Evaluation of association tests for rare variants using simulated data sets in the Genetic Analysis Workshop 17 data

Convolutional neural networks have powerful performances in many visual tasks because of their hierarchical structures and powerful feature extraction capabilities. SPD (symmetric positive definition) matrix is paid attention to in visual classification, because it has excellent ability to learn proper statistical representation and distinguish samples with different information. In this paper, a deep neural network signal detection method based on spectral convolution features is proposed. In this method, local features extracted from convolutional neural network are used to construct the SPD matrix, and a deep learning algorithm for the SPD matrix is used to detect target signals. Feature maps extracted by two kinds of convolutional neural network models are applied in this study. Based on this method, signal detection has become a binary classification problem of signals in samples. In order to prove the availability and superiority of this method, simulated and semi-physical simulated data sets are used. The results show that, under low SCR (signal-to-clutter ratio), compared with the spectral signal detection method based on the deep neural network, this method can obtain a gain of 0.5–2 dB on simulated data sets and semi-physical simulated data sets.

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Comparison of multiple single-nucleotide variant association tests in a meta-analysis of Genetic Analysis Workshop 19 family and unrelated data

BMC Proceedings ◽

10.1186/s12919-016-0028-7 ◽

2016 ◽

Vol 10 (S7) ◽

Author(s):

Shuai Wang ◽

Virginia A. Fisher ◽

Yuning Chen ◽

Josée Dupuis

Keyword(s):

Genetic Analysis ◽

Genetic Analysis Workshop ◽

Meta Analysis ◽

Single Nucleotide Variant ◽

Single Nucleotide ◽

Association Tests

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Benchmarking Statistical Multiple Sequence Alignment

10.1101/304659 ◽

2018 ◽

Cited By ~ 1

Author(s):

Michael Nute ◽

Ehsan Saleh ◽

Tandy Warnow

Keyword(s):

Sequence Alignment ◽

Multiple Sequence Alignment ◽

Structural Alignment ◽

Estimation Method ◽

Simulated Data ◽

Protein Sequences ◽

Data Sets ◽

Sequence Alignments ◽

Multiple Sequence ◽

Simulated Data Sets

AbstractThe estimation of multiple sequence alignments of protein sequences is a basic step in many bioinformatics pipelines, including protein structure prediction, protein family identification, and phylogeny estimation. Statistical co-estimation of alignments and trees under stochastic models of sequence evolution has long been considered the most rigorous technique for estimating alignments and trees, but little is known about the accuracy of such methods on biological benchmarks. We report the results of an extensive study evaluating the most popular protein alignment methods as well as the statistical co-estimation method BAli-Phy on 1192 protein data sets from established benchmarks as well as on 120 simulated data sets. Our study (which used more than 230 CPU years for the BAli-Phy analyses alone) shows that BAli-Phy is dramatically more accurate than the other alignment methods on the simulated data sets, but is among the least accurate on the biological benchmarks. There are several potential causes for this discordance, including model misspecification, errors in the reference alignments, and conflicts between structural alignment and evolutionary alignments; future research is needed to understand the most likely explanation for our observations. multiple sequence alignment, BAli-Phy, protein sequences, structural alignment, homology

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Bayesian Planet Searches for the 10 cm/s Radial Velocity Era

Proceedings of the International Astronomical Union ◽

10.1017/s1743921316002817 ◽

2015 ◽

Vol 11 (A29A) ◽

pp. 205-207

Author(s):

Philip C. Gregory

Keyword(s):

Radial Velocity ◽

State Of The Art ◽

Simulated Data ◽

Model Parameters ◽

Data Sets ◽

Stellar Activity ◽

Bayesian Fusion ◽

Multiple State ◽

Simulated Data Sets ◽

Apodization Function

AbstractA new apodized Keplerian model is proposed for the analysis of precision radial velocity (RV) data to model both planetary and stellar activity (SA) induced RV signals. A symmetrical Gaussian apodization function with unknown width and center can distinguish planetary signals from SA signals on the basis of the width of the apodization function. The general model for m apodized Keplerian signals also includes a linear regression term between RV and the stellar activity diagnostic In (R'hk), as well as an extra Gaussian noise term with unknown standard deviation. The model parameters are explored using a Bayesian fusion MCMC code. A differential version of the Generalized Lomb-Scargle periodogram provides an additional way of distinguishing SA signals and helps guide the choice of new periods. Sample results are reported for a recent international RV blind challenge which included multiple state of the art simulated data sets supported by a variety of stellar activity diagnostics.

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