Human intestinal mucin-like protein (MLP) is homologous with rat MLP in the C-terminal region, and is encoded by a gene on chromosome 11 p 15.5.

A cDNA specific for a human intestinal mucin (MLP) was amplified by PCR from cDNA of cultured human colonic adenocarcinoma cells, LS174T. The human cDNA shared high sequence homology with a corresponding rat intestinal mucin (MLP) cDNA in the 3' terminal region, and hybridized to the same mRNA (approximately 9.0 Kb) that was recognized by a probe for the MUC-2 human intestinal mucin gene. The gene encoding our human mucin peptide also mapped to chromosome 11 p 15.5, the known locus of MUC-2. Our findings suggest that human MLP and MUC-2 are encoded by the same gene and that rat and human intestinal mucin share a common C-terminal amino acid structure.     

When mental fatigue maybe characterized by Event Related Potential (P300) during virtual wheelchair navigation

The goal of this study is to investigate the influence of mental fatigue on the event related potential P300 features (maximum pick, minimum amplitude, latency and period) during virtual wheelchair navigation. For this purpose, an experimental environment was set up based on customizable environmental parameters (luminosity, number of obstacles and obstacles velocities). A correlation study between P300 and fatigue ratings was conducted. Finally, the best correlated features supplied three classification algorithms which are MLP (Multi Layer Perceptron), Linear Discriminate Analysis and Support Vector Machine. The results showed that the maximum feature over visual and temporal regions as well as period feature over frontal, fronto-central and visual regions were correlated with mental fatigue levels. In the other hand, minimum amplitude and latency features didn’t show any correlation. Among classification techniques, MLP showed the best performance although the differences between classification techniques are minimal. Those findings can help us in order to design suitable mental fatigue based wheelchair control.     

基于遗传算法特征选择的HBV再激活分类预测模型

探讨原发性肝癌患者精确放疗后乙型肝炎病毒(hepatitis b virus,HBV)再激活的危险特征和分类预测模型。提出基于遗传算法的特征选择方法,从原发性肝癌数据的初始特征集中选择HBV再激活的最优特征子集。建立贝叶斯和支持向量机的HBV再激活分类预测模型,并预测最优特征子集和初始特征集的分类性能。实验结果表明,基于遗传算法的特征选择提高了HBV再激活分类性能,最优特征子集的分类性能明显优于初始特征子集的分类性能。影响HBV再激活的最优特征子集包括:HBV DNA水平,肿瘤分期TNM,Child-Pugh,外放边界和全肝最大剂量。贝叶斯的分类准确性最高可达82.89%,支持向量机的分类准确性最高可达83.34%。     

A new yeast gene with a myosin-like heptad repeat structure

Summary We isolated a gene encoding a 218 kDa myosin-like protein from Saccharomyces cerevisiae using a monoclonal antibody directed against human platelet myosin as a probe. The protein sequence encoded by the MLP1 gene (for myosin-like protein) contains extensive stretches of a heptad-repeat pattern suggesting that the protein can form coiled coils typical of myosins. Immunolocalization experiments using affinity-purified antibodies raised against a TrpE-MLP1 fusion protein showed a dot-like structure adjacent to the nucleus in yeast cells bearing the MLP1 gene on a multicopy plasmid. In mouse epithelial cells the yeast anti-MLP1 antibodies stained the nucleus. Mutants bearing disruptions of the MLP1 gene were viable, but more sensitive to ultraviolet light than wild-type strains, suggesting an involvement of MLP1 in DNA repair. The MLP1 gene was mapped to chromosome 11, 25 cM from met1.     

Feedforward neural network with adaptive reference pattern layer.

A hybrid neural network architecture is investigated for modeling purposes. The proposed hybrid is based on the multilayer perceptron (MLP) network. In addition to the usual hidden layers, the first hidden layer is selected to be an adaptive reference pattern layer. Each unit in this new layer incorporates a reference pattern that is located somewhere in the space spanned by the input variables. The outputs of these units are the component wise-squared differences between the elements of a reference pattern and the inputs. The reference pattern layer has some resemblance to the hidden layer of the radial basis function (RBF) networks. Therefore the proposed design can be regarded as a sort of hybrid of MLP and RBF networks. The presented benchmark experiments show that the proposed hybrid can provide significant advantages over standard MLPs and RBFs in terms of fast and efficient learning, and compact network structure.     

Classification and clustering analysis of pyruvate dehydrogenase enzyme based on their physicochemical properties

Biological systems are highly organized and enormously coordinated maintaining greater complexity. The increment of secondary data generation and progress of modern mining techniques provided us an opportunity to discover hidden intra and inter relations among these non linear dataset. This will help in understanding the complex biological phenomenon with greater efficiency. In this paper we report comparative classification of Pyruvate Dehydrogenase protein sequences from bacterial sources based on 28 different physicochemical parameters (such as bulkiness, hydrophobicity, total positively and negatively charged residues, α helices, β strand etc.) and 20 type amino acid compositions. Logistic, MLP (Multi Layer Perceptron), SMO (Sequential Minimal Optimization), RBFN (Radial Basis Function Network) and SL (simple logistic) methods were compared in this study. MLP was found to be the best method with maximum average accuracy of 88.20%. Same dataset was subjected for clustering using 2*2 grid of a two dimensional SOM (Self Organizing Maps). Clustering analysis revealed the proximity of the unannotated sequences with the Mycobacterium and Synechococcus genus.     

Defective and nondefective adenovirus vectors for expressing foreign genes in vitro and in vivo.

We have constructed recombinant adenoviruses (Ad), with functional or defective E1a genes, which harbor either the hepatitis B (HB) virus s gene encoding the HB surface antigen, as well as the pre-S2 epitopes, or the bacterial gene encoding chloramphenicol acetyltransferase (CAT) under control of the Ad major late promoter (MLP). The recombinant viruses defective for E1a (Ad.MLP.S2 and Ad.CAT), which can be efficiently propagated only on 293 cells that complement this defect, and the nondefective (Ad.MLP.S2.E1A) recombinant were used to infect a wide spectrum of cells of different origin. The yields of HBs and CAT proteins obtained with these different recombinant viruses demonstrate no real advantage to using nondefective vectors, whatever the cell type infected. The injection into chimpanzees of Ad.MLP.S2 does not elicit the production of antibodies, but can immunologically prime the animals, resulting in a partial protection against HBV challenge.     

Selecting a minimal number of relevant genes from microarray data to design accurate tissue classifiers

It is essential to select a minimal number of relevant genes from microarray data while maximizing classification accuracy for the development of inexpensive diagnostic tests. However, it is intractable to simultaneously optimize gene selection and classification accuracy that is a large parameter optimization problem. We propose an efficient evolutionary approach to gene selection from microarray data which can be combined with the optimal design of various multiclass classifiers. The proposed method (named GeneSelect) consists of three parts which are fully cooperated: an efficient encoding scheme of candidate solutions, a generalized fitness function, and an intelligent genetic algorithm (IGA). An existing hybrid approach based on genetic algorithm and maximum likelihood classification (GA/MLHD) is proposed to select a small number of relevant genes for accurate classification of samples. To evaluate the performance of GeneSelect, the gene selection is combined with the same maximum likelihood classification (named IGA/MLHD) for convenient comparisons. The performance of IGA/MLHD is applied to 11 cancer-related human gene expression datasets. The simulation results show that IGA/MLHD is superior to GA/MLHD in terms of the number of selected genes, classification accuracy, and robustness of selected genes and accuracy.     

Isolation of gibberellin metabolic pathway genes from barley and comparative mapping in barley, wheat and rice

Gene sequences encoding gibberellin (GA) biosynthetic and catabolic enzymes were isolated from Himalaya barley. These genes account for most of the enzymes required for the core pathway of GA biosynthesis as well as for the first major catabolic enzyme. By means of DNA gel blot analysis, we mapped coding sequences to chromosome arms in barley and wheat using barley-wheat chromosome addition lines, nulli-tetrasomic substitution and ditelosomic lines of wheat. These same sequences were used to identify closely related sequences from rice, which were mapped in silico, thereby allowing their syntenic relationship with map locations in barley and wheat to be investigated. Determination of the chromosome arm locations for GA metabolic genes provides a framework for future studies investigating possible identity between GA metabolic genes and dwarfing genes in barley and wheat.Wolfgang Spielmeyer and Marc Ellis have contributed equally to this work.     

On fully automatic feature measurement for banded chromosome classification

Procedures for fully automatic location of chromosome axis and centromere in metaphase chromosomes are described for a practical interactive chromosome analysis system that omits the usual stages of interactive axis and centromere correction. Accuracy of centromere finding and consequential determination of a chromosome's polarity, i.e., which end is which, is measured experimentally. The saving in interaction by not correcting centromeres is compared to the increase in errors at the classification stage and the consequent increase in interaction needed to correct these errors. Some previously unreported features for banded chromosome classification are described, and in particular a set of global shape features is introduced. The discrimination capability of the feature measurements is evaluated by use of simple statistics and by reference to the performance of classifiers trained with various feature subsets. Class discrimination capability of the global shape feature set is shown to be comparable to that of centromere position, a widely used local shape feature. The variability of feature measurements that might occur in data from different laboratories on account of differing tissue, preparation methods, and digitiser hardware is assessed using three data bases of G-banded human metaphase cells. It is shown that the differences can be considerable and that appropriate feature selection and classifier training substantially improve classification performance.     

染色体易位重组位点的自动识别方法研究

染色体易位重组位点的识别对很多染色体遗传性疾病的诊断有着重要的意义.本文基于实际诊断中采集到的24类染色体数据和9号正常与异常染色体数据,构建了一套自动识别染色体易位重组位点的模型和方法.首先,对染色体图像进行预处理,得到了方向梯度直方图特征(HOG)和局部二值模式特征(LBP),构建了基于纹理特征的染色体24分类多通...     

Optimized multilayer perceptrons for molecular classification and diagnosis using genomic data

MOTIVATION: Multilayer perceptrons (MLP) represent one of the widely used and effective machine learning methods currently applied to diagnostic classification based on high-dimensional genomic data. Since the dimensionalities of the existing genomic data often exceed the available sample sizes by orders of magnitude, the MLP performance may degrade owing to the curse of dimensionality and over-fitting, and may not provide acceptable prediction accuracy. RESULTS: Based on Fisher linear discriminant analysis, we designed and implemented an MLP optimization scheme for a two-layer MLP that effectively optimizes the initialization of MLP parameters and MLP architecture. The optimized MLP consistently demonstrated its ability in easing the curse of dimensionality in large microarray datasets. In comparison with a conventional MLP using random initialization, we obtained significant improvements in major performance measures including Bayes classification accuracy, convergence properties and area under the receiver operating characteristic curve (A(z)). SUPPLEMENTARY INFORMATION: The Supplementary information is available on http://www.cbil.ece.vt.edu/publications.htm     

Adaptive pattern classification and universal recoding: I. Parallel development and coding of neural feature detectors

This paper analyses a model for the parallel development and adult coding of neural feature detectors. The model was introduced in Grossberg (1976). We show how experience can retune feature detectors to respond to a prescribed convex set of spatial patterns. In particular, the detectors automatically respond to average features chosen from the set even if the average features have never been experienced. Using this procedure, any set of arbitrary spatial patterns can be recoded, or transformed, into any other spatial patterns (universal recoding), if there are sufficiently many cells in the network's cortex. The network is built from short term memory (STM) and long term memory (LTM) mechanisms, including mechanisms of adaptation, filtering, contrast enhancement, tuning, and nonspecific arousal. These mechanisms capture some experimental properties of plasticity in the kitten visual cortex. The model also suggests a classification of adult feature detector properties in terms of a small number of functional principles. In particular, experiments on retinal dynamics, including amarcrine cell function, are suggested.Supported in part by the Advanced Research Projects Agency under ONR Contract No. N00014-76-C-0185     

Detection of baleen whale species using kernel dynamic mode decomposition-based feature extraction with a hidden Markov model

The negative effects of human activities within the ecological space of whales remains an issue of concern to marine ecologists. The accurate detection and subsequent classification of whale species are vital in mitigating these negative effects. Automatic detection techniques have come in handy for the efficient detection of the various whale species without human error. Hidden Markov model (HMM) remains one the most efficient detectors of whale species. However, its performance efficiency is greatly influenced by the feature vectors adapted with it. In this work, we propose the use of the kernel dynamic mode decomposition (kDMD) algorithm as a tool to extract features of baleen whale species, which are then adapted with HMM for their detection. Dynamic mode decomposition (DMD) is an eigendecomposition-based algorithm that is capable of extracting latent underlying features of non-linear signals such as those vocalised by whales. However, the underlying cost of DMD is the singular value decomposition (SVD), which adds significant complexity to the modes derivation steps. Thus, this work is introducing the kernel method into the DMD, in order to find a more efficient way of computing DMD without explicitly using the SVD algorithm. Furthermore, the feature formation steps in the original DMD was modified (mDMD) in this work, to make it more generic for datasets with sparse whale sound samples. The performance of the detectors was tested on datasets containing sounds of southern right whales (SRWs) and humpback whales. The results obtained show a high true positive rate (TPR), high precision (PREC) and low error rate (ERR) for both species. The performance of the three DMD-based feature-extraction methods were compared. The kDMD-HMM generally performed better than the mDMD-HMM and DMD-HMM detectors. The methods proposed here can be tailored for the automatic detection and classification of other vocalising animal species through their sounds.     

A hybrid genetic-neural model for predicting protein structural classes

A genetic algorithm (GA) for feature selection in conjunction with neural network was applied to predict protein structural classes based on single amino acid and all dipeptide composition frequencies. These sequence parameters were encoded as input features for a GA in feature selection procedure and classified with a three-layered neural network to predict protein structural classes. The system was established through optimization of the classification performance of neural network which was used as evaluation function. In this study, self-consistency and jackknife tests on a database containing 498 proteins were used to verify the performance of this hybrid method, and were compared with some of prior works. The adoption of a hybrid model, which encompasses genetic and neural technologies, demonstrated to be a promising approach in the task of protein structural class prediction.     

Application of genetic algorithms and constructive neural networks for the analysis of microarray cancer data

Background

Extracting relevant information from microarray data is a very complex task due to the characteristics of the data sets, as they comprise a large number of features while few samples are generally available. In this sense, feature selection is a very important aspect of the analysis helping in the tasks of identifying relevant genes and also for maximizing predictive information.

Methods

Due to its simplicity and speed, Stepwise Forward Selection (SFS) is a widely used feature selection technique. In this work, we carry a comparative study of SFS and Genetic Algorithms (GA) as general frameworks for the analysis of microarray data with the aim of identifying group of genes with high predictive capability and biological relevance. Six standard and machine learning-based techniques (Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), Naive Bayes (NB), C-MANTEC Constructive Neural Network, K-Nearest Neighbors (kNN) and Multilayer perceptron (MLP)) are used within both frameworks using six free-public datasets for the task of predicting cancer outcome.

Results

Better cancer outcome prediction results were obtained using the GA framework noting that this approach, in comparison to the SFS one, leads to a larger selection set, uses a large number of comparison between genetic profiles and thus it is computationally more intensive. Also the GA framework permitted to obtain a set of genes that can be considered to be more biologically relevant. Regarding the different classifiers used standard feedforward neural networks (MLP), LDA and SVM lead to similar and best results, while C-MANTEC and k-NN followed closely but with a lower accuracy. Further, C-MANTEC, MLP and LDA permitted to obtain a more limited set of genes in comparison to SVM, NB and kNN, and in particular C-MANTEC resulted in the most robust classifier in terms of changes in the parameter settings.

Conclusions

This study shows that if prediction accuracy is the objective, the GA-based approach lead to better results respect to the SFS approach, independently of the classifier used. Regarding classifiers, even if C-MANTEC did not achieve the best overall results, the performance was competitive with a very robust behaviour in terms of the parameters of the algorithm, and thus it can be considered as a candidate technique for future studies.

     

Karyotyping of comparative genomic hybridization human metaphases using kernel nearest-neighbor algorithm

BACKGROUND: Comparative genomic hybridization (CGH) is a relatively new molecular cytogenetic method that detects chromosomal imbalances. Automatic karyotyping is an important step in CGH analysis because the precise position of the chromosome abnormality must be located and manual karyotyping is tedious and time-consuming. In the past, computer-aided karyotyping was done by using the 4',6-diamidino-2-phenylindole, dihydrochloride (DAPI)-inverse images, which required complex image enhancement procedures. METHODS: An innovative method, kernel nearest-neighbor (K-NN) algorithm, is proposed to accomplish automatic karyotyping. The algorithm is an application of the "kernel approach," which offers an alternative solution to linear learning machines by mapping data into a high dimensional feature space. By implicitly calculating Euclidean or Mahalanobis distance in a high dimensional image feature space, two kinds of K-NN algorithms are obtained. New feature extraction methods concerning multicolor information in CGH images are used for the first time. RESULTS: Experiment results show that the feature extraction method of using multicolor information in CGH images improves greatly the classification success rate. A high success rate of about 91.5% has been achieved, which shows that the K-NN classifier efficiently accomplishes automatic chromosome classification from relatively few samples. CONCLUSIONS: The feature extraction method proposed here and K-NN classifiers offer a promising computerized intelligent system for automatic karyotyping of CGH human chromosomes.