ethnicity-analysis
There are 1 repository under ethnicity-analysis topic.
- facerec-bias-bfw
visionjo / facerec-bias-bfw
Source code and notebooks to reproduce experiments and benchmarks on Bias Faces in the Wild (BFW).
Language:Jupyter Notebook 44 namsor / namsor-python-sdk2
NamSor API v2 Python SDK - classify personal names accurately by gender, country of origin, or ethnicity.
Language:Python 34NamSor Python command line tools, to append gender, origin, diaspora or us 'race'/ethnicity to a CSV file.
Language:Python 14- Language:R 13
NamSor API v2 R SDK - classify personal names accurately by gender, country of origin, or ethnicity.
Language:R 12mmrl-me / mmrl-metadata
Experimental tagging schema for analysis of intersectionality, bias, and reception theories.
Language:JavaScript 8NamSor API v2 Java SDK - classify personal names accurately by gender, country of origin, or ethnicity.
Language:Java 4NamSor API v2 Java SDK - classify personal names accurately by gender, country of origin, or ethnicity.
Language:JavaScript 4NamSor command line tools, to append gender, origin, diaspora or us 'race'/ethnicity to a CSV file.
Language:Java 4anasserhussien / EthnicityRecognition-UTKFaces
EthnicityPredictor-UTK is a PyTorch-based project that focuses on predicting ethnicities from facial images using state-of-the-art ResNet architectures. Leveraging the UTKfaces dataset, the model is trained to recognize diverse facial features and make accurate ethnicity predictions.
Language:Python 3marislab / create-pptc-pdx-oncoprints
As part of an overall strategy for improving therapies for childhood cancers, the PPTC seeks to develop models for the types of tumors that will be encountered in early phase clinical testing by establishing patient derived xenografts (PDXs) from high-risk childhood cancers refractory to current standard of care treatments. Genomic profiling of these models is required to enable PPTC investigators to develop robust "responder hypotheses" when drug activity is observed. With funding provided by Alex's Lemonade Stand Foundation, we genomically characterize a major subset of 286 PDX models. We use whole exome sequencing, transcriptome sequencing, and SNPArray to characterize the tumor models. The focus on DNA and RNA sequencing data mirrors the current standard practice in most clinical diagnostics lab that use these technologies to detect the spectrum of targetable mutations, gene amplifications, and gene fusion events relevant to preclinical drug development.
Language:R 3- rainLanguage:R 3
- PCA-Ethnicity-Determination-from-WGS-Data
laura-budurlean / PCA-Ethnicity-Determination-from-WGS-Data
A pipeline utilizing PCA on 1000 genomes and WGS data from your own samples to determine or validate ancestry of an individual.
Language:Shell 2 InzamamAnwar / Learned-Features-are-better-for-Ethnicity-Classification
Implementation for paper, "Learned Features are better for Ethnicity classification"
Language:Matlab 1NamSor API v2 Ruby SDK - classify personal names accurately by gender, country of origin, or ethnicity.
Language:Ruby 1openheritage / open-plaques-diversity
OpenPlaques diversity analysis
Language:Jupyter Notebook 0- genetic-ancestry
ahmedmoustafa / genetic-ancestry
Genetic Ancestry
Language:Jupyter Notebook
