Farnoush Banaei‐Kashani

This paper introduces MT-LLE, a multi-task manifold learning method that improves disease modeling from longitudinal omics data by integrating clinical labels and temporal progression, achieving better clinical structure and progression forecasting than standard methods.

EC-Bench provides a benchmark for evaluating EC number prediction methods, comparing existing approaches and highlighting task-specific performance variations.

This paper introduces SGTCCA-Net, a framework for multi-omics network inference that addresses limitations of existing methods by incorporating higher-order correlations and improving computational efficiency.

The paper identifies proteomic networks and genetic variants associated with smoking and COPD phenotypes using multi-ethnic proteomic data and network-based GWAS.

The paper identifies heterogeneity in acute exacerbations of COPD using wearable data and develops a deep learning framework with weak supervision and privacy protection for early detection.

This study shows that proteomic data can be linked to specific genomes using pQTL knowledge, highlighting privacy risks and the need for protections similar to genomic data.