AIMC Topic: Phenotype

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PhenoRerank: A re-ranking model for phenotypic concept recognition pre-trained on human phenotype ontology.

Journal of biomedical informatics
The study aims at developing a neural network model to improve the performance of Human Phenotype Ontology (HPO) concept recognition tools. We used the terms, definitions, and comments about the phenotypic concepts in the HPO database to train our mo...

The Xenopus phenotype ontology: bridging model organism phenotype data to human health and development.

BMC bioinformatics
BACKGROUND: Ontologies of precisely defined, controlled vocabularies are essential to curate the results of biological experiments such that the data are machine searchable, can be computationally analyzed, and are interoperable across the biomedical...

DrABC: deep learning accurately predicts germline pathogenic mutation status in breast cancer patients based on phenotype data.

Genome medicine
BACKGROUND: Identifying breast cancer patients with DNA repair pathway-related germline pathogenic variants (GPVs) is important for effectively employing systemic treatment strategies and risk-reducing interventions. However, current criteria and ris...

Machine Learning analysis of high-grade serous ovarian cancer proteomic dataset reveals novel candidate biomarkers.

Scientific reports
Ovarian cancer is one of the most common gynecological malignancies, ranking third after cervical and uterine cancer. High-grade serous ovarian cancer (HGSOC) is one of the most aggressive subtype, and the late onset of its symptoms leads in most cas...

Machine Learning Identifies Pan-Cancer Landscape of Nrf2 Oxidative Stress Response Pathway-Related Genes.

Oxidative medicine and cellular longevity
BACKGROUND: Oxidative stress produced a large amount of reactive oxygen species (ROS), which played a pivotal role in balanced ability and determining cell fate. The activated Nrf2 signaling pathway that responds to the excessive ROS regulated the ex...

TCMPR: TCM Prescription Recommendation Based on Subnetwork Term Mapping and Deep Learning.

BioMed research international
Traditional Chinese medicine (TCM) has played an indispensable role in clinical diagnosis and treatment. Based on a patient's symptom phenotypes, computation-based prescription recommendation methods can recommend personalized TCM prescription using ...

Accurate and rapid prediction of tuberculosis drug resistance from genome sequence data using traditional machine learning algorithms and CNN.

Scientific reports
Effective and timely antibiotic treatment depends on accurate and rapid in silico antimicrobial-resistant (AMR) predictions. Existing statistical rule-based Mycobacterium tuberculosis (MTB) drug resistance prediction methods using bacterial genomic s...

Construction of genetic classification model for coronary atherosclerosis heart disease using three machine learning methods.

BMC cardiovascular disorders
BACKGROUND: Although the diagnostic method for coronary atherosclerosis heart disease (CAD) is constantly innovated, CAD in the early stage is still missed diagnosis for the absence of any symptoms. The gene expression levels varied during disease de...

GestaltMatcher facilitates rare disease matching using facial phenotype descriptors.

Nature genetics
Many monogenic disorders cause a characteristic facial morphology. Artificial intelligence can support physicians in recognizing these patterns by associating facial phenotypes with the underlying syndrome through training on thousands of patient pho...

PDGNet: Predicting Disease Genes Using a Deep Neural Network With Multi-View Features.

IEEE/ACM transactions on computational biology and bioinformatics
The knowledge of phenotype-genotype associations is crucial for the understanding of disease mechanisms. Numerous studies have focused on developing efficient and accurate computing approaches to predict disease genes. However, owing to the sparsenes...