AIMC Topic: Phenotype

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Deep learning-extracted CT imaging phenotypes predict response to total resection in colorectal cancer.

Acta radiologica (Stockholm, Sweden : 1987)
BACKGROUND: Deep learning surpasses many traditional methods for many vision tasks, allowing the transformation of hierarchical features into more abstract, high-level features.

Identifying congenital generalized lipodystrophy using deep learning-DEEPLIPO.

Scientific reports
Congenital Generalized Lipodystrophy (CGL) is a rare autosomal recessive disease characterized by near complete absence of functional adipose tissue from birth. CGL diagnosis can be based on clinical data including acromegaloid features, acanthosis n...

Searching by parts: Towards fine-grained image retrieval respecting species correlation.

Gene expression patterns : GEP
Most of the existing works on fine-grained image categorization and retrieval focus on finding similar images from the same species and often give little importance to inter-species similarities. However, these similarities may carry species correlat...

Expectile Neural Networks for Genetic Data Analysis of Complex Diseases.

IEEE/ACM transactions on computational biology and bioinformatics
The genetic etiologies of common diseases are highly complex and heterogeneous. Classic methods, such as linear regression, have successfully identified numerous variants associated with complex diseases. Nonetheless, for most diseases, the identifie...

Evaluation of the portability of computable phenotypes with natural language processing in the eMERGE network.

Scientific reports
The electronic Medical Records and Genomics (eMERGE) Network assessed the feasibility of deploying portable phenotype rule-based algorithms with natural language processing (NLP) components added to improve performance of existing algorithms using el...

Performance of the Vitek 2 Advanced Expert System (AES) as a Rapid Tool for Reporting Antimicrobial Susceptibility Testing (AST) in from North and Latin America.

Microbiology spectrum
This study evaluated the performance of the Vitek 2 Advanced Expert System (AES) confidence level report as a rapid tool for reporting antimicrobial susceptibility testing (AST) results for a challenging set of isolates from North and Latin America....

Deep learning in image-based phenotypic drug discovery.

Trends in cell biology
Modern drug discovery approaches often use high-content imaging to systematically study the effect on cells of large libraries of chemical compounds. By automatically screening thousands or millions of images to identify specific drug-induced cellula...

Deep learning-derived cardiovascular age shares a genetic basis with other cardiac phenotypes.

Scientific reports
Artificial intelligence (AI)-based approaches can now use electrocardiograms (ECGs) to provide expert-level performance in detecting heart abnormalities and diagnosing disease. Additionally, patient age predicted from ECGs by AI models has shown grea...

Robotic data acquisition with deep learning enables cell image-based prediction of transcriptomic phenotypes.

Proceedings of the National Academy of Sciences of the United States of America
Single-cell whole-transcriptome analysis is the gold standard approach to identifying molecularly defined cell phenotypes. However, this approach cannot be used for dynamics measurements such as live-cell imaging. Here, we developed a multifunctional...

Klarigi: Characteristic explanations for semantic biomedical data.

Computers in biology and medicine
Annotation of biomedical entities with ontology classes provides for formal semantic analysis and mobilisation of background knowledge in determining their relationships. To date, enrichment analysis has been routinely employed to identify classes th...