Halima Bensmail

The paper introduces a nonlinear multivariate model to predict gene expression across multiple tissues by incorporating tissue-tissue expression correlations, improving accuracy in multi-omics data analytics.

This study develops a network-based biomarker discovery framework for IBD using metagenomics data and shows it performs well, especially with small datasets.

The paper develops machine learning classifiers to predict HIV-1 resistance to 33 neutralizing antibodies with 96% accuracy using clinical isolates.

RGBM identifies transcriptional regulators of glioma subtypes by inferring gene regulatory networks from diverse data sources and outperforming existing methods on benchmark datasets.

The paper identifies cancer gene biomarkers associated with drug sensitivity and provides open-source tools for analysis.

This paper introduces DCD, a method to detect differential sub-networks in paired biological networks, showing it outperforms existing methods in simulated and real cancer datasets.

The paper introduces an improved method for detecting statistically significant network changes in biological networks, showing better performance on dense random geometric networks and application to glioma cancer regulatory networks.

The paper introduces a unified framework for representation and ranking in database retrieval and nearest neighbor classification, showing its advantage over independent methods.