AIMC Topic: Deep Learning

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dynDeepDRIM: a dynamic deep learning model to infer direct regulatory interactions using time-course single-cell gene expression data.

Briefings in bioinformatics
Time-course single-cell RNA sequencing (scRNA-seq) data have been widely used to explore dynamic changes in gene expression of transcription factors (TFs) and their target genes. This information is useful to reconstruct cell-type-specific gene regul...

Deep learning models for disease-associated circRNA prediction: a review.

Briefings in bioinformatics
Emerging evidence indicates that circular RNAs (circRNAs) can provide new insights and potential therapeutic targets for disease diagnosis and treatment. However, traditional biological experiments are expensive and time-consuming. Recently, deep lea...

A geometric deep learning framework for drug repositioning over heterogeneous information networks.

Briefings in bioinformatics
Drug repositioning (DR) is a promising strategy to discover new indicators of approved drugs with artificial intelligence techniques, thus improving traditional drug discovery and development. However, most of DR computational methods fall short of t...

Deep learning joint models for extracting entities and relations in biomedical: a survey and comparison.

Briefings in bioinformatics
The rapid development of biomedicine has produced a large number of biomedical written materials. These unstructured text data create serious challenges for biomedical researchers to find information. Biomedical named entity recognition (BioNER) and ...

FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction.

Briefings in bioinformatics
Accurate prediction of molecular properties, such as physicochemical and bioactive properties, as well as ADME/T (absorption, distribution, metabolism, excretion and toxicity) properties, remains a fundamental challenge for molecular design, especial...

Predicting colorectal cancer tumor mutational burden from histopathological images and clinical information using multi-modal deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Tumor mutational burden (TMB) is an indicator of the efficacy and prognosis of immune checkpoint therapy in colorectal cancer (CRC). In general, patients with higher TMB values are more likely to benefit from immunotherapy. Though whole-e...

Multimodal attention-based deep learning for Alzheimer's disease diagnosis.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Alzheimer's disease (AD) is the most common neurodegenerative disorder with one of the most complex pathogeneses, making effective and clinically actionable decision support difficult. The objective of this study was to develop a novel mul...

Deep learning-assisted genome-wide characterization of massively parallel reporter assays.

Nucleic acids research
Massively parallel reporter assay (MPRA) is a high-throughput method that enables the study of the regulatory activities of tens of thousands of DNA oligonucleotides in a single experiment. While MPRA experiments have grown in popularity, their small...

[Progress in application of deep learning in orthodontic diagnosis and treatment].

Zhonghua kou qiang yi xue za zhi = Zhonghua kouqiang yixue zazhi = Chinese journal of stomatology
In recent years, the application of artificial intelligence technology in the field of orthodontics has gradually increased, and deep learning, as a hot direction, has also been rapidly applied in the detection, evaluation, diagnosis, prediction and ...

Enhanced segmentation of gastrointestinal polyps from capsule endoscopy images with artifacts using ensemble learning.

World journal of gastroenterology
BACKGROUND: Endoscopy artifacts are widespread in real capsule endoscopy (CE) images but not in high-quality standard datasets.