AIMC Topic: Deep Learning

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Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery.

Briefings in bioinformatics
Antibodies are versatile molecular binders with an established and growing role as therapeutics. Computational approaches to developing and designing these molecules are being increasingly used to complement traditional lab-based processes. Nowadays,...

A deep learning method for predicting metabolite-disease associations via graph neural network.

Briefings in bioinformatics
Metabolism is the process by which an organism continuously replaces old substances with new substances. It plays an important role in maintaining human life, body growth and reproduction. More and more researchers have shown that the concentrations ...

Adaptive sequencing using nanopores and deep learning of mitochondrial DNA.

Briefings in bioinformatics
Nanopore sequencing is an emerging technology that reads DNA by utilizing a unique method of detecting nucleic acid sequences and identifies the various chemical modifications they carry. Deep learning has increased in popularity as a useful techniqu...

BatchDTA: implicit batch alignment enhances deep learning-based drug-target affinity estimation.

Briefings in bioinformatics
Candidate compounds with high binding affinities toward a target protein are likely to be developed as drugs. Deep neural networks (DNNs) have attracted increasing attention for drug-target affinity (DTA) estimation owning to their efficiency. Howeve...

Generating and screening de novo compounds against given targets using ultrafast deep learning models as core components.

Briefings in bioinformatics
Deep learning is an artificial intelligence technique in which models express geometric transformations over multiple levels. This method has shown great promise in various fields, including drug development. The availability of public structure data...

Evaluating hierarchical machine learning approaches to classify biological databases.

Briefings in bioinformatics
The rate of biological data generation has increased dramatically in recent years, which has driven the importance of databases as a resource to guide innovation and the generation of biological insights. Given the complexity and scale of these datab...

BayeshERG: a robust, reliable and interpretable deep learning model for predicting hERG channel blockers.

Briefings in bioinformatics
Unintended inhibition of the human ether-à-go-go-related gene (hERG) ion channel by small molecules leads to severe cardiotoxicity. Thus, hERG channel blockage is a significant concern in the development of new drugs. Several computational models hav...

DeepSCP: utilizing deep learning to boost single-cell proteome coverage.

Briefings in bioinformatics
Multiplexed single-cell proteomes (SCPs) quantification by mass spectrometry greatly improves the SCP coverage. However, it still suffers from a low number of protein identifications and there is much room to boost proteins identification by computat...

Identifying daily activities of patient work for type 2 diabetes and co-morbidities: a deep learning and wearable camera approach.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: People are increasingly encouraged to self-manage their chronic conditions; however, many struggle to practise it effectively. Most studies that investigate patient work (ie, tasks involved in self-management and contexts influencing such ...

Deep learning for survival analysis in breast cancer with whole slide image data.

Bioinformatics (Oxford, England)
MOTIVATION: Whole slide tissue images contain detailed data on the sub-cellular structure of cancer. Quantitative analyses of this data can lead to novel biomarkers for better cancer diagnosis and prognosis and can improve our understanding of cancer...