AIMC Topic: Phenotype

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Deep learning-based activity recognition and fine motor identification using 2D skeletons of cynomolgus monkeys.

Zoological research
Video-based action recognition is becoming a vital tool in clinical research and neuroscientific study for disorder detection and prediction. However, action recognition currently used in non-human primate (NHP) research relies heavily on intense man...

ActivePPI: quantifying protein-protein interaction network activity with Markov random fields.

Bioinformatics (Oxford, England)
MOTIVATION: Protein-protein interactions (PPI) are crucial components of the biomolecular networks that enable cells to function. Biological experiments have identified a large number of PPI, and these interactions are stored in knowledge bases. Howe...

Phen2Disease: a phenotype-driven model for disease and gene prioritization by bidirectional maximum matching semantic similarities.

Briefings in bioinformatics
Human Phenotype Ontology (HPO)-based approaches have gained popularity in recent times as a tool for genomic diagnostics of rare diseases. However, these approaches do not make full use of the available information on disease and patient phenotypes. ...

ChatGPT for phenotypes extraction: one model to rule them all?

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Information Extraction (IE) is a core task in Natural Language Processing (NLP) where the objective is to identify factual knowledge in textual documents (often unstructured), and feed downstream use cases with the resulting output. In genomic medici...

AttOmics: attention-based architecture for diagnosis and prognosis from omics data.

Bioinformatics (Oxford, England)
MOTIVATION: The increasing availability of high-throughput omics data allows for considering a new medicine centered on individual patients. Precision medicine relies on exploiting these high-throughput data with machine-learning models, especially t...

COmic: convolutional kernel networks for interpretable end-to-end learning on (multi-)omics data.

Bioinformatics (Oxford, England)
MOTIVATION: The size of available omics datasets is steadily increasing with technological advancement in recent years. While this increase in sample size can be used to improve the performance of relevant prediction tasks in healthcare, models that ...

Multi-modal deep learning improves grain yield prediction in wheat breeding by fusing genomics and phenomics.

Bioinformatics (Oxford, England)
MOTIVATION: Developing new crop varieties with superior performance is highly important to ensure robust and sustainable global food security. The speed of variety development is limited by long field cycles and advanced generation selections in plan...

Using machine learning to realize genetic site screening and genomic prediction of productive traits in pigs.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology
Genomic prediction, which is based on solving linear mixed-model (LMM) equations, is the most popular method for predicting breeding values or phenotypic performance for economic traits in livestock. With the need to further improve the performance o...

End-to-end interpretable disease-gene association prediction.

Briefings in bioinformatics
Identifying disease-gene associations is a fundamental and critical biomedical task towards understanding molecular mechanisms, the diagnosis and treatment of diseases. It is time-consuming and expensive to experimentally verify causal links between ...

Multimodal deep learning methods enhance genomic prediction of wheat breeding.

G3 (Bethesda, Md.)
While several statistical machine learning methods have been developed and studied for assessing the genomic prediction (GP) accuracy of unobserved phenotypes in plant breeding research, few methods have linked genomics and phenomics (imaging). Deep ...