Farideh Fazayeli

HiTEC: accurate error correction in high-throughput sequencing data

MOTIVATION High-throughput sequencing technologies produce very large amounts of data and sequencing errors constitute one of the major problems in analyzing such data. Current algorithms for correcting these errors are not very accurate and do not automatically adapt to the given data. RESULTS We present HiTEC, an algorithm that provides a highly accurate, robust and fully automated method to correct reads produced by high-throughput sequencing methods. Our approach provides significantly higher accuracy than previous methods. It is time and space efficient and works very well for all read lengths, genome sizes and coverage levels. AVAILABILITY The source code of HiTEC is freely available at www.csd.uwo.ca/~ilie/HiTEC/.

Feature Selection Based on the Rough Set Theory and Expectation-Maximization Clustering Algorithm

We study the Rough Set theory as a method of feature selection based on tolerant classes that extends the existing equivalent classes. The determination of initial tolerant classes is a challenging and important task for accurate feature selection and classification. In this paper the Expectation-Maximization clustering algorithm is applied to determine similar objects. This method generates fewer features with either a higher or the same accuracy compared with two existing methods, i.e., Fuzzy Rough Feature Selection and Tolerance-based Feature Selection, on a number of benchmarks from the UCI repository.

Back-propagation with chaos

Multilayer feed-forward neural networks are widely used based on minimization of an error function. Back-propagation is a famous training method used in the multilayer networks but it often suffers from a local minima problem. To avoid this problem, we propose a new back-propagation training based on chaos. We investigate whether randomicity and ergodicity property of chaos can enable the learning algorithm to escape from local minima. Validity of the proposed method is examined by performing simulations on three real classification tasks, namely, the Ionosphere, the Wincson Breast Cancer (WBC), and the credit-screening datasets. The algorithm is shown to work better than the original back-propagation and is comparable with the Levenberg-Marquardt algorithm, but simpler and easier to implement comparing to Levenberg-Marquardt algorithm.

Mapping local and global variability in plant trait distributions

Significance Currently, Earth system models (ESMs) represent variation in plant life through the presence of a small set of plant functional types (PFTs), each of which accounts for hundreds or thousands of species across thousands of vegetated grid cells on land. By expanding plant traits from a single mean value per PFT to a full distribution per PFT that varies among grid cells, the trait variation present in nature is restored and may be propagated to estimates of ecosystem processes. Indeed, critical ecosystem processes tend to depend on the full trait distribution, which therefore needs to be represented accurately. These maps reintroduce substantial local variation and will allow for a more accurate representation of the land surface in ESMs. Our ability to understand and predict the response of ecosystems to a changing environment depends on quantifying vegetation functional diversity. However, representing this diversity at the global scale is challenging. Typically, in Earth system models, characterization of plant diversity has been limited to grouping related species into plant functional types (PFTs), with all trait variation in a PFT collapsed into a single mean value that is applied globally. Using the largest global plant trait database and state of the art Bayesian modeling, we created fine-grained global maps of plant trait distributions that can be applied to Earth system models. Focusing on a set of plant traits closely coupled to photosynthesis and foliar respiration—specific leaf area (SLA) and dry mass-based concentrations of leaf nitrogen (Nm) and phosphorus (Pm), we characterize how traits vary within and among over 50,000 ∼50×50-km cells across the entire vegetated land surface. We do this in several ways—without defining the PFT of each grid cell and using 4 or 14 PFTs; each model’s predictions are evaluated against out-of-sample data. This endeavor advances prior trait mapping by generating global maps that preserve variability across scales by using modern Bayesian spatial statistical modeling in combination with a database over three times larger than that in previous analyses. Our maps reveal that the most diverse grid cells possess trait variability close to the range of global PFT means.

The Matrix Generalized Inverse Gaussian Distribution: Properties and Applications

While the Matrix Generalized Inverse Gaussian (

Uncertainty Quantified Matrix Completion Using Bayesian Hierarchical Matrix Factorization

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Assisting Cancer Diagnosis with Fuzzy Neural Networks

) distribution arises naturally in some settings as a distribution over symmetric positive semi-definite matrices, certain key properties of the distribution and effective ways of sampling from the distribution have not been carefully studied. In this paper, we show that the

Estimation with Norm Regularization

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BHPMF – a hierarchical Bayesian approach to gap-filling and trait prediction for macroecology and functional biogeography

is unimodal, and the mode can be obtained by solving an Algebraic Riccati Equation (ARE) equation [7]. Based on the property, we propose an importance sampling method for the

Generalized direct change estimation in ising model structure

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