AI Medical Compendium Journal:
Genetics

Showing 1 to 10 of 10 articles

Expanding biobank pharmacogenomics through machine learning calls of structural variation.

Genetics
Biobanks linking genetic data with clinical health records provide exciting opportunities for pharmacogenomic (PGx) research on genetic variation and drug response. Designed as central and multi-use resources, biobanks can facilitate diverse PGx rese...

Machine Learning Techniques for Classifying the Mutagenic Origins of Point Mutations.

Genetics
There is increasing interest in developing diagnostics that discriminate individual mutagenic mechanisms in a range of applications that include identifying population-specific mutagenesis and resolving distinct mutation signatures in cancer samples....

Can Deep Learning Improve Genomic Prediction of Complex Human Traits?

Genetics
The genetic analysis of complex traits does not escape the current excitement around artificial intelligence, including a renewed interest in "deep learning" (DL) techniques such as Multilayer Perceptrons (MLPs) and Convolutional Neural Networks (CNN...

Genomic Prediction for Quantitative Traits Is Improved by Mapping Variants to Gene Ontology Categories in Drosophila melanogaster.

Genetics
Predicting individual quantitative trait phenotypes from high-resolution genomic polymorphism data is important for personalized medicine in humans, plant and animal breeding, and adaptive evolution. However, this is difficult for populations of unre...

Inference and Analysis of Population Structure Using Genetic Data and Network Theory.

Genetics
Clustering individuals to subpopulations based on genetic data has become commonplace in many genetic studies. Inference about population structure is most often done by applying model-based approaches, aided by visualization using distance-based app...

Computer-Assisted Transgenesis of Caenorhabditis elegans for Deep Phenotyping.

Genetics
A major goal in the study of human diseases is to assign functions to genes or genetic variants. The model organism Caenorhabditis elegans provides a powerful tool because homologs of many human genes are identifiable, and large collections of geneti...

ASiDentify (ASiD): a machine learning model to predict new autism spectrum disorder risk genes.

Genetics
Autism spectrum disorder (ASD) is a neurodevelopmental disorder that affects nearly 3% of children and has a strong genetic component. While hundreds of ASD risk genes have been identified through sequencing studies, the genetic heterogeneity of ASD ...

The Unified Phenotype Ontology : a framework for cross-species integrative phenomics.

Genetics
Phenotypic data are critical for understanding biological mechanisms and consequences of genomic variation, and are pivotal for clinical use cases such as disease diagnostics and treatment development. For over a century, vast quantities of phenotype...

Interpreting generative adversarial networks to infer natural selection from genetic data.

Genetics
Understanding natural selection and other forms of non-neutrality is a major focus for the use of machine learning in population genetics. Existing methods rely on computationally intensive simulated training data. Unlike efficient neutral coalescent...

High-throughput genetic manipulation of multicellular organisms using a machine-vision guided embryonic microinjection robot.

Genetics
Microinjection is a technique used for transgenesis, mutagenesis, cell labeling, cryopreservation, and in vitro fertilization in multiple single and multicellular organisms. Microinjection requires specialized skills and involves rate-limiting and la...