AIMC Topic: Phenotype

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HPO2Vec+: Leveraging heterogeneous knowledge resources to enrich node embeddings for the Human Phenotype Ontology.

Journal of biomedical informatics
BACKGROUND: In precision medicine, deep phenotyping is defined as the precise and comprehensive analysis of phenotypic abnormalities, aiming to acquire a better understanding of the natural history of a disease and its genotype-phenotype associations...

Identification of a Multiplex Biomarker Panel for Hypertrophic Cardiomyopathy Using Quantitative Proteomics and Machine Learning.

Molecular & cellular proteomics : MCP
Hypertrophic cardiomyopathy (HCM) is defined by pathological left ventricular hypertrophy (LVH). It is the commonest inherited cardiac condition and a significant number of high risk cases still go undetected until a sudden cardiac death (SCD) event....

Identifying pre-disease signals before metabolic syndrome in mice by dynamical network biomarkers.

Scientific reports
The establishment of new therapeutic strategies for metabolic syndrome is urgently needed because metabolic syndrome, which is characterized by several disorders, such as hypertension, increases the risk of lifestyle-related diseases. One approach is...

MetaNN: accurate classification of host phenotypes from metagenomic data using neural networks.

BMC bioinformatics
BACKGROUND: Microbiome profiles in the human body and environment niches have become publicly available due to recent advances in high-throughput sequencing technologies. Indeed, recent studies have already identified different microbiome profiles in...

Detection and Classification of Novel Renal Histologic Phenotypes Using Deep Neural Networks.

The American journal of pathology
With the advent and increased accessibility of deep neural networks (DNNs), complex properties of histologic images can be rigorously and reproducibly quantified. We used DNN-based transfer learning to analyze histologic images of periodic acid-Schif...

Identifying genetic determinants of complex phenotypes from whole genome sequence data.

BMC genomics
BACKGROUND: A critical goal in biology is to relate the phenotype to the genotype, that is, to find the genetic determinants of various traits. However, while simple monofactorial determinants are relatively easy to identify, the underpinnings of com...

Using artificial intelligence methods to speed up drug discovery.

Expert opinion on drug discovery
: Drug discovery is the process through which potential new compounds are identified by means of biology, chemistry, and pharmacology. Due to the high complexity of genomic data, AI techniques are increasingly needed to help reduce this and aid the a...

Non-Invasive Sensing of Nitrogen in Plant Using Digital Images and Machine Learning for ssp. L.

Sensors (Basel, Switzerland)
Monitoring plant nitrogen (N) in a timely way and accurately is critical for precision fertilization. The imaging technology based on visible light is relatively inexpensive and ubiquitous, and open-source analysis tools have proliferated. In this st...

Drug repositioning of herbal compounds via a machine-learning approach.

BMC bioinformatics
BACKGROUND: Drug repositioning, also known as drug repurposing, defines new indications for existing drugs and can be used as an alternative to drug development. In recent years, the accumulation of large volumes of information related to drugs and d...

WhoGEM: an admixture-based prediction machine accurately predicts quantitative functional traits in plants.

Genome biology
The explosive growth of genomic data provides an opportunity to make increased use of sequence variations for phenotype prediction. We have developed a prediction machine for quantitative phenotypes (WhoGEM) that overcomes some of the bottlenecks lim...