127 research outputs found
Analyzing imputed financial data: a new approach to cluster analysis
No full textThe authors introduce a novel statistical modeling technique to cluster analysis and apply it to financial data. Their two main goals are to handle missing data and to find homogeneous groups within the data. Their approach is flexible and handles large and complex data structures with missing observations and with quantitative and qualitative measurements. The authors achieve this result by mapping the data to a new structure that is free of distributional assumptions in choosing homogeneous groups of observations. Their new method also provides insight into the number of different categories needed for classifying the data. The authors use this approach to partition a matched sample of stocks. One group offers dividend reinvestment plans, and the other does not. Their method partitions this sample with almost 97 percent accuracy even when using only easily available financial variables. One interpretation of their result is that the misclassified companies are the best candidates either to adopt a dividend reinvestment plan (if they have none) or to abandon one (if they currently offer one). The authors offer other suggestions for applications in the field of finance.
PLoS One
No full textEver since the case of the missing heritability was highlighted some years ago, scientists have been investigating various possible explanations for the issue. However, none of these explanations include non-chromosomal genetic information. Here we describe explicitly how chromosomal and non-chromosomal modifiers collectively influence the heritability of a trait, in this case, the growth rate of yeast. Our results show that the non-chromosomal contribution can be large, adding another dimension to the estimation of heritability. We also discovered, combining the strength of LASSO with model selection, that the interaction of chromosomal and non-chromosomal information is essential in describing phenotypes
Statistical Mixture-Based Methods and Computational Tools for High-Throughput Data Derived in Proteomics and Metabolomics Study
No full textComputational prediction and interpretation of both general and specific types of promoters in Escherichia coli by exploiting a stacked ensemble-learning framework
No full textPromoters are short consensus sequences of DNA, which are responsible for transcription activation or the repression of all genes. There are many types of promoters in bacteria with important roles in initiating gene transcription. Therefore, solving promoter-identification problems has important implications for improving the understanding of their functions. To this end, computational methods targeting promoter classification have been established; however, their performance remains unsatisfactory. In this study, we present a novel stacked-ensemble approach (termed SELECTOR) for identifying both promoters and their respective classification. SELECTOR combined the composition of k-spaced nucleic acid pairs, parallel correlation pseudo-dinucleotide composition, position-specific trinucleotide propensity based on single-strand, and DNA strand features and using five popular tree-based ensemble learning algorithms to build a stacked model. Both 5-fold cross-validation tests using benchmark datasets and independent tests using the newly collected independent test dataset showed that SELECTOR outperformed state-of-the-art methods in both general and specific types of promoter prediction in Escherichia coli. Furthermore, this novel framework provides essential interpretations that aid understanding of model success by leveraging the powerful Shapley Additive exPlanation algorithm, thereby highlighting the most important features relevant for predicting both general and specific types of promoters and overcoming the limitations of existing 'Black-box' approaches that are unable to reveal causal relationships from large amounts of initially encoded features.Fuyi Li, Jinxiang Chen, Zongyuan Ge, Ya Wen, Yanwei Yue, Morihiro Hayashida, Abdelkader Baggag, Halima Bensmail and Jiangning Son
PROSPECT: A web server for predicting protein histidine phosphorylation sites
No full textBackground: Phosphorylation of histidine residues plays crucial roles in signaling pathways and cell metabolism in prokaryotes such as bacteria. While evidence has emerged that protein histidine phosphorylation also occurs in more complex organisms, its role in mammalian cells has remained largely uncharted. Thus, it is highly desirable to develop computational tools that are able to identify histidine phosphorylation sites. Result: Here, we introduce PROSPECT that enables fast and accurate prediction of proteome-wide histidine phosphorylation substrates and sites. Our tool is based on a hybrid method that integrates the outputs of two convolutional neural network (CNN)-based classifiers and a random forest-based classifier. Three features, including the one-of-K coding, enhanced grouped amino acids content (EGAAC) and composition of k-spaced amino acid group pairs (CKSAAGP) encoding, were taken as the input to three classifiers, respectively. Our results show that it is able to accurately predict histidine phosphorylation sites from sequence information. Our PROSPECT web server is user-friendly and publicly available at http://PROSPECT.erc.monash.edu/. Conclusions: PROSPECT is superior than other pHis predictors in both the running speed and prediction accuracy and we anticipate that the PROSPECT webserver will become a popular tool for identifying the pHis sites in bacteria.Zhen Chen, Pei Zhao, Fuyi Li, André Leier, Tatiana T. Marquez-Lago, Geoffrey I. Webb, Abdelkader Baggag, Halima Bensmail, and Jiangning Son
COFADMM: A Computational Features Selection with Alternating Direction Method of Multipliers
No full textAbstractDue to the explosion in size and complexity of Big Data, it is increasingly important to be able to solve problems with very large number of features. Classical feature selection procedures involves combinatorial optimization, with computational time increasing exponentially with the number of features. During the last decade, penalized regression has emerged as an attractive alternative for regularization and high dimensional feature selection problems. Alternating Direction Method of Multipliers (ADMM) optimization is suited for distributed convex optimization and distributed computing for big data. The purpose of this paper is to propose a broader algorithm COFADMM which combines the strength of convex penalized techniques in feature selection for big data and the power of the ADMM for optimization. We show that combining the ADMM algorithm with COFADMM can provide a path of solutions efficiently and quickly. COFADMM is easy to use, is available in C, Matlab upon request from the corresponding author
MetFlexo: An Automated Simulation of Realistic H1-NMR Spectra
No full textAbstractThe development of the ‘omics’ technologies such as transcriptomics, proteomics and metabolomics has made it possible to realize some of the goals of systems biology, where biological systems are interrogated at different levels of biochemical activity (such as gene expression, protein activity and/or metabolite concentration). Metabolomics deals with the metabolome that represents the complete set of small-molecule metabolites. Even though metabolomics can be thought of as a relatively young method, it is nevertheless a rapidly growing one that has the potential to reveal the molecular mechanism of certain diseases. H1 nuclear magnetic resonance (NMR) spectroscopy is commonly used in the metabolic profiling of biofluids as it has the potential to detect all proton-containing metabolites. Metabolites in biofluids are in dynamic equilibrium with those in cells and tissues, so their metabolic profile reflects changes in the state of an organism due to disease or environmental effects.ResultsMetFlexo is as an easy-to-use C package that allows the simulation of datasets of 1H-NMR spectra in order to test data analysis techniques, hypotheses and experimental designs. The idea is based on transforming statistical parameters of metabolites (shifts, couplings, concentrations and magnetic field) to an NMR spectrum using chemical-physics theory. Our method helps in the deconvolution of NMR spectra and in a better determination of metabolite concentrations, as these concentrations are key in detecting diseases and abnormalities. Unlike others, this program generates NMR spectrum of biofluids with no limit on magnetic field or pH. Thus, our approach is able to produce complex NMR profiles with flexible conditions. It is also simple to implement in C, requires small storage, is easy to compute and uses an independent platform. It will be available in R and MATLAB soon. The algorithm is freely available upon request to the corresponding author
- …
