AIMC Topic: Phenotype
Clear Filters Showing 471 to 480 of 922 articles
Combining Supervised and Unsupervised Machine Learning Methods for Phenotypic Functional Genomics Screening.
SLAS discovery : advancing life sciences R & D
May 13, 2020
There has been an increase in the use of machine learning and artificial intelligence (AI) for the analysis of image-based cellular screens. The accuracy of these analyses, however, is greatly dependent on the quality of the training sets used for bu...
PopPhy-CNN: A Phylogenetic Tree Embedded Architecture for Convolutional Neural Networks to Predict Host Phenotype From Metagenomic Data.
IEEE journal of biomedical and health informatics
May 11, 2020
Accurate prediction of the host phenotype from a metagenomic sample and identification of the associated microbial markers are important in understanding potential host-microbiome interactions related to disease initiation and progression. We introdu...
Radiomics and deep learning in lung cancer.
Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]
May 4, 2020
Lung malignancies have been extensively characterized through radiomics and deep learning. By providing a three-dimensional characterization of the lesion, models based on radiomic features from computed tomography (CT) and positron-emission tomograp...
Lung Neoplasms
Genotype
Computational Biology
Treatment Outcome
Tomography, X-Ray Computed
Deep Learning
Image Processing, Computer-Assisted
Radiosurgery
Radiotherapy
Radiation Injuries
Imaging Genomics
Neoplasm Staging
Neoplasm Recurrence, Local
Radiotherapy Planning, Computer-Assisted
False Positive Reactions
Humans
Forecasting
Phenotype
Databases, Factual
Prognosis
Training instance segmentation neural network with synthetic datasets for crop seed phenotyping.
Communications biology
Apr 15, 2020
In order to train the neural network for plant phenotyping, a sufficient amount of training data must be prepared, which requires time-consuming manual data annotation process that often becomes the limiting step. Here, we show that an instance segme...
PRIMAGE project: predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers.
European radiology experimental
Apr 3, 2020
PRIMAGE is one of the largest and more ambitious research projects dealing with medical imaging, artificial intelligence and cancer treatment in children. It is a 4-year European Commission-financed project that has 16 European partners in the consor...
Machine learning, the kidney, and genotype-phenotype analysis.
Kidney international
Apr 1, 2020
With biomedical research transitioning into data-rich science, machine learning provides a powerful toolkit for extracting knowledge from large-scale biological data sets. The increasing availability of comprehensive kidney omics compendia (transcrip...
Dimensionality reduction reveals fine-scale structure in the Japanese population with consequences for polygenic risk prediction.
Nature communications
Mar 26, 2020
The diversity in our genome is crucial to understanding the demographic history of worldwide populations. However, we have yet to know whether subtle genetic differences within a population can be disentangled, or whether they have an impact on compl...
Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data.
BMC bioinformatics
Mar 20, 2020
BACKGROUND: The ability to confidently predict health outcomes from gene expression would catalyze a revolution in molecular diagnostics. Yet, the goal of developing actionable, robust, and reproducible predictive signatures of phenotypes such as cli...
Unsupervised Machine Learning Reveals Novel Traumatic Brain Injury Patient Phenotypes with Distinct Acute Injury Profiles and Long-Term Outcomes.
Journal of neurotrauma
Mar 11, 2020
The heterogeneity of traumatic brain injury (TBI) remains a core challenge for the success of interventional clinical trials. Data-driven approaches for patient stratification may help to identify TBI patient phenotypes during the acute injury period...
Artificial neural networks allow response prediction in squamous cell carcinoma of the scalp treated with radiotherapy.
Journal of the European Academy of Dermatology and Venereology : JEADV
Mar 4, 2020
BACKGROUND: Epithelial neoplasms of the scalp account for approximately 2% of all skin cancers and for about 10-20% of the tumours affecting the head and neck area. Radiotherapy is suggested for localized cutaneous squamous cell carcinomas (cSCC) wit...
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