AIMC Topic: Phenotype

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Cancer immunotherapy response prediction from multi-modal clinical and image data using semi-supervised deep learning.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: Immunotherapy is a standard treatment for many tumor types. However, only a small proportion of patients derive clinical benefit and reliable predictive biomarkers of immunotherapy response are lacking. Although deep learning ...

One hundred important questions facing plant science derived using a large language model.

Trends in plant science
Artificial intelligence (AI) is advancing rapidly and continually evolving in various fields. Recently, the release of ChatGPT has sparked significant public interest. In this study, we revisit the '100 Important Questions Facing Plant Science' by le...

DeepCGP: A Deep Learning Method to Compress Genome-Wide Polymorphisms for Predicting Phenotype of Rice.

IEEE/ACM transactions on computational biology and bioinformatics
Genomic selection (GS) is expected to accelerate plant and animal breeding. During the last decade, genome-wide polymorphism data have increased, which has raised concerns about storage cost and computational time. Several individual studies have att...

Echo2Pheno: a deep-learning application to uncover echocardiographic phenotypes in conscious mice.

Mammalian genome : official journal of the International Mammalian Genome Society
Echocardiography, a rapid and cost-effective imaging technique, assesses cardiac function and structure. Despite its popularity in cardiovascular medicine and clinical research, image-derived phenotypic measurements are manually performed, requiring ...

FooDis: A food-disease relation mining pipeline.

Artificial intelligence in medicine
Nowadays, it is really important and crucial to follow the new biomedical knowledge that is presented in scientific literature. To this end, Information Extraction pipelines can help to automatically extract meaningful relations from textual data tha...

Prediction of Kv11.1 potassium channel PAS-domain variants trafficking via machine learning.

Journal of molecular and cellular cardiology
Congenital long QT syndrome (LQTS) is characterized by a prolonged QT-interval on an electrocardiogram (ECG). An abnormal prolongation in the QT-interval increases the risk for fatal arrhythmias. Genetic variants in several different cardiac ion chan...

Rapid and Accurate Identification of Cell Phenotypes of Different Drug Mechanisms by Using Single-Cell Fluorescence Images Via Deep Learning.

Analytical chemistry
Identification of a drug mechanism is vital for drug development. However, it often resorts to the expensive and cumbersome omics methods along with complex data analysis. Herein, we developed a methodology to analyze organelle staining images of sin...

EmbryoNet: using deep learning to link embryonic phenotypes to signaling pathways.

Nature methods
Evolutionarily conserved signaling pathways are essential for early embryogenesis, and reducing or abolishing their activity leads to characteristic developmental defects. Classification of phenotypic defects can identify the underlying signaling mec...

Characterizing Patient Representations for Computational Phenotyping.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Patient representation learning methods create rich representations of complex data and have potential to further advance the development of computational phenotypes (CP). Currently, these methods are either applied to small predefined concept sets o...

Relating enhancer genetic variation across mammals to complex phenotypes using machine learning.

Science (New York, N.Y.)
Protein-coding differences between species often fail to explain phenotypic diversity, suggesting the involvement of genomic elements that regulate gene expression such as enhancers. Identifying associations between enhancers and phenotypes is challe...