The PubMed database offers an extensive set of publication data that can be useful, yet inherently complex to use without automated computational techniques. Data repositories such as the Genomic Data Commons (GDC) and the Gene Expression Omnibus (GEO) offer experimental data storage and retrieval as well as curated gene expression profiles. Genetic interaction databases, including Reactome and Ingenuity Pathway Analysis, offer pathway and experiment data analysis using data curated from these publications and data repositories. We have created a method to generate and analyze consensus networks, inferring potential gene interactions, using large numbers of Bayesian networks generated by data mining publications in the PubMed database. Through the concept of network resolution, these consensus networks can be tailored to represent possible genetic interactions. We designed a set of experiments to confirm that our method is stable across variation in both sample and topological input sizes. Using gene product interactions from the KEGG pathway database and data mining PubMed publication abstracts, we verify that regardless of the network resolution or the inferred consensus network, our method is capable of inferring meaningful gene interactions through consensus Bayesian network generation with multiple, randomized topological orderings. Our method can not only confirm the existence of currently accepted interactions, but has the potential to hypothesize new ones as well. We show our method confirms the existence of known gene interactions such as JAK-STAT-PI3K-AKT-mTOR, infers novel gene interactions such as RAS- Bcl-2 and RAS-AKT, and found significant pathway-pathway interactions between the JAK-STAT signaling and Cardiac Muscle Contraction KEGG pathways.
An experimental-calculation procedure for determining the kinetic and energy activation criteria for assessing the wear resistance and compatibility of triboconjunction materials using the kinetic three-stage triboreaction is presented. The developed method for tribo-kinetic tests to determine the kinetic characteristics (order and rate constants) and activation energies for all three stages of the triboreaction was tried experimentally for steels ShKh-15 and 45, aviation fuel RT, aviation hydraulic oil AOH-10 and aviation oil MK-8 added with oleic acid. The conducted tribo-kinetic tests established kinetic and activation energy criteria for assessing the wear resistance of ShKh-15 steel and antiwear properties of RT and AOH-10 lubricants, as well as kinetic criteria for assessing the wear resistance of steel 45 and antiwear properties of MK-8 with oleic acid. Also, using the developed method for tribokinetic tests, the kinetic and activation energies of ShKh-15 in long-term storage aviation fuels "TS-1" and "TS-1*", kinetic criteria and activation energy for the second stage of the triboreaction, chemical modification ShKh-15 in the medium of "TS-1", as well as the established kinetic and activation energy of the triboreaction, that is, surface destruction, in fact, wear of steel 45 during reciprocating motion.
Bayesian networks may be utilized to infer genetic relations among genes. This has proven useful in providing information about how gene interactions influence life. However, Bayesian network learning is slow as it is an NP-hard algorithm. K2, a search space reduction, helps speed up the algorithm but may introduce bias. The bias arises from the fact that K2 enforces topologies which makes it impossible for subsequent nodes to become parents of previous nodes while the algorithm builds the network. To eliminate this bias, multiple Bayesian networks must be computed to ensure every node has the chance to be a parent to every other node. The purpose of this paper is to propose a hybrid algorithm for generating consensus networks utilizing OpenMP and MPI. This paper evaluates the parallelization of network generation and provides commentary on learning and implementing OpenMP and MPI. The OpenMP and MPI accelerations are implemented in a single library and can be switched on or off. These accelerations are for computing multiple Bayesian networks simultaneously. Methods are developed and tested to evaluate the results of the implemented accelerations. The results show generating networks across multiple cores results in a linear speed-up with negligible overhead. Distributing the generation of networks across multiple machines also introduces linear speed-up, but results in additional overhead.
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