AIMC Topic: Phenotype

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Natural language processing diagnosed behavioural disturbance phenotypes in the intensive care unit: characteristics, prevalence, trajectory, treatment, and outcomes.

Critical care (London, England)
BACKGROUND: Natural language processing (NLP) may help evaluate the characteristics, prevalence, trajectory, treatment, and outcomes of behavioural disturbance phenotypes in critically ill patients.

DeepGAMI: deep biologically guided auxiliary learning for multimodal integration and imputation to improve genotype-phenotype prediction.

Genome medicine
BACKGROUND: Genotypes are strongly associated with disease phenotypes, particularly in brain disorders. However, the molecular and cellular mechanisms behind this association remain elusive. With emerging multimodal data for these mechanisms, machine...

Automated staging of zebrafish embryos with deep learning.

Life science alliance
The zebrafish () is an important biomedical model organism used in many disciplines. The phenomenon of developmental delay in zebrafish embryos has been widely reported as part of a mutant or treatment-induced phenotype. However, the detection and qu...

Walking the path of treatable traits in interstitial lung diseases.

Respiratory research
Interstitial lung diseases (ILDs) are complex and heterogeneous diseases. The use of traditional diagnostic classification in ILD can lead to suboptimal management, which is worsened by not considering the molecular pathways, biological complexity, a...

Epidemiology of osteoarthritis: literature update 2022-2023.

Current opinion in rheumatology
PURPOSE OF REVIEW: This review highlights recently published studies on osteoarthritis (OA) epidemiology, including topics related to understudied populations and joints, imaging, and advancements in artificial intelligence (AI) methods.

Batch normalization followed by merging is powerful for phenotype prediction integrating multiple heterogeneous studies.

PLoS computational biology
Heterogeneity in different genomic studies compromises the performance of machine learning models in cross-study phenotype predictions. Overcoming heterogeneity when incorporating different studies in terms of phenotype prediction is a challenging an...

Unsupervised machine learning for species delimitation, integrative taxonomy, and biodiversity conservation.

Molecular phylogenetics and evolution
Integrative taxonomy, combining data from multiple axes of biologically relevant variation, is a major goal of systematics. Ideally, such taxonomies will derive from similarly integrative species-delimitation analyses. Yet, most current methods rely ...

Artificial intelligence and heart failure: A state-of-the-art review.

European journal of heart failure
Heart failure (HF) is a heterogeneous syndrome affecting more than 60 million individuals globally. Despite recent advancements in understanding of the pathophysiology of HF, many issues remain including residual risk despite therapy, understanding t...

Artificial intelligence for dementia genetics and omics.

Alzheimer's & dementia : the journal of the Alzheimer's Association
Genetics and omics studies of Alzheimer's disease and other dementia subtypes enhance our understanding of underlying mechanisms and pathways that can be targeted. We identified key remaining challenges: First, can we enhance genetic studies to addre...