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Related Experiment Video

Updated: Jul 9, 2026

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Published on: June 3, 2013

Fairness-aware supervised hierarchical contrastive semantic learning for sexual dimorphism analysis.

Euiseong Ko¹, Sai Phani Parsa², Sai Chandra Kosaraju³

¹Department of Biomedical Informatics and Data Science, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, United States.

Bioinformatics (Oxford, England)

|July 7, 2026

Summary

This study introduces FairHICON, a novel AI approach to identify sex-specific biological features for unbiased precision medicine. FairHICON improves predictive accuracy and reduces performance gaps between sexes in genomic analyses.

Related Experiment Videos

Last Updated: Jul 9, 2026

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

Published on: June 3, 2013

Area of Science:

Genomics
Artificial Intelligence
Precision Medicine
Computational Biology

Background:

Sexual dimorphism significantly impacts disease susceptibility and outcomes.
Current AI genomic models often exhibit bias, failing to account for sex-specific biological mechanisms.
This limits the development of unbiased precision medicine.

Purpose of the Study:

To develop a fairness-aware AI model for discovering unbiased sex-common and sex-specific genomic features.
To address algorithmic bias in AI-based genomic models concerning sexual dimorphism.
To advance inclusive precision medicine by elucidating sex-specific molecular heterogeneity.

Main Methods:

Proposed a fairness-aware supervised hierarchical contrastive learning approach (FairHICON).
Utilized transcriptomic datasets for cancer and asthma.
Evaluated model performance against state-of-the-art benchmarks.

Main Results:

FairHICON significantly outperformed existing benchmarks, improving predictive performance by up to 9%.
The model effectively reduced the performance gap between male and female sexes.
Identified sex-specific pathways significantly improved patient survival stratification within sex groups.

Conclusions:

FairHICON successfully elucidates the molecular heterogeneity of sexual dimorphism.
The approach advances the development of unbiased and inclusive precision medicine.
The identified sex-specific features hold prognostic value for patient stratification.