Efficient training algorithms for HMMs using incremental estimation

Typically, parameter estimation for a hidden Markov model (HMM) is performed using an expectation-maximization (EM) algorithm with the maximum-likelihood (ML) criterion. The EM algorithm is an iterative scheme that is well-defined and numerically stable, but convergence may require a large number of iterations. For speech recognition systems utilizing large amounts of training material, this results in long training times. This paper presents an incremental estimation approach to speed-up the training of HMMs without any loss of recognition performance. The algorithm selects a subset of data from the training set, updates the model parameters based on the subset, and then iterates the process until convergence of the parameters. The advantage of this approach is a substantial increase in the number of iterations of the EM algorithm per training token, which leads to faster training. In order to achieve reliable estimation from a small fraction of the complete data set at each iteration, two training criteria are studied; ML and maximum a posteriori (MAP) estimation. Experimental results show that the training of the incremental algorithms is substantially faster than the conventional (batch) method and suffers no loss of recognition performance. Furthermore, the incremental MAP based training algorithm improves performance over the batch versio

The Genetics of Generalized Osteoarthritis (GOGO) study: study design and evaluation of osteoarthritis phenotypes

PURPOSE: The primary goal of the Genetics of Generalized Osteoarthritis (GOGO) study is to identify chromosomal regions associated with increased susceptibility to generalized osteoarthritis (OA). Here we describe the study design and phenotype of the 2728 participants from the 1145 families recruited for this study. METHODS: GOGO is an investigator-initiated collaboration involving seven clinical academic sites and sponsored by GlaxoSmithKline. Family ascertainment was carried out between 1999 and 2002. A qualifying family required self-reported Caucasian ethnicity and at least two affected siblings with clinical hand OA. We hypothesized that this clinical phenotype would facilitate identification of participants with multijoint radiographic OA (rOA) in and beyond the hand. The "gold standard" case definition, however, was based on rOA (Kellgren-Lawrence grade > or =2) involving > or =3 hand joints distributed bilaterally and including at least one distal interphalangeal joint, with two of the three involved joints within a joint group (distal interphalangeal, proximal interphalangeal, or carpometacarpal). Radiographs of hips, knees and spine were also obtained. Additional siblings and living parents from qualifying families, both affected and unaffected, were invited to participate. RESULTS: A total of 2706 participants had complete clinical and radiological examination data. Of these, 2569 participants met clinical examination criteria for affected status; while 1963 (73%) participants met the prespecified radiographic criteria for affected status. This corresponded to a total of 707 families with at least two affected siblings that met the hand rOA criteria. Of those individuals with rOA of the hand, the frequency of rOA at other sites was highest for the knee (51%) and spine (54%), and less common for the hip (25%). Concordance rates among hand affected siblings were greatest for spine (36%) followed by knee (31%) and hip (9%); a total of 53% of the affected sib pairs were concordant for specific patterns of generalized rOA involving the hand and large joints (knees, hips or spine). CONCLUSIONS: GOGO represents a large multicenter collection of families with multiple joint OA that have been characterized both clinically and radiographically. The GOGO study will employ a comprehensive strategy for genetic screening based upon both qualitative and quantitative radiographic trait analyses, circulating biomarkers in a quantitative trait-based analysis, fine mapping, and candidate gene analysis. This sample should provide sufficient power to detect linkage to OA associated genes

The lowest X4 Geno2Pheno false-positive rate is associated with greater CD4 depletion in HIV-1 infected patients

Through this study we evaluated whether the HIV-1 tropism determined by genotypic analysis correlates with HIV-1 markers, such as CD4 cell count and plasma HIV-RNA. The analysis was performed on 1221 HIV-1 B-subtype infected patients with an available V3 sequence (all maraviroc naive). Of them, 532 were antiretroviral therapy (ART) naive and 689 ART experienced. Tropism determination was performed by using the geno2pheno (co-receptor) algorithm set at a false-positive rate (FPR) of 10% and 2%. Potential associations of FPR with CD4 cell count and viraemia were evaluated. Association of V3 mutations with genotypic-determined tropism was also evaluated according to different FPR ranges. About 26% of patients (either ART naive or ART experienced) were infected by X4-tropic viruses (using the classical 10% FPR cut-off). However, a significantly lower proportion of ART-naive patients had FPR ≤ 2% in comparison with ART-experienced patients (4.9% vs. 12.6%, respectively, p <0.001). The risk of advanced HIV-1 infection (with CD4 cell count ≤ 200 cells/mm(3) ) was significantly greater in X4-infected patients, either ART-naive (OR (95% CI)), 4.2 (1.8-9.2); p 0.0006) or ART-experienced (2.3 (1.4-3.6); p 0.0003), with FPR set at 2% (but not at 10%). This finding was confirmed by multivariable logistic analysis. No relationship was found between viraemia and FPR ≤2%. Some X4-related mutations were significantly associated with FPR ≤2% (ART-naive patients, S11R, Y21V, G24K and G24R, p ≤0.001; ART-experienced patients, Y7K, S11R, H13Y, p ≤0.002). In conclusion, these findings show that within the context of genotypically-assessed CXCR4 tropism, FPR ≤2% defines (far better than 10%-FPR) a viral population associated with low CD4 rank, with potentially greater cytopathic effect, and with more advanced disease