13746374
A Generalized Output-Coding Scheme with SVM for Multiclass Microarray Classification
Multiclass cancer classification based on microarray data is described. A generalized output-coding scheme combined with binary classifiers is used. Different coding strategies, decoding functions and feature selection methods are combined and validated on two cancer datasets: GCM and ALL. The effects of these different methods and their combinations are then discussed. The highest testing accuracies achieved are 78% and 100% for the two datasets respectively. The results are considered to be very good when compared with the other researchers’ work.
74980
PLS and SVD based penalized logistic regression for cancer classification using microarray data
Accurate cancer prediction is important for treatment of cancers. The combination of two dimension reduction methods, partial least squares (PLS) and singular value decomposition (SVD), with the penalized logistic regression (PLR) has created powerful classifiers for cancer prediction using microarray data. Comparing with support vector machine (SVM) on seven publicly available cancer datasets, the new algorithms can achieve very good performance and run much faster. They also have the advantage that the probabilities of predictions can be directly given. PLS based PLR is also combined with recursive feature elimination (RFE) to select a 16-gene subset for acute leukemia cancer classification. The testing error on this subset of genes is empirically zero.
43366543
VLSI functional testing using critical path traces at hardware description language level
A new approach for testing VLSI circuits is presented. Through backward critical path tracing, a test and all faults detectable by the test are generated simultaneously. Therefore, the expensive fault simulation is completely eliminated. We present a critical path test generation procedure for digital systems described by hardware description language (HDL). A multiplication circuit described by a HDL is utilized for demonstrating the test generation method.
19415028
Unified model in identity subspace for face recognition
Human faces have two important characteristics: (1) They are similar objects and the specific, variations of each face are similar to each other; (2) They are nearly bilateral symmetric. Exploiting the two important properties, we build a unified model in identity subspace (UMIS) as a novel technique for face recognition from only one example image per person. An identity subspace spanned by bilateral symmetric bases, which compactly encodes identity information. is presented. The unified model, trained on an obtained training set with multiple samples per class from a known people groupA, can be generalized well to facial images of unknown individuals, and can be used to recognize facial images from an unknown people groupB with only one sample per subject. Extensive experimental results on two public databases (the Yale database and the Bern database) and our own database (the ICT-JDL database) demonstrate that the UMIS approach is significantly effective and robust for face recognition.
62074016
Dimension Reduction-Based Penalized Logistic Regression for Cancer Classification Using Microarray Data
The use of penalized logistic regression for cancer classification using microarray expression data is presented. Two dimension reduction methods are respectively combined with the penalized logistic regression so that both the classification accuracy and computational speed are enhanced. Two other machine-learning methods, support vector machines and least-squares regression, have been chosen for comparison. It is shown that our methods have achieved at least equal or better results. They also have the advantage that the output probability can be explicitly given and the regression coefficients are easier to interpret. Several other aspects, such as the selection of penalty parameters and components, pertinent to the application of our methods for cancer classification are also discussed.
7641777
GBNet: Deciphering regulatory rules in the co-regulated genes using a Gibbs sampler enhanced Bayesian network approach
BackgroundCombinatorial regulation of transcription factors (TFs) is important in determining the complex gene expression patterns particularly in higher organisms. Deciphering regulatory rules between cooperative TFs is a critical step towards understanding the mechanisms of combinatorial regulation.ResultsWe present here a Bayesian network approach called GBNet to search for DNA motifs that may be cooperative in transcriptional regulation and the sequence constraints that these motifs may satisfy. We showed that GBNet outperformed the other available methods in the simulated and the yeast data. We also demonstrated the usefulness of GBNet on learning regulatory rules between YY1, a human TF, and its co-factors. Most of the rules learned by GBNet on YY1 and co-factors were supported by literature. In addition, a spacing constraint between YY1 and E2F was also supported by independent TF binding experiments.ConclusionWe thus conclude that GBNet is a useful tool for deciphering the "grammar" of transcriptional regulation.
17743882
Morphometric Analysis of Brain Structures for Improved Discrimination
We perform discriminative analysis of brain structures using morphometric information. Spherical harmonics technique and point distribution model are used for shape description. Classification is performed using linear discriminants and support vector machines with several feature selection approaches. We consider both inclusion and exclusion of volume information in the discrimination. We perform extensive experimental studies by applying different combinations of techniques to hippocampal data in schizophrenia and achieve best jackknife classification accuracies of 95% (whole set) and 90% (right-handed males), respectively. Our results find that the left hippocampus is a better predictor than the right in the complete dataset, but that the right hippocampus is a stronger predictor than the left in the right-handed male subset. We also propose a new method for visualization of discriminative patterns.
2536263
Multi-scale Voxel-Based Morphometry Via Weighted Spherical Harmonic Representation
Although the voxel-based morphometry (VBM) has been widely used in quantifying the amount of gray matter of the human brain, the optimal amount of registration that should be used in VBM has not been addressed. In this paper, we present a novel multi-scale VBM using the weighted spherical harmonic (SPHARM) representation to address the issue. The weighted-SPHARM provides the explicit smooth functional representation of a true unknown cortical boundary. Based on this new representation, the gray matter tissue density is constructed using the Euclidean distance map from a voxel to the estimated smooth cortical boundary. The methodology is applied in localizing abnormal cortical regions in a group of autistic subjects.
14020196
Spherical mapping for processing of 3D closed surfaces
Abstract The spherical harmonic (SPHARM) description is a powerful surface modeling technique used by many applications in biomedical imaging. However, earlier SPHARM studies employ a spherical parameterization procedure that can be applied only to voxel surfaces. This paper presents CALD, a new spherical parameterization algorithm that makes the SPHARM model applicable to general triangle meshes. The CALD algorithm starts from an initial mapping and performs novel local and global smoothing methods alternately until a solution is approached. This new algorithm can also be used for processing of 3D closed surfaces in many different areas, including medical image analysis, computer vision, and computer graphics.
6802677
A surface-based approach for classification of 3D neuroanatomic structures
We present a new framework for 3D surface object classification that combines a powerful shape description method with suitable pattern classification techniques. Spherical harmonic parameterization and normalization techniques are used to describe a surface shape and derive a dual high dimensional landmark representation. A point distribution model is applied to reduce the dimensionality. Fisher's linear discriminants and support vector machines are used for classification. Several feature selection schemes are proposed for learning better classifiers. After showing the effectiveness of this framework using simulated shape data, we apply it to real hippocampal data in schizophrenia and perform extensive experimental studies by examining different combinations of techniques. We achieve best leave-one-out cross-validation accuracies of 93% (whole set, N = 56) and 90% (right-handed males, N = 39), respectively, which are competitive with the best results in previous studies using different techniques on similar types of data. Furthermore, to help medical diagnosis in practice, we employ a threshold-free receiver operating characteristic (ROC) approach as an alternative evaluation of classification results as well as propose a new method for visualizing discriminative patterns.
15589759
Mining the Most Interesting Web Access Associations
Web access patterns can provide valuable information for website designers in making website-based communication more efficient. To extract interesting or useful web access patterns, we use data mining techniques which analyze historical web access logs. In this paper, we present an efficient approach to mine the most interesting web access associations, where the word "interesting" denotes patterns that are supported by a high fraction of access activities with strong confidence. Our approach consists of three steps: 1) transform raw web logs to a relational table; 2) convert the relational table to a collection of access transactions; 3) mine the transaction collection to extract associations and rules. In both step 1 and step 2, we provide users with an effective mechanism to help them generate only "interesting" access records and transactions for mining. In the third step, we present a new efficient data mining algorithm to find the most interesting web access associations. We evaluate this approach using both synthetic data sets and real web logs and show the efficacy, efficiency and good scalability of the proposed mining methods.