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Phenomics

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Machine Learning for Image Analysis: Leaf Disease Segmentation.

Methods in molecular biology (Clifton, N.J.)
Plant phenomics field has seen a great increase in scalability in the last decade mainly due to technological advances in remote sensors and phenotyping platforms. These are capable of screening thousands of plants many times throughout the day, gene...

Machine learning phenomics (MLP) combining deep learning with time-lapse-microscopy for monitoring colorectal adenocarcinoma cells gene expression and drug-response.

Scientific reports
High-throughput phenotyping is becoming increasingly available thanks to analytical and bioinformatics approaches that enable the use of very high-dimensional data and to the availability of dynamic models that link phenomena across levels: from gene...

A primer on artificial intelligence in plant digital phenomics: embarking on the data to insights journey.

Trends in plant science
Artificial intelligence (AI) has emerged as a fundamental component of global agricultural research that is poised to impact on many aspects of plant science. In digital phenomics, AI is capable of learning intricate structure and patterns in large d...

Temporal Phenomics - A Powerful Approach Using AI to Achieve "Earlier Medicine".

Studies in health technology and informatics
The resurgence of machine learning AI has triggered the importance of collecting "personal big data" over a long period of time from wearable devices and EHRs. Collecting data from this large number of variables over a significant period of time has ...

PiTLiD: Identification of Plant Disease From Leaf Images Based on Convolutional Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics
With the development of plant phenomics, the identification of plant diseases from leaf images has become an effective and economic approach in plant disease science. Among the methods of plant diseases identification, the convolutional neural networ...

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...

Phenome-wide identification of therapeutic genetic targets, leveraging knowledge graphs, graph neural networks, and UK Biobank data.

Science advances
The ongoing expansion of human genomic datasets propels therapeutic target identification; however, extracting gene-disease associations from gene annotations remains challenging. Here, we introduce Mantis-ML 2.0, a framework integrating AstraZeneca'...

Integrating genomics, phenomics, and deep learning improves the predictive ability for Fusarium head blight-related traits in winter wheat.

The plant genome
Fusarium head blight (FHB) remains one of the most destructive diseases of wheat (Triticum aestivum L.), causing considerable losses in yield and end-use quality. Phenotyping of FHB resistance traits, Fusarium-damaged kernels (FDK), and deoxynivaleno...

Deep Hair Phenomics: Implications in Endocrinology, Development, and Aging.

The Journal of investigative dermatology
Hair quality is an important indicator of health in humans and other animals. Current approaches to assess hair quality are generally nonquantitative or are low throughput owing to technical limitations of splitting hairs. We developed a deep learnin...

The Unified Phenotype Ontology : a framework for cross-species integrative phenomics.

Genetics
Phenotypic data are critical for understanding biological mechanisms and consequences of genomic variation, and are pivotal for clinical use cases such as disease diagnostics and treatment development. For over a century, vast quantities of phenotype...