Optimizing therapeutic targets for breast cancer using boolean network models.

Studying gene regulatory networks associated with cancer provides valuable insights for therapeutic purposes, given that cancer is fundamentally a genetic disease. However, as the number of genes in the system increases, the complexity arising from the interconnections between network components grows exponentially. In this study, using Boolean logic to adjust the existing relationships between network components has facilitated simplifying the modeling process, enabling the generation of attractors that represent cell phenotypes based on breast cancer RNA-seq data.

Can the Clobber game become a classroom-based tool for screening students' executive functions?

The use of games for cognitive screening is not new and involves employing simple tasks as well as virtual reality. In this work, we introduce the use of the combinatorial game Clobber, created by the mathematicians Albert, Grossman, Nowakowski and Wolfe in 2001 in a classroom-based experiment and analyzed how it can assess cognitive functions. Specifically, this study tries to address how the use of the Clobber game can target executive functions (EFs) and why it may be a valuable game to assess EFs.

Data-Driven Modeling of Breast Cancer Tumors Using Boolean Networks.

Cancer is a genomic disease involving various intertwined pathways with complex cross-communication links. Conceptually, this complex interconnected system forms a network, which allows one to model the dynamic behavior of the elements that characterize it to describe the entire system's development in its various evolutionary stages of carcinogenesis. Knowing the activation or inhibition status of the genes that make up the network during its temporal evolution is necessary for the rational intervention on the critical factors for controlling the system's dynamic evolution.

A language independent acronym extraction from biomedical texts with hidden Markov models.

This paper proposes to model the extraction of acronyms and their meaning from unstructured text as a stochastic process using Hidden Markov Models (HMM). The underlying, or hidden, chain is derived from the acronym where the states in the chain are made by the acronyms characters. The transition between two states happens when the origin state emits a signal.

The acceptance of virtual reality devices for cognitive rehabilitation: a report of positive results with schizophrenia.

This study presents a process of virtual environment development supported by a cognitive model that is specific to cognitive deficits of diverse disorders or traumatic brain injury, and evaluates the acceptance of computer devices by a group of schizophrenic patients. The subjects that participated in this experiment accepted to work with computers and immersive glasses and demonstrated a high level of interest in the proposed tasks. No problems of illness have been observed.