AIMC Topic: Phenotype

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EasyGeSe - a resource for benchmarking genomic prediction methods.

BMC genomics
BACKGROUND: Genomic prediction is a widely used method to predict phenotypes from genotypic data. Advances in both biological and computer science have enabled the generation of vast amounts of data and the development of new algorithms, specifically...

Categorical and phenotypic image synthetic learning as an alternative to federated learning.

Nature communications
Multi-center collaborations are crucial in developing robust and generalizable machine learning models in medical imaging. Traditional methods, such as centralized data sharing or federated learning (FL), face challenges, including privacy issues, co...

Nondestructive VOC-Based Phenotyping Strategy for Assessing Brown Planthopper Resistance at the Adult Stage in Rice.

Journal of agricultural and food chemistry
The brown planthopper (BPH) is a major rice pest in Asia, with the most severe damage occurring at the adult stage in rice. Although breeding resistant varieties is key to pest control, current screening focuses mainly on seedlings using destructive,...

Multi-objective optimization of electromagnetic vibration parameters for corn seed phenotype prediction based on deep learning.

Scientific reports
This study presents a novel framework for adaptive optimization of electromagnetic vibration parameters in corn seed treatment using multi-objective deep learning approaches. A hybrid CNN-LSTM network architecture was developed to process heterogeneo...

A comparative study highlights superiority of LSTM in crop genomic prediction.

Planta
We systematically evaluated three key determinants affecting prediction accuracy and the algorithm performance differences based on fifteen state-of-the-art GP methods, and found LSTM suitable for capturing additive and epistatic effects. Genomic pre...

Machine learning detects hidden treatment response patterns only in the presence of comprehensive clinical phenotyping.

PloS one
Inferential statistics traditionally used in clinical trials can miss relationships between clinical phenotypes and treatment responses. We simulated a randomised clinical trial to explore how gradient boosting (XGBoost) machine learning compares wit...

Automated Chronic Obstructive Pulmonary Disease Phenotyping and Control Assessment in Primary Care: Retrospective Multicenter Study Using the Seleida Model.

JMIR medical informatics
BACKGROUND: Chronic obstructive pulmonary disease (COPD) remains a leading global health burden. In primary care, the inconsistent availability of spirometry and symptom scores limits the detection of patients with poor disease control. There is a pr...

AI-based modality-agnostic classification system for vascular calcifications.

Scientific reports
The importance of vascular calcification in major adverse cardiovascular events such as heart attacks or strokes has been established. However, calcifications have heterogeneous phenotypes, and their influence on diseased tissue stability remains poo...

Pan-cancer single-cell and spatial transcriptomics implicate cancer-associated fibroblasts in neutrophil immunosuppressive phenotypic transitions and immunotherapy resistance.

Functional & integrative genomics
Neutrophils are the most abundant granulocyte population and have important functions such as defense against pathogens. However, they show significant Heterogeneity and play more complex roles in tumors. The theory of two-tiered differentiation of n...

Deep phenotyping of patient lived experience in functional bowel disorders using machine learning.

Scientific reports
Contemporary clinical management relies on a diagnostic label as the primary guide to treatment. However, individual patients' lived experiences vary more widely than standard diagnostic categories reflect. This is especially true for functional bowe...