Algorithmic Assessment of Vaccine-Induced Selective Pressure and Its Implications on Future Vaccine Candidates

Posttrial assessment of a vaccine's selective pressure on infecting strains may be realized through a bioinformatic tool such as parsimony phylogenetic analysis. Following a failed gonococcal pilus vaccine trial of Neisseria gonorrhoeae, we conducted a phylogenetic analysis of pilin DNA and predicted peptide sequences from clinical isolates to assess the extent of the vaccine's effect on the type of field strains that the volunteers contracted. Amplified pilin DNA sequences from infected vaccinees, placebo recipients, and vaccine specimens were phylogenetically analyzed. Cladograms show that the vaccine peptides have diverged substantially from their paternal isolate by clustering distantly from each other. Pilin genes of the field clinical isolates were heterogeneous, and their peptides produced clades comprised of vaccinated and placebo recipients' strains indicating that the pilus vaccine did not exert any significant selective pressure on gonorrhea field strains. Furthermore, sequences of the semivariable and hypervariable regions pointed out heterotachous rates of mutation and substitution

Endometriosis Gene Expression Heterogeneity and Biosignature: A Phylogenetic Analysis

Endometriosis is a multifactorial disease with poorly understood etiology, and reflecting an evolutionary nature where genetic alterations accumulate throughout pathogenesis. Our objective was to characterize the heterogeneous pathological process using parsimony phylogenetics. Gene expression microarray data of ovarian endometriosis obtained from NCBI database were polarized and coded into derived (abnormal) and ancestral (normal) states. Such alterations are referred to as synapomorphies in a phylogenetic sense (or biomarkers). Subsequent gene linkage was modeled by Genomatix BiblioSphere Pathway software. A list of clonally shared derived (abnormal) expressions revealed the pattern of heterogeneity among specimens. In addition, it has identified disruptions within the major regulatory pathways including those involved in cell proliferation, steroidogenesis, angiogenesis, cytoskeletal organization and integrity, and tumorigenesis, as well as cell adhesion and migration. Furthermore, the analysis supported the potential central involvement of ESR2 in the initiation of endometriosis. The pathogenesis mapping showed that eutopic and ectopic lesions have different molecular biosignatures

Automating Model Acquisition by Fault Knowledge Re-use: Introducing the Diagnostic Remodeler Algorithm

The paper addresses the problem of automated model acquisition through the re-use of fault knowledge. The Diagnostic Remodeler (DR) algorithm has been implemented for the automated generation of behavioural component models with an explicit representation of function by re-using fault-based knowledge. DR re-uses as its first application the fault knowledge of the Jet Engine Troubleshooting Assistant (JETA). DR extracts a model of the Main Fuel System using real-world engine fault knowledge and two types of background knowledge as input: device dependent and device independent background knowledge. To demonstrate DR's generality, it has also been applied to a coffee maker fault knowledge base to extract the component models of a full coffee device.Cet article examine le probl\ue8me de l'acquisition automatis\ue9e de mod\ue8les au moyen de la r\ue9utilisation des connaissances bas\ue9es sur des fautes. L'algorithme Diagnostic Remodeler (DR) a \ue9t\ue9 impl\ue9ment\ue9 pour assurer la g\ue9n\ue9ration automatis\ue9e de mod\ue8les comportementaux constitutifs, avec repr\ue9sentation explicite des fonctions, en r\ue9utilisant les connaissance bas\ue9es sur des fautes. Comme premi\ue8re application, l'algorithme DR r\ue9utilise les connaissances bas\ue9es sur des fautes du syst\ue8me Jet Engine Troubleshooting Assistant (JETA). L'algorithme DR extrait un mod\ue8le du circuit de carburant en utilisant comme intrants les connaissances sur les fautes de moteurs r\ue9els et deux types de connaissances contextuelles, soit des connaissances d\ue9pendantes du dispositif et ind\ue9pendantes du dispositif. Pour d\ue9montrer la g\ue9n\ue9ralit\ue9 de DR, on l'a \ue9galement appliqu\ue9 \ue0 une base de connaissances sur les fautes d'une cafeti\ue8re pour extraire les mod\ue8les constitutifs d'une cafeti\ue8re compl\ue8te.NRC publication: Ye

An Approach for the Validation of Fault-based Knowledge Through the Automated Generation of Model-based Functional Knowledge.

The paper addresses the problem of validation of fault knowledge through automated model acquisition. The Diagnostic Remodeler (DR) algorithm has been implemented for the automated generation of behavioural component models with an explicit representation of function by re-using fault-based knowledge. DR re-uses as its first application the fault knowledge of the Jet Engine troubleshooting Assistant (JETA). DR extracts a model of the Main Fuel System using real-world engine fault knowledge and two types of background knowledge as input: device dependent and device independent background knowledge. The generated model uncovers gaps and inconsistencies in the fault-based knowledge. To demonstrate DR's generality, it was applied to coffee maker fault knowledge to extract the component models of a full coffee device. It is possible to use DR as a general means of validating fault knowledge.Cet article examine le probl\ue8me de la validation des connaissances bas\ue9es sur des fautes par l'acquisition automatis\ue9e de mod\ue8les. L'algorithme Diagnostic Remodeler (DR) a \ue9t\ue9 impl\ue9ment\ue9 pour assurer la g\ue9n\ue9ration automatis\ue9e de mod\ue8les comportementaux constitutifs, avec repr\ue9sentation explicite des fonctions, en r\ue9utilisant les connaissance bas\ue9es sur des fautes. Comme premi\ue8re application, l'algorithme DR r\ue9utilise les connaissances bas\ue9es sur des fautes du syst\ue8me Jet Engine Troubleshooting Assistant (JETA). L'algorithme DR extrait un mod\ue8le du circuit de carburant en utilisant comme intrants les connaissances sur les fautes de moteurs r\ue9els et deux types de connaissances contextuelles, soit des connaissances d\ue9pendantes du dispositif et ind\ue9pendantes du dispositif. Le mod\ue8le g\ue9n\ue9r\ue9 identifie les lacunes et les incoh\ue9rences dans les connaissances bas\ue9es sur les fautes. Pour d\ue9montrer la g\ue9n\ue9ralit\ue9 de DR, on l'a \ue9galement appliqu\ue9 \ue0 une base de connaissances sur les fautes d'une cafeti\ue8re pour extraire les mod\ue8les constitutifs d'une cafeti\ue8re compl\ue8te. Il est possible d'utiliser DR comme moyen de validation g\ue9n\ue9ral des connaissances bas\ue9es sur des fautes.NRC publication: Ye

Artificial Intelligence Techniques in Diagnosis: A Review of Approaches, Applications, Issues

NRC publication: Ye

A Generic Environment for Constructing Diagnostic Hierarchies

NRC publication: Ye