A view of Estimation of Distribution Algorithms through the lens of Expectation-Maximization

We show that a large class of Estimation of Distribution Algorithms, including, but not limited to, Covariance Matrix Adaption, can be written as a Monte Carlo Expectation-Maximization algorithm, and as exact EM in the limit of infinite samples. Because EM sits on a rigorous statistical foundation and has been thoroughly analyzed, this connection provides a new coherent framework with which to reason about EDAs

The Automated Instrumentation and Monitoring System (AIMS) reference manual

Whether a researcher is designing the 'next parallel programming paradigm,' another 'scalable multiprocessor' or investigating resource allocation algorithms for multiprocessors, a facility that enables parallel program execution to be captured and displayed is invaluable. Careful analysis of execution traces can help computer designers and software architects to uncover system behavior and to take advantage of specific application characteristics and hardware features. A software tool kit that facilitates performance evaluation of parallel applications on multiprocessors is described. The Automated Instrumentation and Monitoring System (AIMS) has four major software components: a source code instrumentor which automatically inserts active event recorders into the program's source code before compilation; a run time performance-monitoring library, which collects performance data; a trace file animation and analysis tool kit which reconstructs program execution from the trace file; and a trace post-processor which compensate for data collection overhead. Besides being used as prototype for developing new techniques for instrumenting, monitoring, and visualizing parallel program execution, AIMS is also being incorporated into the run-time environments of various hardware test beds to evaluate their impact on user productivity. Currently, AIMS instrumentors accept FORTRAN and C parallel programs written for Intel's NX operating system on the iPSC family of multi computers. A run-time performance-monitoring library for the iPSC/860 is included in this release. We plan to release monitors for other platforms (such as PVM and TMC's CM-5) in the near future. Performance data collected can be graphically displayed on workstations (e.g. Sun Sparc and SGI) supporting X-Windows (in particular, Xl IR5, Motif 1.1.3)

Quantification of periodontal attachment at single-rooted teeth

. The measurement process of attachment Joss has been criticized in recent years. Problems with clinical interpretation, precision of the measurement, and statistical manipulation of the obtained data, are some of the problems associated with the present methodology. The purpose of the present study was to propose an alternative measurement process which addresses some of the existing problems by estimating the lost attachment surface area (LAS) and the remaining attachment surface area (RAS) from a combination of clinical measurements. The results show that a linear combination of several sources of clinical information can be used to predict RAS and LAS. A diagnostic model for LAS (R 2 =81.5%) predicts the square root of LAS with information obtained from bucco-lingual attachment level measurements, the radiographic lost attachment area, the gingivitis index and the radiographic tooth length. This model increases the precision of the estimate of LAS by a factor of 1.86 when compared to the estimate of LAS using only attachment level measurements, A diagnostic model for RAS (R 2 =75.5%) predicts the square root of RAS with the information obtained from the remaining radiographic attachment area, the gingivitis index and the mobility index. Both linear inference models are constructed with measurements of anatomical landmarks to avoid the discrepancy between anatomical and clinical measurements in the produced estimates. It is concluded that modeling of periodontal data provides a simple, inexpensive, and precise diagnostic tool for predicting the lost and the remaining periodontal attachment of single-rooted teeth. Measurement processes of this type could provide a convincing, basis for the evaluation of clinical decisions and research questions.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72962/1/j.1600-051X.1989.tb01645.x.pd

A Statistical Framework for Modeling HLA-Dependent T Cell Response Data

The identification of T cell epitopes and their HLA (human leukocyte antigen) restrictions is important for applications such as the design of cellular vaccines for HIV. Traditional methods for such identification are costly and time-consuming. Recently, a more expeditious laboratory technique using ELISpot assays has been developed that allows for rapid screening of specific responses. However, this assay does not directly provide information concerning the HLA restriction of a response, a critical piece of information for vaccine design. Thus, we introduce, apply, and validate a statistical model for identifying HLA-restricted epitopes from ELISpot data. By looking at patterns across a broad range of donors, in conjunction with our statistical model, we can determine (probabilistically) which of the HLA alleles are likely to be responsible for the observed reactivities. Additionally, we can provide a good estimate of the number of false positives generated by our analysis (i.e., the false discovery rate). This model allows us to learn about new HLA-restricted epitopes from ELISpot data in an efficient, cost-effective, and high-throughput manner. We applied our approach to data from donors infected with HIV and identified many potential new HLA restrictions. Among 134 such predictions, six were confirmed in the lab and the remainder could not be ruled as invalid. These results shed light on the extent of HLA class I promiscuity, which has significant implications for the understanding of HLA class I antigen presentation and vaccine development

Comparison of Randomly Cloned and Whole Genomic DNA Probes for the Detection of Porphyromonas Gingivalis and Bacteroides Forsythus

Whole genomic and randomly-cloned DNA probes for two fastidious periodontal pathogens, Porphyromonas gingivalis and Bacteroides forsythus were labeled with digoxigenin and detected by a colorimetric method. The specificity and sensitivity of the whole genomic and cloned probes were compared. The cloned probes were highly specific compared to the whole genomic probes. A significant degree of cross-reactivity with Bacteroides species. Capnocytophaga sp. and Prevotella sp. was observed with the whole genomic probes. The cloned probes were less sensitive than the whole genomic probes and required at least 106 target cells or a minimum of 10 ng of target DNA to be detected during hybridization. Although a ten-fold increase in sensitivity was obtained with the whole genomic probes, cross-hybridization to closely related species limits their reliability in identifying target bacteria in subgingival plaque samples

The benefits of selecting phenotype-specific variants for applications of mixed models in genomics

Applications of linear mixed models (LMMs) to problems in genomics include phenotype prediction, correction for confounding in genome-wide association studies, estimation of narrow sense heritability, and testing sets of variants (e.g., rare variants) for association. In each of these applications, the LMM uses a genetic similarity matrix, which encodes the pairwise similarity between every two individuals in a cohort. Although ideally these similarities would be estimated using strictly variants relevant to the given phenotype, the identity of such variants is typically unknown. Consequently, relevant variants are excluded and irrelevant variants are included, both having deleterious effects. For each application of the LMM, we review known effects and describe new effects showing how variable selection can be used to mitigate them.National Institute on Aging (Brain eQTL Study (dbGaP phs000249.v1.p1)

Greater power and computational efficiency for kernel-based association testing of sets of genetic variants

Motivation: Set-based variance component tests have been identified as a way to increase power in association studies by aggregating weak individual effects. However, the choice of test statistic has been largely ignored even though it may play an important role in obtaining optimal power. We compared a standard statistical test-a score test-with a recently developed likelihood ratio (LR) test. Further, when correction for hidden structure is needed, or gene-gene interactions are sought, state-of-the art algorithms for both the score and LR tests can be computationally impractical. Thus we develop new computationally efficient methods. Results: After reviewing theoretical differences in performance between the score and LR tests, we find empirically on real data that the LR test generally has more power. In particular, on 15 of 17 real datasets, the LR test yielded at least as many associations as the score test-up to 23 more associations-whereas the score test yielded at most one more association than the LR test in the two remaining datasets. On synthetic data, we find that the LR test yielded up to 12% more associations, consistent with our results on real data, but also observe a regime of extremely small signal where the score test yielded up to 25% more associations than the LR test, consistent with theory. Finally, our computational speedups now enable (i) efficient LR testing when the background kernel is full rank, and (ii) efficient score testing when the background kernel changes with each test, as for gene-gene interaction tests. The latter yielded a factor of 2000 speedup on a cohort of size 13 500. Availability: Software available at http://research.microsoft.com/en-us/um/redmond/projects/MSCompBio/Fastlmm/. Contact: [email protected] Supplementary Information: Supplementary data are available at Bioinformatics online