Currently, Dual Specificity YAK1-Related Kinases (MNB/DYRK) were found in slime molds, protista, fungi, and animals, but the existence of plant homologues is still unclear. In the present study, we have identified 14 potential plant homologues with the previously unknown functions, based on the strong sequence similarity. The results of bioinformatics analysis revealed their correspondence to DYRK1A, DYRK1B, DYRK3, and DYRK4. For two plant homologues of animal DYRK1A from <i>Physcomitrella patens</i> and <i>Arabidopsis thaliana</i> spatial structures of catalytic domains were predicted, as well as their complexes with ADP and selective inhibitor d15. Comparative analysis of 3D-structures of the human DYRK1A and plant homologues, their complexes with the specific inhibitors, and results of molecular dynamics confirm their structural and functional similarity with high probability. Preliminary data indicate the presence of potential MNB/DYRK specific phosphorylation sites in such proteins associated with plant cytoskeleton as plant microtubule-associated proteins WVD2 and WDL1, and FH5 and SCAR2 involved in the organization and polarity of the actin cytoskeleton and some kinesin-like microtubule motor proteins.

Pavel Karpov

Aleksey Raevsky

Maxim Korablyov

Yaroslav Blume

Identification of Plant Homologues of Dual Specificity Yak1-Related Kinases

Microarray datasets are widely used resources to predict and characterize functional entities of the whole genomics. The study initiated here aims to identify overexpressed stress responsive genes using microarray datasets applying <i>in silico</i> approaches. The target also extended to build a protein-protein interaction model of regulatory genes with their upstream and downstream connection in <i>Arabidopsis thaliana</i>. Four microarray datasets generated treating abiotic stresses like salinity, cold, drought, and abscisic acid (ABA) were chosen. Retrieved datasets were firstly filtered based on their expression comparing to control. Filtered datasets were then used to create an expression hub. Extensive literature mining helped to identify the regulatory molecules from the expression hub. The study brought out 42 genes/TF/enzymes as the role player during abiotic stress response. Further bioinformatics study and also literature mining revealed that thirty genes from those forty-two were highly correlated in all four datasets and only eight from those thirty genes were determined as highly responsive to the above abiotic stresses. Later their protein-protein interaction (PPI), conserved sequences, protein domains, and GO biasness were studied. Some web based tools and software like String database, Gene Ontology, InterProScan, NCBI BLASTn suite, etc. helped to extend the study arena.

Samsad Razzaque

Rabab Mahdi

Aparna Islam

Identification of Synchronized Role of Transcription Factors, Genes, and Enzymes in <i>Arabidopsis thaliana</i> under Four Abiotic Stress Responsive Pathways

This paper introduces a computer-assisted diagnosis (CAD) system for automatic mitosis detection from breast cancer histopathology slide images. In this system, a new approach for reducing the number of false positives is proposed based on Teaching-Learning-Based optimization (TLBO). The proposed CAD system is implemented on the histopathology slide images acquired by Aperio XT scanner (scanner A). In TLBO algorithm, the number of false positives (falsely detected nonmitosis candidates as mitosis ones) is defined as a cost function and, by minimizing it, many of nonmitosis candidates will be removed. Then some color and texture (textural) features such as those derived from cooccurrence and run-length matrices are extracted from the remaining candidates and finally mitotic cells are classified using a specific support vector machine (SVM) classifier. The simulation results have proven the claims about the high performance and efficiency of the proposed CAD system.

Ramin Nateghi

Habibollah Danyali

Mohammad Sadegh Helfroush

Ashkan Tashk

Intelligent CAD System for Automatic Detection of Mitotic Cells from Breast Cancer Histology Slide Images Based on Teaching-Learning-Based Optimization

With the rapid development of high throughput sequencing technologies, new transcriptomes can be sequenced for little cost with high coverage. Sequence assembly approaches have been modified to meet the requirements for <i>de novo</i> transcriptomes, which have complications not found in traditional genome assemblies such as variation in coverage for each candidate mRNA and alternative splicing. As a consequence, <i>de novo</i> assembly strategies tend to generate a large number of redundant contigs due to sequence variations, which adversely affects downstream analysis and experiments. In this work we proposed TransPS, a transcriptome post-scaffolding method, to generate high quality, nonredundant <i>de novo</i> transcriptomes. TransPS shows promising results on the test transcriptome datasets, where redundancy is greatly reduced by more than 50&#x25; and, at the same time, coverage is improved considerably. The web server and source code are available.

Mingming Liu

Zach N. Adelman

Kevin M. Myles

Liqing Zhang

A Transcriptome Post-Scaffolding Method for Assembling High Quality Contigs

Multiple antigen miniarrays can provide accurate tools for cancer detection and diagnosis. These miniarrays can be validated by examining their operating characteristics in classifying individuals as either cancer patients or normal (non-cancer) subjects. We describe the use of restricted Boltzmann machines for this classification problem, relative to diagnosis of hepatocellular carcinoma. In this setting, we find that its operating characteristics are similar to a logistic regression standard and suggest that restricted Boltzmann machines merit further consideration for classification problems.

James A. Koziol

Eng M. Tan

Liping Dai

Pengfei Ren

Jian-Ying Zhang

Restricted Boltzmann Machines for Classification of Hepatocellular Carcinoma

In a phage <svg     style="vertical-align:-0.20064pt;width:9.5124998px;" id="M1" height="12.4375" version="1.1" viewBox="0 0 9.5124998 12.4375" width="9.5124998"  xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg">
	
		
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</svg> genetic switch model, bistable dynamical behavior can be destroyed due to the bifurcation caused by inappropriately chosen model parameters. Since the values of many parameters with biological
significance often cannot be accurately acquired, it is thus of fundamental importance to analyze how and to
which extent the system dynamics is influenced by model parameters, especially those parameters pertaining
to binding energies. In this paper, we apply a Jacobian method to investigate the relation between bifurcation
and parameter uncertainties on a phage <svg     style="vertical-align:-0.20064pt;width:9.5124998px;" id="M2" height="12.4375" version="1.1" viewBox="0 0 9.5124998 12.4375" width="9.5124998"  xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg">
	
		
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</svg> OR model. By introducing bistable range as a measure of system robustness, we find that RNA polymerase binding energies have the minimum bistable ranges among all the
binding energies, which implies that the uncertainties on these parameters tend to demolish the bistability
more easily. Moreover, parameters describing mutual prohibition between proteins Cro and CI have finite
bistable ranges, whereas those describing self-prohibition have infinity bistable ranges. Hence, the former are
more sensitive to parameter uncertainties than the latter. These results help to understand the influence of
parameter uncertainties on the bifurcation and thus bistability.

Ning Xu

Xue Lei

Ping Ao

Jun Zhang

Bifurcation Analysis for Phage Lambda with Binding Energy Uncertainty

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Computational Biology Journal