Generalized adjacency and the conservation of gene clusters in genetic networks defined by synthetic lethals

<p>Abstract</p> <p>Background</p> <p>Given genetic networks derived from two genomes, it may be difficult to decide if their local structures are similar enough in both genomes to infer some ancestral configuration or some conserved functional relationships. Current methods all depend on searching for identical substructures.</p> <p>Methods</p> <p>We explore a generalized vertex proximity criterion, and present analytic and probability results for the comparison of random lattice networks.</p> <p>Results</p> <p>We apply this criterion to the comparison of the genetic networks of two evolutionarily divergent yeasts, <it>Saccharomyces cerevisiae </it>and <it>Schizosaccharomyces pombe</it>, derived using the Synthetic Genetic Array screen. We show that the overlapping parts of the networks of the two yeasts share a common structure beyond the shared edges. This may be due to their conservation of redundant pathways containing many synthetic lethal pairs of genes.</p> <p>Conclusions</p> <p>Detecting the shared generalized adjacency clusters in the genetic networks of the two yeasts show that this analytical construct can be a useful tool in probing conserved network structure across divergent genomes.</p

Application of kernel functions for accurate similarity search in large chemical databases

Background Similaritysearch in chemical structure databases is an important problem with many applications in chemical genomics, drug design, and efficient chemical probe screening among others. It is widely believed that structure based methods provide an efficient way to do the query. Recently various graph kernel functions have been designed to capture the intrinsic similarity of graphs. Though successful in constructing accurate predictive and classification models, graph kernel functions can not be applied to large chemical compound database due to the high computational complexity and the difficulties in indexing similarity search for large databases. Results To bridge graph kernel function and similarity search in chemical databases, we applied a novel kernel-based similarity measurement, developed in our team, to measure similarity of graph represented chemicals. In our method, we utilize a hash table to support new graph kernel function definition, efficient storage and fast search. We have applied our method, named G-hash, to large chemical databases. Our results show that the G-hash method achieves state-of-the-art performance for k-nearest neighbor (k-NN) classification. Moreover, the similarity measurement and the index structure is scalable to large chemical databases with smaller indexing size, and faster query processing time as compared to state-of-the-art indexing methods such as Daylight fingerprints, C-tree and GraphGrep. Conclusions Efficient similarity query processing method for large chemical databases is challenging since we need to balance running time efficiency and similarity search accuracy. Our previous similarity search method, G-hash, provides a new way to perform similarity search in chemical databases. Experimental study validates the utility of G-hash in chemical databases

The Spectral Graph Wavelet Transform: Fundamental Theory and Fast Computation

International audienc

Precaution against terrorism

International audienceStunned by the terrorist attacks of September 11, 2001, the Bush administration adopted a new National Security Strategy in September 2002. The UK government took a similar stance. This new strategy calls for anticipatory attacks against potential enemies with uncertain capacities and intentions, even before their threat is imminent. Rather than wait for evidence of weapons of mass destruction, it shifts the burden of proof, obliging "rogue states to show that they do not harbor weapons of mass destruction or terrorist cells, or else face the possibility of attack. This new strategy amounts to the adoption of the Precautionary Principle against the risk of terrorism. We offer two main conclusions about precaution against terrorism. First, any action taken to reduce a target risk always poses the introduction of countervailing risks. Moreover, a precautionary approach to terrorism is likely to entail larger, more expensive interventions, so the expected opportunity costs are likely to be higher. While considering worst-case scenarios is important for the development of sound policy, taking action based only on worst-case thinking can introduce unforeseen dangers and costs. We argue that a better approach to managing risk involves an assessment of the full portfolio of risks - those reduced by the proposed intervention, as well as those increased. We argue that decision makers developing counterterrorism measures need mechanisms to ensure that sensible risk analysis precedes precautionary actions. Such a mechanism currently exists to review and improve or reject proposed precautionary measures against health and environmental risks, but not, so far, for counterterrorism and national security policies. We urge the creation of such a review mechanism

The MAQC-II Project: A comprehensive study of common practices for the development and validation of microarray-based predictive models.

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis

The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis. © 2010 Nature America, Inc. All rights reserved.0SCOPUS: ar.jinfo:eu-repo/semantics/publishe