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Unified segmentation.

A probabilistic framework is presented that enables image registration, tissue classification, and bias correction to be combined within the same generative model. A derivation of a log-likelihood objective function for the unified model is provided. The model is based on a mixture of Gaussians and is extended to incorporate a smooth intensity variation and nonlinear registration with tissue probability maps. A strategy for optimising the model parameters is described, along with the requisite partial derivatives of the objective function.

Algorithms↗

Penalized-likelihood sinogram smoothing for low-dose CT.

We have developed a sinogram smoothing approach for low-dose computed tomography (CT) that seeks to estimate the line integrals needed for reconstruction from the noisy measurements by maximizing a penalized-likelihood objective function. The maximization is performed by an algorithm derived by use of the separable paraboloidal surrogates framework. The approach overcomes some of the computational limitations of a previously proposed spline-based penalized-likelihood sinogram smoothing approach, and it is found to yield better resolution-variance tradeoffs than this spline-based approach as well an existing adaptive filtering approach. Such sinogram smoothing approaches could be valuable when applied to the low-dose data acquired in CT screening exams, such as those being considered for lung-nodule detection.

Algorithms↗

Stochastic organization of output codes in multiclass learning problems.

The best-known decomposition schemes of multiclass learning problems are one per class coding (OPC) and error-correcting output coding (ECOC). Both methods perform a prior decomposition, that is, before training of the classifier takes place. The impact of output codes on the inferred decision rules can be experienced only after learning. Therefore, we present a novel algorithm for the code design of multiclass learning problems. This algorithm applies a maximum-likelihood objective function in conjunction with the expectation-maximization (EM) algorithm. Minimizing the augmented objective function yields the optimal decomposition of the multiclass learning problem in two-class problems. Experimental results show the potential gain of the optimized output codes over OPC or ECOC methods.

Algorithms↗

A fast and scalable radiation hybrid map construction and integration strategy.

This paper describes a fast and scalable strategy for constructing a radiation hybrid (RH) map from data on different RH panels. The maps on each panel are then integrated to produce a single RH map for the genome. Recurring problems in using maps from several sources are that the maps use different markers, the maps do not place the overlapping markers in same order, and the objective functions for map quality are incomparable. We use methods from combinatorial optimization to develop a strategy that addresses these issues. We show that by the standard objective functions of obligate chromosome breaks and maximum likelihood, software for the traveling salesman problem produces RH maps with better quality much more quickly than using software specifically tailored for RH mapping. We use known algorithms for the longest common subsequence problem as part of our map integration strategy. We demonstrate our methods by reconstructing and integrating maps for markers typed on the Genebridge 4 (GB4) and the Stanford G3 panels publicly available from the RH database. We compare map quality of our integrated map with published maps for GB4 panel and G3 panel by considering whether markers occur in the same order on a map and in DNA sequence contigs submitted to GenBank. We find that all of the maps are inconsistent with the sequence data for at least 50% of the contigs, but our integrated maps are more consistent. The map integration strategy not only scales to multiple RH maps but also to any maps that have comparable criteria for measuring map quality. Our software improves on current technology for doing RH mapping in areas of computation time and algorithms for considering a large number of markers for mapping. The essential impediments to producing dense high-quality RH maps are data quality and panel size, not computation.

Algorithms↗

Bounds on the area under the receiver operating characteristic curve for the ideal observer.

A new upper bound is derived on the area under the receiver operating characteristic curve for the ideal observer in a signal-detection task. This upper bound is determined by the values of the likelihood-generating function and its second derivative at the origin. This bound is compared with other bounds on ideal-observer performance that have been derived recently, and it is also shown how this bound leads to some asymptotic results for approximations to ideal-observer performance.

Area Under Curve↗

The influence of acute rejection on long-term renal allograft survival: a comparison of living and cadaveric donor transplantation.

BACKGROUND: We investigated whether recipients of living donor grafts who suffer an acute rejection progress to graft loss because of chronic rejection at a slower rate than recipients of cadaveric grafts. METHODS: A retrospective review was made of 296 renal transplantations performed at Mount Sinai Hospital. Only grafts functioning for at least 3 months were included in this analysis. Demographic variables of donor and recipient age, race, sex, and serum creatinine at 3 months after transplantation were compared between groups. RESULTS: Among the acute rejection-free cohort, the estimated 5-year graft survival was 90% for those receiving transplants from living relatives and 88% for those receiving cadaveric transplants (P=0.76). However, in grafts with early acute rejection, the 5-year survival was 40% for cadaveric recipients compared with 73% for living related graft recipients (P<0.014). Using the proportional hazards model, cadaveric donor source, older donor age, African American recipient race, and elevated 3-month serum creatinine were independent predictors of long-term graft loss caused by chronic rejection. The severity of acute rejection and recipient age had no impact on the risk of graft loss because of chronic rejection. CONCLUSION: These data indicate that the benefit of living related transplantation results from the fact that a living related graft progresses from acute to chronic rejection at a slower rate than a cadaveric graft. Furthermore, a cadaveric graft that is free of acute rejection 3 months after transplantation has an equal likelihood of functioning at 5 years as that of a graft from a living related donor.

Acute Disease↗

GOFCOX: a computer program for the goodness-of-fit analysis of the Cox proportional hazards model.

GOFCOX is a user-friendly FORTRAN program for assessing the adequacy of the Cox proportional hazards model. The underlying methodology is based on the comparison of the maximum partial likelihood estimator and a weighted parameter estimator. The latter is the root to an estimation equation that assigns varying weights to the individual contributions to the partial likelihood score function. The weighted and unweighted parameter estimators have the same expectation under the Cox model, but tend to differ when the model is inappropriate. The GOFCOX program computes a rich class of weighted parameter estimators and corresponding goodness-of-fit test statistics. The program runs on both mainframe computers and microcomputers. The running time is minimal even for large data sets. A simple example is provided to illustrate the features of the program.

Computers, Mainframe↗

Extending the limits of molecular replacement through combined simulated annealing and maximum-likelihood refinement.

Phases determined by the molecular-replacement method often suffer from model bias. In extreme cases, the refinement of the atomic model can stall at high free R values when the resulting electron-density maps provide little indication of how to correct the model, sometimes rendering even a correct solution unusable. Here, it is shown that several recent advances in refinement methodology allow productive refinement, even in cases where the molecular-replacement-phased electron-density maps do not allow manual rebuilding. In test calculations performed with a series of homologous models of penicillopepsin using either backbone atoms, or backbone atoms plus conserved core residues, model bias is reduced and refinement can proceed efficiently, even if the initial model is far from the correct one. These new methods combine cross-validation, torsion-angle dynamics simulated annealing and maximum-likelihood target functions. It is also shown that the free R value is an excellent indicator of model quality after refinement, potentially discriminating between correct and incorrect molecular-replacement solutions. The use of phase information, even in the form of bimodal single-isomorphous-replacement phase distributions, greatly improves the radius of convergence of refinement and hence the quality of the electron-density maps, further extending the limits of molecular replacement.

Amino Acid Sequence↗

Implicit learning in 3D object recognition: the importance of temporal context.

A novel architecture and set of learning rules for cortical self-organization is proposed. The model is based on the idea that multiple information channels can modulate one another's plasticity. Features learned from bottom-up information sources can thus be influenced by those learned from contextual pathways, and vice versa. A maximum likelihood cost function allows this scheme to be implemented in a biologically feasible, hierarchical neural circuit. In simulations of the model, we first demonstrate the utility of temporal context in modulating plasticity. The model learns a representation that categorizes people's faces according to identity, independent of viewpoint, by taking advantage of the temporal continuity in image sequences. In a second set of simulations, we add plasticity to the contextual stream and explore variations in the architecture. In this case, the model learns a two-tiered representation, starting with a coarse view-based clustering and proceeding to a finer clustering of more specific stimulus features. This model provides a tenable account of how people may perform 3D object recognition in a hierarchical, bottom-up fashion.

Animals↗

Maximum likelihood methods reveal conservation of function among closely related kinesin families.

We have reconstructed the evolution of the anciently derived kinesin superfamily using various alignment and tree-building methods. In addition to classifying previously described kinesins from protists, fungi, and animals, we analyzed a variety of kinesin sequences from the plant kingdom including 12 from Zea mays and 29 from Arabidopsis thaliana. Also included in our data set were four sequences from the anciently diverged amitochondriate protist Giardia lamblia. The overall topology of the best tree we found is more likely than previously reported topologies and allows us to make the following new observations: (1) kinesins involved in chromosome movement including MCAK, chromokinesin, and CENP-E may be descended from a single ancestor; (2) kinesins that form complex oligomers are limited to a monophyletic group of families; (3) kinesins that crosslink antiparallel microtubules at the spindle midzone including BIMC, MKLP, and CENP-E are closely related; (4) Drosophila NOD and human KID group with other characterized chromokinesins; and (5) Saccharomyces SMY1 groups with kinesin-I sequences, forming a family of kinesins capable of class V myosin interactions. In addition, we found that one monophyletic clade composed exclusively of sequences with a C-terminal motor domain contains all known minus end-directed kinesins.

Animals↗

Activation detection in functional MRI using subspace modeling and maximum likelihood estimation.

A statistical method for detecting activated pixels in functional MRI (fMIRI) data is presented. In this method, the fMRI time series measured at each pixel is modeled as the sum of a response signal which arises due to the experimentally controlled activation-baseline pattern, a nuisance component representing effects of no interest, and Gaussian white noise. For periodic activation-baseline patterns, the response signal is modeled by a truncated Fourier series with a known fundamental frequency but unknown Fourier coefficients. The nuisance subspace is assumed to be unknown. A maximum likelihood estimate is derived for the component of the nuisance subspace which is orthogonal to the response signal subspace. An estimate for the order of the nuisance subspace is obtained from an information theoretic criterion. A statistical test is derived and shown to be the uniformly most powerful (UMP) test invariant to a group of transformations which are natural to the hypothesis testing problem. The maximal invariant statistic used in this test has an F distribution. The theoretical F distribution under the null hypothesis strongly concurred with the experimental frequency distribution obtained by performing null experiments in which the subjects did not perform any activation task. Application of the theory to motor activation and visual stimulation fMRI studies is presented.

Adult↗

Parametric models to assess tracking of categorical high-risk sexual behaviour for HIV infection.

Prevalence and tracking (within-individual correlation) of high-risk behaviour measured in longitudinal studies provide a complementary description of how behaviour is maintained over time. Without tracking, when population prevalences are approximately constant over time, the steadiness of the aggregate pattern is likely to mask the dynamic changes of the response occurring on an individual level. In this paper, a parametric model is proposed for the estimation of these features when the observed response is a time-stationary categorical measurement. The model extends the standard Dirichlet-multinomial model in the spirit of Prentice (1986, JASA) by allowing both negative and positive correlation among repeated categorical measurements. In addition, both the marginal category probabilities and the tracking can be modelled as a function of individual-level covariates. Details regarding likelihood based estimation and inference are provided and illustrated with data from the Multicenter AIDS Cohort Study (MACS).

Cohort Studies↗

A pseudolikelihood approach for simultaneous analysis of array comparative genomic hybridizations.

DNA sequence copy number has been shown to be associated with cancer development and progression. Array-based comparative genomic hybridization (aCGH) is a recent development that seeks to identify the copy number ratio at large numbers of markers across the genome. Due to experimental and biological variations across chromosomes and hybridizations, current methods are limited to analyses of single chromosomes. We propose a more powerful approach that borrows strength across chromosomes and hybridizations. We assume a Gaussian mixture model, with a hidden Markov dependence structure and with random effects to allow for intertumoral variation, as well as intratumoral clonal variation. For ease of computation, we base estimation on a pseudolikelihood function. The method produces quantitative assessments of the likelihood of genetic alterations at each clone, along with a graphical display for simple visual interpretation. We assess the characteristics of the method through simulation studies and analysis of a brain tumor aCGH data set. We show that the pseudolikelihood approach is superior to existing methods both in detecting small regions of copy number alteration and in accurately classifying regions of change when intratumoral clonal variation is present. Software for this approach is available at http://www.biostat.harvard.edu/ approximately betensky/papers.html.

Algorithms↗

Statistical modeling and reconstruction of randoms precorrected PET data.

Randoms precorrected positron emission tomography (PET) data is formed as the difference of two Poisson random variables. Its exact probability mass function (PMF) is inconvenient for use in likelihood-based iterative image reconstruction as it contains an infinite summation. The shifted Poisson model is a tractable approximation to this PMF but requires that negative values are truncated, resulting in positively biased reconstructions in low count studies. Here we analyze the properties of the exact PMF and propose a simple but accurate approximation that allows negative valued data. We investigate the properties of this approximation and demonstrate its application to penalized maximum likelihood image reconstruction.

Algorithms↗

General quadratic functions in real and reciprocal space and their application to likelihood phasing.

A general multivariate quadratic function of the structure factors is constructed and transformed to obtain a quadratic function of the continuous electron density. Two special cases, where structure factors are independent and where electron-density values are independent, are examined. These results are related to the new likelihood-based framework of Terwilliger [Terwilliger (1999), Acta Cryst. D55, pp. 1863-1871] for employing structural information which was previously exploited by means of conventional density-modification calculations. The treatment here involves different assumptions and highlights new features of Terwilliger's calculation. The generalization quadratic construction allows the generation of cross terms relating all reflections and electron densities. Other applications of this approach are considered.

Computational Biology↗