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If your computer has an NVIDIA or ATI GPU with up-to-date driver software, and you are attached to a project that has GPU applications, BOINC will automatically use your GPU for computing when you are not at the computer. More details are here.
Every living organism consists of cells. Each cell is a living entity and within each cell are Organelle (parts of cell that have unique function). Genes are located inside the cell’s core. Genes encode the information of the cell’s characteristics such as its behavior. Every cell reproduces by dividing itself into two distinct yet identical cells. Cells use the information contained in a gene in order to correctly divide themselves.
A method we use to represent the gene expression process and causal relationships among genes.
By examining the gene network, the causality of the gene can be studied. The existence of some genes can possibly be caused by other genes.
Gene@home is a project aimed at expanding networks of genes and find causality relation among genes. Today, there are only a few methods available to perform gene expansion. This project uses PC-IM algorithm, a modification of PC Algorithm, to compute the causal relation of genes.
The expansion is performed using the PC-IM algorithm. The algorithm receives a gene network and a set of new genes, and tries to find relationships between them by exploiting the PC algorithm. PC-IM is an iterative implementation of the PC algorithm, which finds a gene network and studies its causal relationships, aimed to estimate if a list of new genes can have a causal relationship with an already known GRN. In particular, the new genes are partitioned in blocks and merged with the GRN. Afterwards the PC is applied on each block to look for new possible relationships. At the end of the process the algorithm self-evaluates its performance, and based on this, decides the final network to return as an output.
This project investigates an important topic in the bioinformatics: the causal relationship among genes. The algorithm presented in this research is tested on plants, namely Arabidopsis thaliana. In the future, algorithms are expected to be used in gene networks investigation in animals and eventually in humans. Eventually, the result of this research could contribute to the development of medical science to study gene causal relation in other species.
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