The Experts below are selected from a list of 288 Experts worldwide ranked by ideXlab platform
Kiyoshi Ezawa - One of the best experts on this subject based on the ideXlab platform.
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characterization of multiple sequence alignment errors using complete Likelihood Score and position shift map
BMC Bioinformatics, 2016Co-Authors: Kiyoshi EzawaAbstract:Background Reconstruction of multiple sequence alignments (MSAs) is a crucial step in most homology-based sequence analyses, which constitute an integral part of computational biology. To improve the accuracy of this crucial step, it is essential to better characterize errors that state-of-the-art aligners typically make. For this purpose, we here introduce two tools: the complete-Likelihood Score and the position-shift map.
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characterization of multiple sequence alignment errors using complete Likelihood Score and position shift map
BMC Bioinformatics, 2016Co-Authors: Kiyoshi EzawaAbstract:Reconstruction of multiple sequence alignments (MSAs) is a crucial step in most homology-based sequence analyses, which constitute an integral part of computational biology. To improve the accuracy of this crucial step, it is essential to better characterize errors that state-of-the-art aligners typically make. For this purpose, we here introduce two tools: the complete-Likelihood Score and the position-shift map. The logarithm of the total probability of a MSA under a stochastic model of sequence evolution along a time axis via substitutions, insertions and deletions (called the “complete-Likelihood Score” here) can serve as an ideal Score of the MSA. A position-shift map, which maps the difference in each residue’s position between two MSAs onto one of them, can clearly visualize where and how MSA errors occurred and help disentangle composite errors. To characterize MSA errors using these tools, we constructed three sets of simulated MSAs of selectively neutral mammalian DNA sequences, with small, moderate and large divergences, under a stochastic evolutionary model with an empirically common power-law insertion/deletion length distribution. Then, we reconstructed MSAs using MAFFT and Prank as representative state-of-the-art single-optimum-search aligners. About 40–99% of the hundreds of thousands of gapped segments were involved in alignment errors. In a substantial fraction, from about 1/4 to over 3/4, of erroneously reconstructed segments, reconstructed MSAs by each aligner showed complete-Likelihood Scores not lower than those of the true MSAs. Out of the remaining errors, a majority by an iterative option of MAFFT showed discrepancies between the aligner-specific Score and the complete-Likelihood Score, and a majority by Prank seemed due to inadequate exploration of the MSA space. Analyses by position-shift maps indicated that true MSAs are in considerable neighborhoods of reconstructed MSAs in about 80–99% of the erroneous segments for small and moderate divergences, but in only a minority for large divergences. The results of this study suggest that measures to further improve the accuracy of reconstructed MSAs would substantially differ depending on the types of aligners. They also re-emphasize the importance of obtaining a probability distribution of fairly likely MSAs, instead of just searching for a single optimum MSA.
Mary Sara Mcpeek - One of the best experts on this subject based on the ideXlab platform.
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case control association testing with related individuals a more powerful quasi Likelihood Score test
American Journal of Human Genetics, 2007Co-Authors: Timothy A Thornton, Mary Sara McpeekAbstract:We consider the problem of genomewide association testing of a binary trait when some sampled individuals are related, with known relationships. This commonly arises when families sampled for a linkage study are included in an association study. Furthermore, power to detect association with complex traits can be increased when affected individuals with affected relatives are sampled, because they are more likely to carry disease alleles than are randomly sampled affected individuals. With related individuals, correlations among relatives must be taken into account, to ensure validity of the test, and consideration of these correlations can also improve power. We provide new insight into the use of pedigree-based weights to improve power, and we propose a novel test, the M QLS test, which, as we demonstrate, represents an overall, and in many cases, substantial, improvement in power over previous tests, while retaining a computational simplicity that makes it useful in genomewide association studies in arbitrary pedigrees. Other features of the M QLS are as follows: (1) it is applicable to completely general combinations of family and case-control designs, (2) it can incorporate both unaffected controls and controls of unknown phenotype into the same analysis, and (3) it can incorporate phenotype data about relatives with missing genotype data. The methods are applied to data from the Genetic Analysis Workshop 14 Collaborative Study of the Genetics of Alcoholism, where the M QLS detects genomewide significant association (after Bonferroni correction) with an alcoholism-related phenotype for four different single-nucleotide polymorphisms: tsc1177811 ( P =5.9×10 −7 ), tsc1750530 ( P =4.0×10 −7 ), tsc0046696 ( P =4.7×10 −7 ), and tsc0057290 ( P =5.2×10 −7 ) on chromosomes 1, 16, 18, and 18, respectively. Three of these four significant associations were not detected in previous studies analyzing these data.
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novel case control test in a founder population identifies p selectin as an atopy susceptibility locus
American Journal of Human Genetics, 2003Co-Authors: Catherine Bourgain, Sabine Hoffjan, Raluca Nicolae, Dina L Newman, Lori Steiner, Karen Walker, Rebecca L Reynolds, Carole Ober, Mary Sara McpeekAbstract:To avoid problems related to unknown population substructure, association studies may be conducted in founder populations. In such populations, however, the relatedness among individuals may be considerable. Neglecting such correlations among individuals can lead to seriously spurious associations. Here, we propose a method for case-control association studies of binary traits that is suitable for any set of related individuals, provided that their genealogy is known. Although we focus here on large inbred pedigrees, this method may also be used in outbred populations for case-control studies in which some individuals are relatives. We base inference on a quasi-Likelihood Score (QLS) function and construct a QLS test for allelic association. This approach can be used even when the pedigree structure is far too complex to use an exact-Likelihood calculation. We also present an alternative approach to this test, in which we use the known genealogy to derive a correction factor for the case-control association chi2 test. We perform analytical power calculations for each of the two tests by deriving their respective noncentrality parameters. The QLS test is more powerful than the corrected chi2 test in every situation considered. Indeed, under certain regularity conditions, the QLS test is asymptotically the locally most powerful test in a general class of linear tests that includes the corrected chi2 test. The two methods are used to test for associations between three asthma-associated phenotypes and 48 SNPs in 35 candidate genes in the Hutterites. We report a highly significant novel association (P=2.10-6) between atopy and an amino acid polymorphism in the P-selectin gene, detected with the QLS test and also, but less significantly (P=.0014), with the transmission/disequilibrium test.
Shaogang Gong - One of the best experts on this subject based on the ideXlab platform.
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modelling activity global temporal dependencies using time delayed probabilistic graphical model
International Conference on Computer Vision, 2009Co-Authors: Chen Change Loy, Tao Xiang, Shaogang GongAbstract:We present a novel approach for detecting global behaviour anomalies in multiple disjoint cameras by learning time delayed dependencies between activities cross camera views. Specifically, we propose to model multi-camera activities using a Time Delayed Probabilistic Graphical Model (TD-PGM) with different nodes representing activities in different semantically decomposed regions from different camera views, and the directed links between nodes encoding causal relationships between the activities. A novel two-stage structure learning algorithm is formulated to learn globally optimised time-delayed dependencies. A new cumulative abnormality Score is also introduced to replace the conventional log-Likelihood Score for gaining significantly more robust and reliable real-time anomaly detection. The effectiveness of the proposed approach is validated using a camera network installed at a busy underground station.
Tsuneo Nitta - One of the best experts on this subject based on the ideXlab platform.
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confidence scoring for accurate hmm based word recognition by using sm based monophone Score normalization
International Conference on Acoustics Speech and Signal Processing, 2002Co-Authors: Takaharu Sato, Muhammad Ghulam, Takashi Fukuda, Tsuneo NittaAbstract:In this paper, we propose a novel confidence scoring method that is applied to N-best hypotheses output from an HMM-based classifier. In the first pass of the proposed method, the HMM-based classifier with monophone models outputs N-best hypotheses and boundaries of all the monophones in the hypotheses. In the second pass, an SM(sub-space method)-based verifier tests the hypotheses by comparing confidence Scores. We discuss how to convert a monophone similarity Score of SM into a Likelihood Score, how to normalize the variations of acoustic quality in an utterance, and how to combine an HMM-based Likelihood of word level and an SM-based Likelihood of monophone level. In the experiments performed on speaker-independent word recognition, the proposed confidence scoring method significantly improves correct word recognition rate from 95.3% obtained by the standard HMM classifier to 98.0%.
Ritse M Mann - One of the best experts on this subject based on the ideXlab platform.
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detection of breast cancer with mammography effect of an artificial intelligence support system
Radiology, 2019Co-Authors: Alejandro Rodriguezruiz, Elizabeth A Krupinski, Jan Jurre Mordang, Kathy Schilling, Sylvia H Heywangkobrunner, Ioannis Sechopoulos, Ritse M MannAbstract:Purpose To compare breast cancer detection performance of radiologists reading mammographic examinations unaided versus supported by an artificial intelligence (AI) system. Materials and Methods An enriched retrospective, fully crossed, multireader, multicase, HIPAA-compliant study was performed. Screening digital mammographic examinations from 240 women (median age, 62 years; range, 39-89 years) performed between 2013 and 2017 were included. The 240 examinations (100 showing cancers, 40 leading to false-positive recalls, 100 normal) were interpreted by 14 Mammography Quality Standards Act-qualified radiologists, once with and once without AI support. The readers provided a Breast Imaging Reporting and Data System Score and probability of malignancy. AI support provided radiologists with interactive decision support (clicking on a breast region yields a local cancer Likelihood Score), traditional lesion markers for computer-detected abnormalities, and an examination-based cancer Likelihood Score. The area under the receiver operating characteristic curve (AUC), specificity and sensitivity, and reading time were compared between conditions by using mixed-models analysis dof variance and generalized linear models for multiple repeated measurements. Results On average, the AUC was higher with AI support than with unaided reading (0.89 vs 0.87, respectively; P = .002). Sensitivity increased with AI support (86% [86 of 100] vs 83% [83 of 100]; P = .046), whereas specificity trended toward improvement (79% [111 of 140]) vs 77% [108 of 140]; P = .06). Reading time per case was similar (unaided, 146 seconds; supported by AI, 149 seconds; P = .15). The AUC with the AI system alone was similar to the average AUC of the radiologists (0.89 vs 0.87). Conclusion Radiologists improved their cancer detection at mammography when using an artificial intelligence system for support, without requiring additional reading time. Published under a CC BY 4.0 license. See also the editorial by Bahl in this issue.