Augusto González

The paper introduces Gene Deregulation Networks (GDNs) to analyze gene interventions in cancer by constructing networks from gene expression data without prior functional annotations.

The paper identifies NT-genes as optimal targets for partial cancer reversal by analyzing gene regulatory networks and their impact on tumor and normal states.

The paper analyzes gene expression dynamics in normal and tumor tissues, identifying N-genes and T-genes that mark transitions to malignancy in prostate, lung, and liver cancers.

The paper shows that a small number of genes can accurately classify tumor types, using a method based on rough set theory.

The paper analyzes the geometric relationship between normal tissue and cancer gene expression manifolds, showing they are disjoint and that tumors are translations of normal tissues along a cancer progression axis.