AIMC Topic: Phenotype
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Analysis of COVID-19 clinical trials: A data-driven, ontology-based, and natural language processing approach.
PloS one
Sep 30, 2020
With the novel COVID-19 pandemic disrupting and threatening the lives of millions, researchers and clinicians have been recently conducting clinical trials at an unprecedented rate to learn more about the virus and potential drugs/treatments/vaccines...
Machine learning assistive rapid, label-free molecular phenotyping of blood with two-dimensional NMR correlational spectroscopy.
Communications biology
Sep 28, 2020
Translation of the findings in basic science and clinical research into routine practice is hampered by large variations in human phenotype. Developments in genotyping and phenotyping, such as proteomics and lipidomics, are beginning to address these...
Artificial intelligence to detect MYC translocation in slides of diffuse large B-cell lymphoma.
Virchows Archiv : an international journal of pathology
Sep 26, 2020
In patients with suspected lymphoma, the tissue biopsy provides lymphoma confirmation, classification, and prognostic factors, including genetic changes. We developed a deep learning algorithm to detect MYC rearrangement in scanned histological slide...
Humans
Phenotype
Reproducibility of Results
Biomarkers, Tumor
Image Interpretation, Computer-Assisted
Immunohistochemistry
Proof of Concept Study
Staining and Labeling
Lymphoma, Large B-Cell, Diffuse
Antigens, CD20
Predictive Value of Tests
Deep Learning
Proto-Oncogene Proteins c-myc
Microscopy
Genetic Predisposition to Disease
Translocation, Genetic
In Situ Hybridization, Fluorescence
Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism.
Nature communications
Sep 25, 2020
Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be c...
Learning patterns of the ageing brain in MRI using deep convolutional networks.
NeuroImage
Sep 24, 2020
Both normal ageing and neurodegenerative diseases cause morphological changes to the brain. Age-related brain changes are subtle, nonlinear, and spatially and temporally heterogenous, both within a subject and across a population. Machine learning mo...
Deep learning-enabled analysis reveals distinct neuronal phenotypes induced by aging and cold-shock.
BMC biology
Sep 23, 2020
BACKGROUND: Access to quantitative information is crucial to obtain a deeper understanding of biological systems. In addition to being low-throughput, traditional image-based analysis is mostly limited to error-prone qualitative or semi-quantitative ...
Evaluating the informativeness of deep learning annotations for human complex diseases.
Nature communications
Sep 17, 2020
Deep learning models have shown great promise in predicting regulatory effects from DNA sequence, but their informativeness for human complex diseases is not fully understood. Here, we evaluate genome-wide SNP annotations from two previous deep learn...
Modelling kidney disease using ontology: insights from the Kidney Precision Medicine Project.
Nature reviews. Nephrology
Sep 16, 2020
An important need exists to better understand and stratify kidney disease according to its underlying pathophysiology in order to develop more precise and effective therapeutic agents. National collaborative efforts such as the Kidney Precision Medic...
Deep-learning-assisted biophysical imaging cytometry at massive throughput delineates cell population heterogeneity.
Lab on a chip
Sep 16, 2020
The association of the intrinsic optical and biophysical properties of cells to homeostasis and pathogenesis has long been acknowledged. Defining these label-free cellular features obviates the need for costly and time-consuming labelling protocols t...
Usefulness of Semisupervised Machine-Learning-Based Phenogrouping to Improve Risk Assessment for Patients Undergoing Transcatheter Aortic Valve Implantation.
The American journal of cardiology
Sep 15, 2020
Semisupervised machine-learning methods are able to learn from fewer labeled patient data. We illustrate the potential use of a semisupervised automated machine-learning (AutoML) pipeline for phenotyping patients who underwent transcatheter aortic va...
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