acceptable clinic...

Post Hoc Sample Size Estimation for Deep Learning Architectures for ECG-Classification.

Studies in health technology and informatics
Deep Learning architectures for time series require a large number of training samples, however traditional sample size estimation for sufficient model performance is not applicable for machine learning, especially in the field of electrocardiograms ...

Can Synthetic Images Improve CNN Performance in Wound Image Classification?

Studies in health technology and informatics
For artificial intelligence (AI) based systems to become clinically relevant, they must perform well. Machine Learning (ML) based AI systems require a large amount of labelled training data to achieve this level. In cases of a shortage of such large ...

Information Extraction from Medical Texts with BERT Using Human-in-the-Loop Labeling.

Studies in health technology and informatics
Neural network language models, such as BERT, can be used for information extraction from medical texts with unstructured free text. These models can be pre-trained on a large corpus to learn the language and characteristics of the relevant domain an...

Classifiers of Medical Eponymy in Scientific Texts.

Studies in health technology and informatics
Many concepts in the medical literature are named after persons. Frequent ambiguities and spelling varieties, however, complicate the automatic recognition of such eponyms with natural language processing (NLP) tools. Recently developed methods inclu...