AIMC Topic: Phenotype

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Identification of a blood-based 12-gene signature that predicts the severity of coronary artery stenosis: An integrative approach based on gene network construction, Support Vector Machine algorithm, and multi-cohort validation.

Atherosclerosis Oct 9, 2019
BACKGROUND AND AIMS: We aimed to identify a blood-based gene expression score (GES) to predict the severity of coronary artery stenosis in patients with known or suspected coronary artery disease (CAD) by integrative use of gene network construction,...
Humans Decision Support Techniques Phenotype Retrospective Studies Predictive Value of Tests Coronary Stenosis Gene Expression Profiling Transcriptome China Gene Regulatory Networks Genetic Predisposition to Disease Nomograms Support Vector Machine Aged Male Middle Aged Severity of Illness Index Female Reproducibility of Results Risk Factors
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Machine Learning Approaches to Improve Three Basic Plant Phenotyping Tasks Using Three-Dimensional Point Clouds.

Plant physiology Oct 7, 2019
Developing automated methods to efficiently process large volumes of point cloud data remains a challenge for three-dimensional (3D) plant phenotyping applications. Here, we describe the development of machine learning methods to tackle three primary...
Algorithms Phenotype Reproducibility of Results Imaging, Three-Dimensional Machine Learning Plant Stems Plant Leaves Plant Shoots Plant Roots Environment Solanum lycopersicum Nicotiana Plants
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Machine learning and big data analytics in bipolar disorder: A position paper from the International Society for Bipolar Disorders Big Data Task Force.

Bipolar disorders Sep 18, 2019
OBJECTIVES: The International Society for Bipolar Disorders Big Data Task Force assembled leading researchers in the field of bipolar disorder (BD), machine learning, and big data with extensive experience to evaluate the rationale of machine learnin...
Prognosis Clinical Decision-Making Risk Assessment Big Data Humans Machine Learning Phenotype Data Science Suicidal Ideation Bipolar Disorder Advisory Committees
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Cell Type Classification and Unsupervised Morphological Phenotyping From Low-Resolution Images Using Deep Learning.

Scientific reports Sep 17, 2019
Convolutional neural networks (ConvNets) have proven to be successful in both the classification and semantic segmentation of cell images. Here we establish a method for cell type classification utilizing images taken with a benchtop microscope direc...
Organ Specificity Cell Tracking Single-Cell Analysis Models, Theoretical Microscopy Neural Networks, Computer Algorithms Phenotype Deep Learning
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Facetto: Combining Unsupervised and Supervised Learning for Hierarchical Phenotype Analysis in Multi-Channel Image Data.

IEEE transactions on visualization and computer graphics Sep 10, 2019
Facetto is a scalable visual analytics application that is used to discover single-cell phenotypes in high-dimensional multi-channel microscopy images of human tumors and tissues. Such images represent the cutting edge of digital histology and promis...
Machine Learning Software Phenotype Neoplasms Systems Biology Cluster Analysis Image Interpretation, Computer-Assisted Humans Neural Networks, Computer
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Identifying Phenotypes of Atopic Dermatitis in a Longitudinal United States Cohort Using Unbiased Statistical Clustering.

The Journal of investigative dermatology Aug 22, 2019
Humans Machine Learning Phenotype Female Infant, Newborn Male Cluster Analysis Child, Preschool Infant Registries Child Severity of Illness Index Age of Onset United States Diagnosis, Computer-Assisted Dermatitis, Atopic Longitudinal Studies
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Improving prediction of phenotypic drug response on cancer cell lines using deep convolutional network.

BMC bioinformatics Jul 29, 2019
BACKGROUND: Understanding the phenotypic drug response on cancer cell lines plays a vital role in anti-cancer drug discovery and re-purposing. The Genomics of Drug Sensitivity in Cancer (GDSC) database provides open data for researchers in phenotypic...
Organ Specificity Neural Networks, Computer Genomics Humans Phenotype Neoplasms Regression Analysis Databases, Factual Inhibitory Concentration 50 Antineoplastic Agents Deep Learning Cell Line, Tumor
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Recovering Wind-Induced Plant Motion in Dense Field Environments via Deep Learning and Multiple Object Tracking.

Plant physiology Jul 22, 2019
Understanding the relationships between local environmental conditions and plant structure and function is critical for both fundamental science and for improving the performance of crops in field settings. Wind-induced plant motion is important in m...
Algorithms Phenotype Wind Crops, Agricultural Deep Learning Breeding Agriculture Environment Triticum Motion
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Opportunities and Challenges in Phenotypic Screening for Neurodegenerative Disease Research.

Journal of medicinal chemistry Jul 18, 2019
Toxic misfolded proteins potentially underly many neurodegenerative diseases, but individual targets which regulate these proteins and their downstream detrimental effects are often unknown. Phenotypic screening is an unbiased method to screen for no...
Cell Line, Transformed Animals Neurodegenerative Diseases High-Throughput Screening Assays Clustered Regularly Interspaced Short Palindromic Repeats Machine Learning Phenotype Biomedical Research Humans
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Design of a tomato classifier based on machine vision.

PloS one Jul 18, 2019
This paper attempts to design an automated, efficient and intelligent tomato grading method that facilitates the graded selling of the fruit. Based on machine vision, the color images of tomatoes with different morphologies were studied, and the colo...
Phenotype Humans Reproducibility of Results Machine Learning Solanum lycopersicum Algorithms
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