A multiple-instance scoring method to predict tissue-specific cis-regulatory motifs and regions

Transcription is the central process of gene regulation. In higher eukaryotes, the transcription of a gene is usually regulated by multiple cis-regulatory regions (CRRs). In different tissues, different transcription factors bind to their cis-regulatory motifs in these CRRs to drive tissue-specific expression patterns of their target genes. By combining the genome-wide gene expression data with the genomic sequence data, we proposed multiple-instance scoring (MIS) method to predict the tissue-specific motifs and the corresponding CRRs. The method is mainly based on the assumption that only a subset of CRRs of the expressed gene should function in the studied tissue. By testing on the simulated datasets and the fly muscle dataset, MIS can identify true motifs when noise is high and shows higher specificity for predicting the tissue-specific functions of CRRs

A Robust Zero-point Attraction LMS Algorithm on Near Sparse System Identification

The newly proposed $l_1$ norm constraint zero-point attraction Least Mean Square algorithm (ZA-LMS) demonstrates excellent performance on exact sparse system identification. However, ZA-LMS has less advantage against standard LMS when the system is near sparse. Thus, in this paper, firstly the near sparse system modeling by Generalized Gaussian Distribution is recommended, where the sparsity is defined accordingly. Secondly, two modifications to the ZA-LMS algorithm have been made. The $l_1$ norm penalty is replaced by a partial $l_1$ norm in the cost function, enhancing robustness without increasing the computational complexity. Moreover, the zero-point attraction item is weighted by the magnitude of estimation error which adjusts the zero-point attraction force dynamically. By combining the two improvements, Dynamic Windowing ZA-LMS (DWZA-LMS) algorithm is further proposed, which shows better performance on near sparse system identification. In addition, the mean square performance of DWZA-LMS algorithm is analyzed. Finally, computer simulations demonstrate the effectiveness of the proposed algorithm and verify the result of theoretical analysis.Comment: 20 pages, 11 figure

Performance Analysis of l_0 Norm Constraint Least Mean Square Algorithm

As one of the recently proposed algorithms for sparse system identification, $l_0$ norm constraint Least Mean Square ($l_0$-LMS) algorithm modifies the cost function of the traditional method with a penalty of tap-weight sparsity. The performance of $l_0$-LMS is quite attractive compared with its various precursors. However, there has been no detailed study of its performance. This paper presents all-around and throughout theoretical performance analysis of $l_0$-LMS for white Gaussian input data based on some reasonable assumptions. Expressions for steady-state mean square deviation (MSD) are derived and discussed with respect to algorithm parameters and system sparsity. The parameter selection rule is established for achieving the best performance. Approximated with Taylor series, the instantaneous behavior is also derived. In addition, the relationship between $l_0$-LMS and some previous arts and the sufficient conditions for $l_0$-LMS to accelerate convergence are set up. Finally, all of the theoretical results are compared with simulations and are shown to agree well in a large range of parameter setting.Comment: 31 pages, 8 figure

Statistical partition problem for exponential populations and statistical surveillance of cancers in Louisiana

In this dissertation, we consider the problem of partitioning a set of k population with respect to a control population. For this problem some multistage methodologies are proposed and their properties are derived. Using the Monte Carlo simulation techniques, the small and moderate sample size performance of the proposed procedure are studied. We have also considered at statistical surveillance of various cancers in Louisiana

Di-μ-benzoato-κ3 O,O′:O′;κ3 O:O,O′-bis­[(benzoato-κ2 O,O′)(1,10-phenanthroline-κ2 N,N′)cadmium]

The dinuclear title compound, [Cd2(C7H5O2)4(C12H8N2)2], lies on a crystallographic twofold axis. The CdII ions are connected by two bridging benzoate anions and each ion is seven-coordinated by five O atoms from three benzoate ligands and by two N atoms from 1,10-phenanthroline. The benzoate ligands adopt two different coordination modes, acting as bidentate and bridging tridentate ligands. The discrete neutral mol­ecules further extend their structure into a three-dimensional supra­molecular framework by inter­molecular π–π [inter­planar distances of 3.392 (4) Å] and C—H⋯π stacking inter­actions [H–mean plane = 2.567 (4) and 2.781 (4) Å]