AIMC Topic: Phenotype
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MITK Phenotyping: An open-source toolchain for image-based personalized medicine with radiomics.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Dec 31, 2018
Radiomics - The extraction of quantitative features from radiologic images - shows increasing potential in contributing to modern personalized medicine approaches. MITK Phenotyping is an openly distributed radiomics framework implementing an exhausti...
A Machine Learning Approach to Reveal the NeuroPhenotypes of Autisms.
International journal of neural systems
Dec 13, 2018
Although much research has been undertaken, the spatial patterns, developmental course, and sexual dimorphism of brain structure associated with autism remains enigmatic. One of the difficulties in investigating differences between the sexes in autis...
Identifying mouse developmental essential genes using machine learning.
Disease models & mechanisms
Dec 13, 2018
The genes that are required for organismal survival are annotated as 'essential genes'. Identifying all the essential genes of an animal species can reveal critical functions that are needed during the development of the organism. To inform studies o...
EHR phenotyping via jointly embedding medical concepts and words into a unified vector space.
BMC medical informatics and decision making
Dec 12, 2018
BACKGROUND: There has been an increasing interest in learning low-dimensional vector representations of medical concepts from Electronic Health Records (EHRs). Vector representations of medical concepts facilitate exploratory analysis and predictive ...
Identifying disease genes using machine learning and gene functional similarities, assessed through Gene Ontology.
PloS one
Dec 10, 2018
Identifying disease genes from a vast amount of genetic data is one of the most challenging tasks in the post-genomic era. Also, complex diseases present highly heterogeneous genotype, which difficult biological marker identification. Machine learnin...
Disease comorbidity-guided drug repositioning: a case study in schizophrenia.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Dec 5, 2018
UNLABELLED: The key to any computational drug repositioning is the availability of relevant data in machine-understandable format. While large amount of genetic, genomic and chemical data are publicly available, large-scale higher-level disease and d...
Phenotyping through Semi-Supervised Tensor Factorization (PSST).
AMIA ... Annual Symposium proceedings. AMIA Symposium
Dec 5, 2018
A computational phenotype is a set of clinically relevant and interesting characteristics that describe patients with a given condition. Various machine learning methods have been proposed to derive phenotypes in an automatic, high-throughput manner....
Sharing the Right Data Right: A Symbiosis with Machine Learning.
Trends in plant science
Nov 26, 2018
In 2014 plant phenotyping research was not benefiting from the machine learning (ML) revolution because appropriate data were lacking. We report the success of the first open-access dataset suitable for ML in image-based plant phenotyping suitable fo...
High rates of atherogenic dyslipidemia, β-cell function loss, and microangiopathy among Turkish migrants with T2DM.
Diabetes & metabolic syndrome
Nov 24, 2018
AIMS: Non-Caucasian migrants require dedicated approaches in diabetes management due to specific genetic; socio-cultural; demographic and anthropological determinants. Documenting such phenotypes allows for better understanding unmet needs and manage...
Machine Learning Methods as a Tool for Predicting Risk of Illness Applying Next-Generation Sequencing Data.
Risk analysis : an official publication of the Society for Risk Analysis
Nov 21, 2018
Next-generation sequencing (NGS) data present an untapped potential to improve microbial risk assessment (MRA) through increased specificity and redefinition of the hazard. Most of the MRA models do not account for differences in survivability and vi...
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