AIMC Topic: Phenotype

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G2Φnet: Relating genotype and biomechanical phenotype of tissues with deep learning.

PLoS computational biology
Many genetic mutations adversely affect the structure and function of load-bearing soft tissues, with clinical sequelae often responsible for disability or death. Parallel advances in genetics and histomechanical characterization provide significant ...

Addressing Noise and Estimating Uncertainty in Biomedical Data through the Exploration of Chemical Space.

International journal of molecular sciences
Noise is a basic ingredient in data, since observed data are always contaminated by unwanted deviations, i.e., noise, which, in the case of overdetermined systems (with more data than model parameters), cause the corresponding linear system of equati...

[Artificial intelligence in the diagnosis of rare disorders: the development of phenotype analysis].

Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz
Rare diseases can often be diagnosed by carefully assessing the phenotype of the patient, as characteristic deviations (dysmorphisms) occur in many genetic diseases. These affect, for example, the features of the face - the "facial gestalt."This pape...

Phenotyping in clinical text with unsupervised numerical reasoning for patient stratification.

Experimental biology and medicine (Maywood, N.J.)
Phenotypic information of patients, as expressed in clinical text, is important in many clinical applications such as identifying patients at risk of hard-to-diagnose conditions. Extracting and inferring some phenotypes from clinical text requires nu...

Logistic Regression With Machine Learning Sheds Light on the Problematic Sexual Behavior Phenotype.

Journal of addiction medicine
OBJECTIVES: There has been a longstanding debate about whether the mechanisms involved in problematic sexual behavior (PSB) are similar to those observed in addictive disorders, or related to impulse control or to compulsivity. The aim of this report...

Self-supervised classification of subcellular morphometric phenotypes reveals extracellular matrix-specific morphological responses.

Scientific reports
Cell morphology is profoundly influenced by cellular interactions with microenvironmental factors such as the extracellular matrix (ECM). Upon adhesion to specific ECM, various cell types are known to exhibit different but distinctive morphologies, s...

Neural network and Bayesian-based prediction of breeding values in Beetal goat.

Tropical animal health and production
The estimation of breeding values is prime concern for animal breeders in order to achieve desired genetic progress of farm animals. However, current methods for estimating BV involve simultaneous selection of animal model which are computationally i...

Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction.

Molecular plant
The first paradigm of plant breeding involves direct selection-based phenotypic observation, followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and, more recently, by inco...

Weakly Semi-supervised phenotyping using Electronic Health records.

Journal of biomedical informatics
OBJECTIVE: Electronic Health Record (EHR) based phenotyping is a crucial yet challenging problem in the biomedical field. Though clinicians typically determine patient-level diagnoses via manual chart review, the sheer volume and heterogeneity of EHR...

LeMeDISCO is a computational method for large-scale prediction & molecular interpretation of disease comorbidity.

Communications biology
To understand the origin of disease comorbidity and to identify the essential proteins and pathways underlying comorbid diseases, we developed LeMeDISCO (Large-Scale Molecular Interpretation of Disease Comorbidity), an algorithm that predicts disease...