Error curves for evaluating the quality of feature rankings.

In this article, we propose a method for evaluating feature ranking algorithms. A feature ranking algorithm estimates the importance of descriptive features when predicting the target variable, and the proposed method evaluates the correctness of these importance values by computing the error measures of two chains of predictive models. The models in the first chain are built on nested sets of top-ranked features, while the models in the other chain are built on nested sets of bottom ranked features.

Biomarker discovery by feature ranking: Evaluation on a case study of embryonal tumors.

The task of biomarker discovery is best translated to the machine learning task of feature ranking. Namely, the goal of biomarker discovery is to identify a set of potentially viable targets for addressing a given biological status. This is aligned with the definition of feature ranking and its goal - to produce a list of features ordered by their importance for the target concept.

Morphogenesis in robot swarms.

Morphogenesis allows millions of cells to self-organize into intricate structures with a wide variety of functional shapes during embryonic development. This process emerges from local interactions of cells under the control of gene circuits that are identical in every cell, robust to intrinsic noise, and adaptable to changing environments. Constructing human technology with these properties presents an important opportunity in swarm robotic applications ranging from construction to exploration.

Neuroblastoma tumorigenesis is regulated through the Nm23-H1/h-Prune C-terminal interaction.

Nm23-H1 is one of the most interesting candidate genes for a relevant role in Neuroblastoma pathogenesis. H-Prune is the most characterized Nm23-H1 binding partner, and its overexpression has been shown in different human cancers. Our study focuses on the role of the Nm23-H1/h-Prune protein complex in Neuroblastoma.

ADP-ribosylation factor guanine nucleotide-exchange factor 2 (ARFGEF2): a new potential biomarker in Huntington's disease.

Microarray searches have revealed potential genetic biomarkers in a wide variety of human diseases. Identification of biomarkers for disease status is particularly important in chronic neurodegenerative diseases where brain tissue cannot be sampled. A previous study identified 12 genes from microarray analysis as associated with Huntington's disease, although the relationships had not been validated.