Charge environments around phosphorylation sites in proteins

Background  

Phosphorylation is a central feature in many biological processes. Structural analyses have identified the importance of charge-charge interactions, for example mediating phosphorylation-driven allosteric change and protein binding to phosphopeptides. Here, we examine computationally the prevalence of charge stabilisation around phosphorylated sites in the structural database, through comparison with locations that are not phosphorylated in the same structures.     

Cluster analysis for DNA methylation profiles having a detection threshold

Background  

DNA methylation, a molecular feature used to investigate tumor heterogeneity, can be measured on many genomic regions using the MethyLight technology. Due to the combination of the underlying biology of DNA methylation and the MethyLight technology, the measurements, while being generated on a continuous scale, have a large number of 0 values. This suggests that conventional clustering methodology may not perform well on this data.     

NBA-Palm: prediction of palmitoylation site implemented in Na?ve Bayes algorithm

Background  

Protein palmitoylation, an essential and reversible post-translational modification (PTM), has been implicated in cellular dynamics and plasticity. Although numerous experimental studies have been performed to explore the molecular mechanisms underlying palmitoylation processes, the intrinsic feature of substrate specificity has remained elusive. Thus, computational approaches for palmitoylation prediction are much desirable for further experimental design.     

Recursive Cluster Elimination (RCE) for classification and feature selection from gene expression data

Background  

Classification studies using gene expression datasets are usually based on small numbers of samples and tens of thousands of genes. The selection of those genes that are important for distinguishing the different sample classes being compared, poses a challenging problem in high dimensional data analysis. We describe a new procedure for selecting significant genes as recursive cluster elimination (RCE) rather than recursive feature elimination (RFE). We have tested this algorithm on six datasets and compared its performance with that of two related classification procedures with RFE.     

Polarization of the endomembrane system is an early event in fucoid zygote development

Background  

Fucoid zygotes are excellent experimental organisms for investigating mechanisms that establish cell polarity and determine the site of tip growth. A common feature of polarity establishment is targeting endocytosis and exocytosis (secretion) to localized cortical domains. We have investigated the spatiotemporal development of endomembrane asymmetry in photopolarizing zygotes, and examined the underlying cellular physiology.     

Tumor classification and marker gene prediction by feature selection and fuzzy c-means clustering using microarray data

Background  

Using DNA microarrays, we have developed two novel models for tumor classification and target gene prediction. First, gene expression profiles are summarized by optimally selected Self-Organizing Maps (SOMs), followed by tumor sample classification by Fuzzy C-means clustering. Then, the prediction of marker genes is accomplished by either manual feature selection (visualizing the weighted/mean SOM component plane) or automatic feature selection (by pair-wise Fisher's linear discriminant).     

Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data

Background  

Numerous feature selection methods have been applied to the identification of differentially expressed genes in microarray data. These include simple fold change, classical t-statistic and moderated t-statistics. Even though these methods return gene lists that are often dissimilar, few direct comparisons of these exist. We present an empirical study in which we compare some of the most commonly used feature selection methods. We apply these to 9 publicly available datasets, and compare, both the gene lists produced and how these perform in class prediction of test datasets.     

Feature extraction and signal processing for nylon DNA microarrays

Background  

High-density DNA microarrays require automatic feature extraction methodologies and softwares. These can be a potential source of non-reproducibility of gene expression measurements. Variation in feature location or in signal integration methodology may be a significant contribution to the observed variance in gene expression levels.     

The migration of paraxial and lateral plate mesoderm cells emerging from the late primitive streak is controlled by different Wnt signals

Background  

Co-ordinated cell movement is a fundamental feature of developing embryos. Massive cell movements occur during vertebrate gastrulation and during the subsequent extension of the embryonic body axis. These are controlled by cell-cell signalling and a number of pathways have been implicated. Here we use long-term video microscopy in chicken embryos to visualize the migration routes and movement behaviour of mesoderm progenitor cells as they emerge from the primitive streak (PS) between HH stages 7 and 10.     

Hypoxia upregulates angiogenesis and synovial cell migration in rheumatoid arthritis

Introduction  

Rheumatoid arthritis (RA) is characterised by invasion of cartilage, bone and tendon by inflamed synovium. Previous studies in our laboratory have shown that hypoxia is a feature of RA synovitis. In the present study, we investigated the consequences of hypoxia on angiogenesis and synovial fibroblast migration in RA.     

Expression of carbonic anhydrases IX and XII during mouse embryonic development

Background  

Of the thirteen active carbonic anhydrase (CA) isozymes, CA IX and XII have been linked to carcinogenesis. It has been suggested that these membrane-bound CAs participate in cancer cell invasion, which is facilitated by an acidic tumor cell environment. Since active cell migration is a characteristic feature of embryonic development, we set out to explore whether these isozymes are expressed in mouse embryos of different ages. The studies were focused on organogenesis stage.     

PPSP: prediction of PK-specific phosphorylation site with Bayesian decision theory

Background  

As a reversible and dynamic post-translational modification (PTM) of proteins, phosphorylation plays essential regulatory roles in a broad spectrum of the biological processes. Although many studies have been contributed on the molecular mechanism of phosphorylation dynamics, the intrinsic feature of substrates specificity is still elusive and remains to be delineated.     

Raised intrathecal levels of APRIL and BAFF in patients with systemic lupus erythematosus: relationship to neuropsychiatric symptoms

Introduction  

The tumour necrosis factor (TNF) family ligands BAFF (B-cell activating factor of TNF family) and APRIL (a proliferation-inducing ligand) are essential for B-cell survival and function. Elevated serum levels of BAFF and APRIL have been reported earlier in patients with systemic lupus erythematosus (SLE). Since autoantibody formation in the central nervous system (CNS) is a distinct feature of neuropsychiatric SLE (NPSLE), we have investigated whether NPSLE is associated with an enhanced intrathecal production of APRIL and BAFF.     

Protein sequences classification by means of feature extraction with substitution matrices

Background  

This paper deals with the preprocessing of protein sequences for supervised classification. Motif extraction is one way to address that task. It has been largely used to encode biological sequences into feature vectors to enable using well-known machine-learning classifiers which require this format. However, designing a suitable feature space, for a set of proteins, is not a trivial task. For this purpose, we propose a novel encoding method that uses amino-acid substitution matrices to define similarity between motifs during the extraction step.     

A combinational feature selection and ensemble neural network method for classification of gene expression data

Background  

Microarray experiments are becoming a powerful tool for clinical diagnosis, as they have the potential to discover gene expression patterns that are characteristic for a particular disease. To date, this problem has received most attention in the context of cancer research, especially in tumor classification. Various feature selection methods and classifier design strategies also have been generally used and compared. However, most published articles on tumor classification have applied a certain technique to a certain dataset, and recently several researchers compared these techniques based on several public datasets. But, it has been verified that differently selected features reflect different aspects of the dataset and some selected features can obtain better solutions on some certain problems. At the same time, faced with a large amount of microarray data with little knowledge, it is difficult to find the intrinsic characteristics using traditional methods. In this paper, we attempt to introduce a combinational feature selection method in conjunction with ensemble neural networks to generally improve the accuracy and robustness of sample classification.     

Feature selection for splice site prediction: A new method using EDA-based feature ranking

Background  

The identification of relevant biological features in large and complex datasets is an important step towards gaining insight in the processes underlying the data. Other advantages of feature selection include the ability of the classification system to attain good or even better solutions using a restricted subset of features, and a faster classification. Thus, robust methods for fast feature selection are of key importance in extracting knowledge from complex biological data.     

Feature selection using Haar wavelet power spectrum

Background  

Feature selection is an approach to overcome the 'curse of dimensionality' in complex researches like disease classification using microarrays. Statistical methods are utilized more in this domain. Most of them do not fit for a wide range of datasets. The transform oriented signal processing domains are not probed much when other fields like image and video processing utilize them well. Wavelets, one of such techniques, have the potential to be utilized in feature selection method. The aim of this paper is to assess the capability of Haar wavelet power spectrum in the problem of clustering and gene selection based on expression data in the context of disease classification and to propose a method based on Haar wavelet power spectrum.     

A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data

Background  

Regularized regression methods such as principal component or partial least squares regression perform well in learning tasks on high dimensional spectral data, but cannot explicitly eliminate irrelevant features. The random forest classifier with its associated Gini feature importance, on the other hand, allows for an explicit feature elimination, but may not be optimally adapted to spectral data due to the topology of its constituent classification trees which are based on orthogonal splits in feature space.     

Phylogenetic analysis of ferlin genes reveals ancient eukaryotic origins

Background  

The ferlin gene family possesses a rare and identifying feature consisting of multiple tandem C2 domains and a C-terminal transmembrane domain. Much currently remains unknown about the fundamental function of this gene family, however, mutations in its two most well-characterised members, dysferlin and otoferlin, have been implicated in human disease. The availability of genome sequences from a wide range of species makes it possible to explore the evolution of the ferlin family, providing contextual insight into characteristic features that define the ferlin gene family in its present form in humans.