133,964 research outputs found
Fold Recognition via a Tree
Recently, we developed a pairwise structural alignment algorithm using realistic structural and environmental information (SAUCE). In this paper, we at first present an automatic fold hierarchical classification based on SAUCE alignments. This classification enables us to build a fold tree containing different levels of multiple structural profiles. Then a tree-based fold search algorithm is described. We applied this method to a group of structures with sequence identity less than 35% and did a series of leave one out tests. These tests are approximately comparable to fold recognition tests on superfamily level. Results show that fold recognition via a fold tree can be faster and better at detecting distant homologues than classic fold recognition methods.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/63155/1/cmb.2006.13.1565.pd
Pathogen Response Genes Mediate Caenorhabditis elegans Innate Immunity
Innate immunity is crucial in the response and defense against pathogens for invertebrates and vertebrates alike. The soil nematode Caenorhabditis elegans is a useful model to study the eukaryotic innate immune response to microbial pathogenesis. Prior research indicates that the protein receptor FSHR-1 plays an important role in the innate recognition of intestinal infection due to pathogen consumption. Determining what genes are controlled by FSHR-1 may uncover an unknown pathway that could increase not only the comprehension of the C. elegans immune system but also innate immunity generally. To characterize the function of FSHR-1, four candidate pathogen response genes that appear to be regulated by FSHR-1 were evaluated in worms infected with Pseudomonas aeruginosa. Although intestine specific RNA interference of these four genes did not show immunity phenotypes, quantitative PCR suggests that FSHR-1 regulates the basal and/or infection-induced expression of three of the four genes. To explore this FSHR-1-dependent transcriptional induction, fluorescent transgenic reporters were constructed for the three candidate FSHR-1 target genes. The spatial expression of one putative pathogen response gene was characterized in transgenic worms under both control and pathogenic conditions. RNA interference was performed to assess the FSHR-1 dependency of this expression pattern
Feature Selection via Binary Simultaneous Perturbation Stochastic Approximation
Feature selection (FS) has become an indispensable task in dealing with
today's highly complex pattern recognition problems with massive number of
features. In this study, we propose a new wrapper approach for FS based on
binary simultaneous perturbation stochastic approximation (BSPSA). This
pseudo-gradient descent stochastic algorithm starts with an initial feature
vector and moves toward the optimal feature vector via successive iterations.
In each iteration, the current feature vector's individual components are
perturbed simultaneously by random offsets from a qualified probability
distribution. We present computational experiments on datasets with numbers of
features ranging from a few dozens to thousands using three widely-used
classifiers as wrappers: nearest neighbor, decision tree, and linear support
vector machine. We compare our methodology against the full set of features as
well as a binary genetic algorithm and sequential FS methods using
cross-validated classification error rate and AUC as the performance criteria.
Our results indicate that features selected by BSPSA compare favorably to
alternative methods in general and BSPSA can yield superior feature sets for
datasets with tens of thousands of features by examining an extremely small
fraction of the solution space. We are not aware of any other wrapper FS
methods that are computationally feasible with good convergence properties for
such large datasets.Comment: This is the Istanbul Sehir University Technical Report
#SHR-ISE-2016.01. A short version of this report has been accepted for
publication at Pattern Recognition Letter
Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer
Semantic annotations are vital for training models for object recognition,
semantic segmentation or scene understanding. Unfortunately, pixelwise
annotation of images at very large scale is labor-intensive and only little
labeled data is available, particularly at instance level and for street
scenes. In this paper, we propose to tackle this problem by lifting the
semantic instance labeling task from 2D into 3D. Given reconstructions from
stereo or laser data, we annotate static 3D scene elements with rough bounding
primitives and develop a model which transfers this information into the image
domain. We leverage our method to obtain 2D labels for a novel suburban video
dataset which we have collected, resulting in 400k semantic and instance image
annotations. A comparison of our method to state-of-the-art label transfer
baselines reveals that 3D information enables more efficient annotation while
at the same time resulting in improved accuracy and time-coherent labels.Comment: 10 pages in Conference on Computer Vision and Pattern Recognition
(CVPR), 201
Solving Multiclass Learning Problems via Error-Correcting Output Codes
Multiclass learning problems involve finding a definition for an unknown
function f(x) whose range is a discrete set containing k > 2 values (i.e., k
``classes''). The definition is acquired by studying collections of training
examples of the form [x_i, f (x_i)]. Existing approaches to multiclass learning
problems include direct application of multiclass algorithms such as the
decision-tree algorithms C4.5 and CART, application of binary concept learning
algorithms to learn individual binary functions for each of the k classes, and
application of binary concept learning algorithms with distributed output
representations. This paper compares these three approaches to a new technique
in which error-correcting codes are employed as a distributed output
representation. We show that these output representations improve the
generalization performance of both C4.5 and backpropagation on a wide range of
multiclass learning tasks. We also demonstrate that this approach is robust
with respect to changes in the size of the training sample, the assignment of
distributed representations to particular classes, and the application of
overfitting avoidance techniques such as decision-tree pruning. Finally, we
show that---like the other methods---the error-correcting code technique can
provide reliable class probability estimates. Taken together, these results
demonstrate that error-correcting output codes provide a general-purpose method
for improving the performance of inductive learning programs on multiclass
problems.Comment: See http://www.jair.org/ for any accompanying file
A decision forest based feature selection framework for action recognition from RGB-Depth cameras
In this paper, we present an action recognition framework
leveraging data mining capabilities of random decision forests trained on
kinematic features. We describe human motion via a rich collection of
kinematic feature time-series computed from the skeletal representation
of the body in motion. We discriminatively optimize a random decision
forest model over this collection to identify the most effective subset
of features, localized both in time and space. Later, we train a support
vector machine classifier on the selected features. This approach improves
upon the baseline performance obtained using the whole feature set with
a significantly less number of features (one tenth of the original). On
MSRC-12 dataset (12 classes), our method achieves 94% accuracy. On
the WorkoutSU-10 dataset, collected by our group (10 physical exercise
classes), the accuracy is 98%. The approach can also be used to provide
insights on the spatiotemporal dynamics of human actions
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