AIMC Topic: Phenotype

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Toward robust and privacy-enhanced facial recognition: A decentralized blockchain-based approach with GANs and deep learning.

Mathematical biosciences and engineering : MBE
In recent years, the extensive use of facial recognition technology has raised concerns about data privacy and security for various applications, such as improving security and streamlining attendance systems and smartphone access. In this study, a b...

The Human Phenotype Ontology in 2024: phenotypes around the world.

Nucleic acids research
The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similar...

Discovering genotype-phenotype relationships with machine learning and the Visual Physiology Opsin Database (VPOD).

GigaScience
BACKGROUND: Predicting phenotypes from genetic variation is foundational for fields as diverse as bioengineering and global change biology, highlighting the importance of efficient methods to predict gene functions. Linking genetic changes to phenoty...

Deep learning links localized digital pathology phenotypes with transcriptional subtype and patient outcome in glioblastoma.

GigaScience
BACKGROUND: Deep learning has revolutionized medical image analysis in cancer pathology, where it had a substantial clinical impact by supporting the diagnosis and prognostic rating of cancer. Among the first available digital resources in the field ...

A Large Language Model Outperforms Other Computational Approaches to the High-Throughput Phenotyping of Physician Notes.

AMIA ... Annual Symposium proceedings. AMIA Symposium
High-throughput phenotyping, the automated mapping of patient signs and symptoms to standardized ontology concepts, is essential for realizing value from electronic health records (EHR) in support of precision medicine. Despite technological advances...

Machine Learning Strategies for Improved Phenotype Prediction in Underrepresented Populations.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Precision medicine models often perform better for populations of European ancestry due to the over-representation of this group in the genomic datasets and large-scale biobanks from which the models are constructed. As a result, prediction models ma...

SSLpheno: a self-supervised learning approach for gene-phenotype association prediction using protein-protein interactions and gene ontology data.

Bioinformatics (Oxford, England)
MOTIVATION: Medical genomics faces significant challenges in interpreting disease phenotype and genetic heterogeneity. Despite the establishment of standardized disease phenotype databases, computational methods for predicting gene-phenotype associat...

Automated and reproducible cell identification in mass cytometry using neural networks.

Briefings in bioinformatics
The principal use of mass cytometry is to identify distinct cell types and changes in their composition, phenotype and function in different samples and conditions. Combining data from different studies has the potential to increase the power of thes...

SoyDNGP: a web-accessible deep learning framework for genomic prediction in soybean breeding.

Briefings in bioinformatics
Soybean is a globally significant crop, playing a vital role in human nutrition and agriculture. Its complex genetic structure and wide trait variation, however, pose challenges for breeders and researchers aiming to optimize its yield and quality. A...

Spatially aware deep learning reveals tumor heterogeneity patterns that encode distinct kidney cancer states.

Cell reports. Medicine
Clear cell renal cell carcinoma (ccRCC) is molecularly heterogeneous, immune infiltrated, and selectively sensitive to immune checkpoint inhibition (ICI). However, the joint tumor-immune states that mediate ICI response remain elusive. We develop spa...