111 research outputs found
Stable Feature Selection for Biomarker Discovery
Feature selection techniques have been used as the workhorse in biomarker
discovery applications for a long time. Surprisingly, the stability of feature
selection with respect to sampling variations has long been under-considered.
It is only until recently that this issue has received more and more attention.
In this article, we review existing stable feature selection methods for
biomarker discovery using a generic hierarchal framework. We have two
objectives: (1) providing an overview on this new yet fast growing topic for a
convenient reference; (2) categorizing existing methods under an expandable
framework for future research and development
A Combinatorial Perspective of the Protein Inference Problem
In a shotgun proteomics experiment, proteins are the most biologically
meaningful output. The success of proteomics studies depends on the ability to
accurately and efficiently identify proteins. Many methods have been proposed
to facilitate the identification of proteins from the results of peptide
identification. However, the relationship between protein identification and
peptide identification has not been thoroughly explained before.
In this paper, we are devoted to a combinatorial perspective of the protein
inference problem. We employ combinatorial mathematics to calculate the
conditional protein probabilities (Protein probability means the probability
that a protein is correctly identified) under three assumptions, which lead to
a lower bound, an upper bound and an empirical estimation of protein
probabilities, respectively. The combinatorial perspective enables us to obtain
a closed-form formulation for protein inference.
Based on our model, we study the impact of unique peptides and degenerate
peptides on protein probabilities. Here, degenerate peptides are peptides
shared by at least two proteins. Meanwhile, we also study the relationship of
our model with other methods such as ProteinProphet. A probability confidence
interval can be calculated and used together with probability to filter the
protein identification result. Our method achieves competitive results with
ProteinProphet in a more efficient manner in the experiment based on two
datasets of standard protein mixtures and two datasets of real samples.
We name our program ProteinInfer. Its Java source code is available at
http://bioinformatics.ust.hk/proteininfe
Using MicroPET Imaging in Quantitative Verification of Acupuncture Effect in Ischemia Stroke Treatment
While acupuncture has survived several thousand years’ evolution of medical practice, its function still remains as a myth from the view point of modern medicine. Our goal in this paper is to quantitatively understand the function of acupuncture in ischemia stroke treatment. We carried out a comparative study using the Sprague Dawley rat animal model. We induced the focal cerebral ischemia in the rats using the middle cerebral artery occlusion (MCAO) procedure. For each rat from the real acupuncture group (n = 40), sham acupoint treatment group (n = 54), and blank control group (n = 16), we acquired 3-D FDG-microPET images at baseline, after MCAO, and after treatment (i.e., real acupuncture, sham acupoint treatment, or resting according to the group assignment), respectively. After verifying that the injured area is in the right hemisphere of the cerebral cortex in the brain by using magnetic resonance imaging(MRI) and triphenyl tetrazolium cchloride (TTC)-staining, we directly compared the glucose metabolism in the right hemisphere of each rat. We carried out t-test and permutation test on the image data. Both tests demonstrated that acupuncture had a more positive effect than non-acupoint stimulus and blank control (P < 0.025) in increasing the glucose metabolic level in the stroke-injured area in the brain, while there was no statistically significant difference between non-acupoint stimulus and blank control (P>0.15). The immediate positive effect of acupuncture over sham acupoint treatment and blank control is verified using our experiments. The long-term benefit of acupuncture needs to be further studied
Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation
Object detection is a fundamental step for automated video analysis in many
vision applications. Object detection in a video is usually performed by object
detectors or background subtraction techniques. Often, an object detector
requires manually labeled examples to train a binary classifier, while
background subtraction needs a training sequence that contains no objects to
build a background model. To automate the analysis, object detection without a
separate training phase becomes a critical task. People have tried to tackle
this task by using motion information. But existing motion-based methods are
usually limited when coping with complex scenarios such as nonrigid motion and
dynamic background. In this paper, we show that above challenges can be
addressed in a unified framework named DEtecting Contiguous Outliers in the
LOw-rank Representation (DECOLOR). This formulation integrates object detection
and background learning into a single process of optimization, which can be
solved by an alternating algorithm efficiently. We explain the relations
between DECOLOR and other sparsity-based methods. Experiments on both simulated
data and real sequences demonstrate that DECOLOR outperforms the
state-of-the-art approaches and it can work effectively on a wide range of
complex scenarios.Comment: 30 page
3D-Orientation Signatures with Conic Kernel Filtering for Multiple Motion Analysis
In this paper we propose a new 3D kernel for the recovery of 3D-orientation signatures. The kernel is a Gaussian function defined in local spherical coordinates and its Cartesian support has the shape of a truncated cone with its axis in the radial direction and very small angular support. A set of such kernels is obtained by uniformly sampling the 2D space of polar and azimuth angles. The projection of a local neighborhood on such a kernel set produces a local 3D-orientation signature. In the case of spatiotemporal analysis, such a kernel set can be applied either on the derivative space of a local neighborhood or on the local Fourier transform. The well known planes arising from single or multiple motion produce maxima in the orientation signature. Due to the kernel\u27s local support spatiotemporal signatures possess higher orientation resolution than 3D steerable filters and motion maxima can be detected and localized more accurately. We describe and show in experiments the superiority of the proposed kernels compared to Hough transformation or EM-based multiple motion detection
Optimization-Based Peptide Mass Fingerprinting for Protein Mixture Identification
*Motivation:* In current proteome research, peptide sequencing is probably the most widely used method for protein mixture identification. However, this peptide-centric method has its own disadvantages such as the immense volume of tandem Mass Spectrometry (MS) data for sequencing peptides. With the fast development of technology, it is possible to investigate other alternative techniques. Peptide Mass Fingerprinting (PMF) has been widely used to identify single purified proteins for more than 15 years. Unfortunately, this technique is less accurate than peptide sequencing method and cannot handle protein mixtures, which hampers the widespread use of PMF technique. If we can remove these limitations, PMF will become a useful tool in protein mixture identification. 
*Results:* We first formulate the problem of PMF protein mixture identification as an optimization problem. Then, we show that the use of some simple heuristics enables us to find good solutions. As a result, we obtain much better identification results than previous methods. Moreover, the result on real MS data can be comparable with that of the peptide sequencing method. Through a comprehensive simulation study, we identify a set of limiting factors that hinder the performance of PMF method in protein mixtures. We argue that it is feasible to remove these limitations and PMF can be a powerful tool in the analysis of protein mixtures
BOOST: A fast approach to detecting gene-gene interactions in genome-wide case-control studies
Gene-gene interactions have long been recognized to be fundamentally
important to understand genetic causes of complex disease traits. At present,
identifying gene-gene interactions from genome-wide case-control studies is
computationally and methodologically challenging. In this paper, we introduce a
simple but powerful method, named `BOolean Operation based Screening and
Testing'(BOOST). To discover unknown gene-gene interactions that underlie
complex diseases, BOOST allows examining all pairwise interactions in
genome-wide case-control studies in a remarkably fast manner. We have carried
out interaction analyses on seven data sets from the Wellcome Trust Case
Control Consortium (WTCCC). Each analysis took less than 60 hours on a standard
3.0 GHz desktop with 4G memory running Windows XP system. The interaction
patterns identified from the type 1 diabetes data set display significant
difference from those identified from the rheumatoid arthritis data set, while
both data sets share a very similar hit region in the WTCCC report. BOOST has
also identified many undiscovered interactions between genes in the major
histocompatibility complex (MHC) region in the type 1 diabetes data set. In the
coming era of large-scale interaction mapping in genome-wide case-control
studies, our method can serve as a computationally and statistically useful
tool.Comment: Submitte
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