AIMC Topic: Phenotype
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Deep learning for plant genomics and crop improvement.
Current opinion in plant biology
Jan 24, 2020
Our era has witnessed tremendous advances in plant genomics, characterized by an explosion of high-throughput techniques to identify multi-dimensional genome-wide molecular phenotypes at low costs. More importantly, genomics is not merely acquiring m...
Using animal-mounted sensor technology and machine learning to predict time-to-calving in beef and dairy cows.
Animal : an international journal of animal bioscience
Jan 13, 2020
Worldwide, there is a trend towards increased herd sizes, and the animal-to-stockman ratio is increasing within the beef and dairy sectors; thus, the time available to monitoring individual animals is reducing. The behaviour of cows is known to chang...
Deep Learning of Imaging Phenotype and Genotype for Predicting Overall Survival Time of Glioblastoma Patients.
IEEE transactions on medical imaging
Jan 6, 2020
Glioblastoma (GBM) is the most common and deadly malignant brain tumor. For personalized treatment, an accurate pre-operative prognosis for GBM patients is highly desired. Recently, many machine learning-based methods have been adopted to predict ove...
Machine Learning Characterization of COPD Subtypes: Insights From the COPDGene Study.
Chest
Dec 28, 2019
COPD is a heterogeneous syndrome. Many COPD subtypes have been proposed, but there is not yet consensus on how many COPD subtypes there are and how they should be defined. The COPD Genetic Epidemiology Study (COPDGene), which has generated 10-year lo...
Spectroscopy based novel spectral indices, PCA- and PLSR-coupled machine learning models for salinity stress phenotyping of rice.
Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Dec 23, 2019
Identification and development of salinity tolerant genotypes and varieties are one of the promising ways to improve productivity of salt-affected soils. Alternate methods to achieve this are required as the conventional methods are time-consuming an...
StressGenePred: a twin prediction model architecture for classifying the stress types of samples and discovering stress-related genes in arabidopsis.
BMC genomics
Dec 20, 2019
BACKGROUND: Recently, a number of studies have been conducted to investigate how plants respond to stress at the cellular molecular level by measuring gene expression profiles over time. As a result, a set of time-series gene expression data for the ...
Hyperspectral imaging combined with machine learning as a tool to obtain high-throughput plant salt-stress phenotyping.
The Plant journal : for cell and molecular biology
Dec 9, 2019
The rapid selection of salinity-tolerant crops to increase food production in salinized lands is important for sustainable agriculture. Recently, high-throughput plant phenotyping technologies have been adopted that use plant morphological and physio...
Recent advances on constraint-based models by integrating machine learning.
Current opinion in biotechnology
Dec 5, 2019
Research that meaningfully integrates constraint-based modeling with machine learning is at its infancy but holds much promise. Here, we consider where machine learning has been implemented within the constraint-based modeling reconstruction framewor...
CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence.
SLAS technology
Nov 26, 2019
The clinical team attending to a patient upon a diagnosis is faced with two main questions: what treatment, and at what dose? Clinical trials' results provide the basis for guidance and support for official protocols that clinicians use to base their...
ImaGene: a convolutional neural network to quantify natural selection from genomic data.
BMC bioinformatics
Nov 22, 2019
BACKGROUND: The genetic bases of many complex phenotypes are still largely unknown, mostly due to the polygenic nature of the traits and the small effect of each associated mutation. An alternative approach to classic association studies to determini...
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