G protein coupled receptors (GPCRs) are one of the most prominent and abundant family of membrane proteins in the human genome. Since they are main targets of many drugs, GPCR research has grown significantly in recen...
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ISBN:
(纸本)9783642341236
G protein coupled receptors (GPCRs) are one of the most prominent and abundant family of membrane proteins in the human genome. Since they are main targets of many drugs, GPCR research has grown significantly in recent years. However the fact that only few structures of GPCRs are known still remains as an important challenge. therefore, the classification of GPCRs is a significant problem provoked from increasing gap between orphan GPCR sequences and a small amount of annotated ones. this work employs motif distillation using defined parameters, distinguishing power evaluation method and general weighted set cover problem in order to determine the minimum set of motifs which can cover a particular GPCR subfamily. Our results indicate that in Family A Peptide subfamily, 91% of all proteins listed in GPCRdb can be covered by using only 691 different motifs, which can be employed later as an invaluable source for developing a third level GPCR classification tool.
Robotic applications impose hard real-time demands on their vision components. To accommodate the realtime constraints, the visual component of robotic systems are often simplified by narrowing the scope of the vision...
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DNA sequences recognition is a key problem in bioinformatics and biomedical informatics. In this paper, we solve this problem by use of the probability method and metric instead of traditional frequency metric because...
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ISBN:
(纸本)9781479927616
DNA sequences recognition is a key problem in bioinformatics and biomedical informatics. In this paper, we solve this problem by use of the probability method and metric instead of traditional frequency metric because the characters in DNA alphabet set meet the Markov properties. For this purpose, transition probabilities, transition matrixes, and log odds ratios are defined. And then, we put forward our sequence recognition algorithm based on the Markov model (SRM), which has better performance on time complexity than some sequence alignment algorithms in the same field. the results of the contrast experiments show that our SRM algorithm can recognize DNA sequences correctly and effectively without any ambiguities.
the proceedings contain 39 papers. the topics discussed include: weighted maximum variance dimensionality reduction;improved performance of computer networks by embedded pattern detection;on two definitions of reduct;...
ISBN:
(纸本)9783319074900
the proceedings contain 39 papers. the topics discussed include: weighted maximum variance dimensionality reduction;improved performance of computer networks by embedded pattern detection;on two definitions of reduct;a family of two-dimensional benchmark data sets and its application to comparing different cluster validation indices;studying Netconf in hybrid rule ordering strategies for associative classification;problem solving environment based on knowledge based system principles;positive and negative local trend association patterns in analysis of associations between time series;an effective permutant selection heuristic for proximity searching in metric spaces;a feasibility study on the use of binary keypoint descriptors for 3D face recognition;robust head gestures recognition for assistive technology;and noise-removal markers to improve PCA-based face recognition.
A basic task in protein analysis is to discover a set of sequence patterns that characterizes the function of a protein family. To address this task, we introduce a synthesized pattern representation called Aligned Pa...
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ISBN:
(纸本)9783642341236
A basic task in protein analysis is to discover a set of sequence patterns that characterizes the function of a protein family. To address this task, we introduce a synthesized pattern representation called Aligned pattern (AP) Cluster to discover potential functional segments in protein sequences. We apply our algorithm to identify and display the binding segments for the Cytochrome C. and Ubiquitin protein families. the resulting AP Clusters correspond to protein binding segments that surround the binding residues. When compared to the results from the protein annotation databases, PROSITE and pFam, ours are more efficient in computation and comprehensive in quality. the significance of the AP Cluster is that it is able to capture subtle variations of the binding segments in protein families. It thus could help to reduce time-consuming simulations and experimentation in the protein analysis.
In this paper, the algorithm for thinning of grey-scale images is proposed that is based on a pseudo-distance map (PDM). the PDM is a simplified distance map of gray-scale image and uses only that features of image an...
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ISBN:
(纸本)0769525210
In this paper, the algorithm for thinning of grey-scale images is proposed that is based on a pseudo-distance map (PDM). the PDM is a simplified distance map of gray-scale image and uses only that features of image and objects that are necessary to build a skeleton. the algorithm works fast for large gray-scale images and allows constructing a high quality skeleton.
Various algorithms for the automatic extraction of features from micrographs of forged INCONEL 718 (TM) will be presented in this paper this includes the extraction of an optimized boundary image, the elimination of s...
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ISBN:
(纸本)0769525210
Various algorithms for the automatic extraction of features from micrographs of forged INCONEL 718 (TM) will be presented in this paper this includes the extraction of an optimized boundary image, the elimination of scratches and parallel lines ("twins") and from subsequent evaluation and the detection of delta phase particles. Requested features are grain size, the amount and distribution of 5 phase with respect to grain boundaries and anisotropic effects.
One of the important problems in functional genomics is how to select the disease genes. In this regard, the paper presents a new similarity measure to compute the functional similarity between two genes. It is based ...
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Protein fold recognition has been the focus of computational biologists for many years. In order to map a protein primary structure to its correct 3D fold, we introduce in this paper a machine learning paradigm that w...
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ISBN:
(纸本)0769525210
Protein fold recognition has been the focus of computational biologists for many years. In order to map a protein primary structure to its correct 3D fold, we introduce in this paper a machine learning paradigm that we entitled 11 structural hidden Markov model" (SHMM). We show how the concept of SHMM can efficiently use the protein secondary structure during the fold recognition task. Experimental results showed that the SHMM outperforms the SVM with a 6% improvement in the average accuracy. However, because in this application the two classifiers are not correlated, therefore their combination based on the highest rank criterion boosted the SHMM average accuracy with 10%.
the insufficient performance of statistical recognition of composite objects (images, speech signals) is explored in case of medium-sized database (thousands of classes). In contrast to heuristic approximate nearest-n...
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