ISSN (Print): 1570-1646
ISSN (Online): 1875-6247
Volume 16, 5 Issues, 2019
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ISSN (Print): 1570-1646
ISSN (Online): 1875-6247
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Ajoy Basak Ottawa Hospital Research Institute University of Ottawa Ottawa, ON Canada
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"The wide areas covered by these articles gives an idea of how diverse the field is."
Quoted in Advances In: Proteomics - Preparing proteins' published in March 25, 2005 issue of Science
NANOFLUIDICS AND MICROFLUIDICS: NOVEL APPROACHES IN BIOMEDICAL SCIENCE
Guest Editor(s): Alexandru Mihai GRUMEZESCU
Tentative Publication Date: May, 2015
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Thank you for considering my opinion. I am very satisfied with your services. Everything went as scheduled and I received nice comments from the referees that helped me in improving my work. When submitting my work, the freedom to choose the charges we consider necessary, which may also end up with a free of charge publication, is very important for researchers from developing countries to get to our works published in nice journals as yours.
Diego de Carvalho Carneiro (Health Sciences Institute, Federal University of Bahia, Salvador, Bahia, Brazil.)
Has contributed: Conjugative Post-Translational Modifications for Pharmacological Improvement of Therapeutic Proteins.
12 Abstract Ahead of Print are available electronically
14 Articles Ahead of Print are available electronically
Advances in sequencing techniques has resulted in a large body of protein sequences in the last
decades. However, annotating these proteins lags far behind. For example, the UniProtKB database 
currently collected more than 60 million sequences, of which only 1/100, i.e., about 600 thousands
have experimentally verified annotations . Protein is the principally structural element and signaling
messenger. The knowledge about proteins such as types, structure, subcellular localization, function
and interactions, is vital for us to explore and understand cellular mechanism. Therefore, annotation of
proteins is one of most challenging tasks in the post-genomic era.
To make up for the low throughput of experiments’ annotating proteins, some researchers have
pioneered a computational route to automatic annotation [3-8]. Such methodology has the advantage of
high throughput and efficiency over physical experiments. At theory, the number of annotated proteins is not limited. Once
building a better computational model or method, one can use it to annotate more efficiently thousands of proteins within
several seconds. There is no doubt that computational methods or models are very promising for annotating proteins.
Protein functions and inter-interactions are a key to understand the molecular machine of the cell. Increasing effort has paid
to explore protein functions and interactions . Especially in the past twenty years, a large number of computational methods
or models have been proposed for identifying protein function and interactions [8, 10-13]. Chen et al. summarized briefly these
computational methods or models especially clustering method for proteins function and interaction prediction.
The subcellular localization of proteins is closely associated with its functions. Thus, the knowledge about protein
subcellular localization is fundamental to predict its functions and interactions. Yao et al. presented a support vector machinebased
method for predicting protein subcellular localization. Such method derived evolutionary features from PSI-BLAST
profile and used principal component analysis to extract key information used to recognize subcellular localization in the SVD
classifier. Proteins may dynamically be located in multiple sites. Few methods have deal with this situation and most
approaches have predicted only single site. Han et al. proposed a multi-label radial basis function-based classifier for predicting
protein multiple subcellular sites.
Enzyme is an important type of biocatalyst which is closely associated with metabolic activities. Identification of enzyme
classes may help infer in which chemical reaction it was involved. Cui et al. presented a SVD-based method to predicting
Enzyme Commission number which was an enzyme-related classification scheme built by the Nomenclature Committee of the
International Union of biochemistry and molecular biology .
Gene is the carrier of genetic information, and is the final regulator of all the phenotype even if some other factors also
participate in forming of phenotype. Thus, identifying disease-related genes is very critical to disease prevention and treatment.
Chen et al. proposed a gene selection method based on sparse representation and minimum-redundancy maximum-relevancy of
maximum compatibility center which was applicable to tumor classification. Zhang et al. developed a protein-protein networkbased
method for identifying genes of breast cancer, a most common disease in women. Zhang et al. proposed a Network
Motifs Slicing Feedback-based method for pathogenic genes selection of genetic disease. Li et al. proposed a decision tree and
mutual entropy-based method for predicting two-locus epistasis of complex disease.
Parameter optimization in most Computational models or methods is a challenging question for a long time whether now or
in the future. Remli et al. used the hybridization of quasi opposition-based learning in enhanced scatter search to improve
parameter estimation of kinetic models.
Molecular chaperones play several important roles for protein and nucleic acid
homeostasis. They act in protein folding and maturation, protein translocation through
membranes, protein disaggregation and targeting for degradation, among others. Over nucleic
acid metabolism, they interact with critical machinery for telomerase DNA maintenance and
RNA biogenesis. This special issue focus on the basic structure-function relationship of four
chaperones families: Hsp70, Hsp90, DnaJ-proteins/Hsp40 and Hsp100. Hsp70 acts as a pivot in
the protein quality control system handling client proteins with other chaperone families. It is a
dynamic protein that assumes an ensemble of protein conformations in response to a reciprocal
allosteric mechanism and is subjected to the action of a set of co-chaperones. DnaJproteins/Hsp40
act as Hsp70 co-chaperone stimulating its ATPase activity but also has intrinsic
chaperone activity. Besides, DnaJ-proteins/Hsp40 together with Hsp70 form a system capable of
recovering proteins from aggregates in metazoan cells. Interestingly, in non-metazoan cells, this
function is played by Hsp100 chaperone. Actually, classical Hsp100 are absent in metazoan
which make them potential target for inhibition in human pathogenic organisms. Hsp90 is a
chaperone that act on the maturation of a plethora of client proteins including corticoid receptors
and kinase proteins. Hsp90 has a mechanochemical mechanism, which is driven by ATP binding,
hydrolysis and ADP releasing and is also under control of several co-chaperones and posttranslational
modifications. Nevertheless, Hsp90 and co-chaperones participate of protein
complexes related to nucleic acid metabolism like telomerase maintenance and RNA maturation.
Some newer and specialized aspects of these chaperones will be also reviewed taking into
account: i) mitochondria chaperones and co-chaperones; ii) Hsp90 participation on cell wall
integrity pathway signaling in pathogenic fungi; iii) chaperones involved on the protein/DNA
secretion in bacteria and iv) involvement of Hsp70 and Hsp90 in protein targeting for
In recent years, computational methods have been employed extensively in bioinformatics and
medicine researches, including protein function prediction, gene-disease relationship and cancer
genomics. In particular, predictions or classifications are required to analyze or screen the
genomics data, such as oncogenes, protein sequences, micro-array and GWAS data for all sorts of
purposes. In term of computational methods, data mining and network analytics are essential in
the analysis of genomics and medicine data.
Data mining and network analytics techniques have advanced quickly over the past few years.
Several novel methods were reported in the top journals and conferences. For example, affinity
propagation was published in Science as a novel clustering algorithm, and deep learning has
become a hot topic in the predictions and classifications which is capable of processing big data.
Parallel mechanisms, such as Spark and Mahout, are also developed by the scholar and industry
researchers to speed up the algorithms. Computer scientists devote themselves to the advanced
large scale data mining and graph analytics techniques. However, the applications on genomics
and medicine are limited and fall behind the techniques.
This special issue will target the recent large-scale data mining and network analytics
techniques in bioinformatics and medicine applications. We especially welcome novel
classification and clustering algorithms and integrative network modeling approaches, such as
strategies for large and imbalanced learning, strategies for learning with multiple views,
strategies for various semi-supervised learning, strategies for multiple kernels learning,
integrative network analysis of multi-scale data, random walk and shortest path analysis on
heterogeneous network, etc. Applications on medical and biological large-scale data are strongly
encouraged. We also encourage authors to make their codes and experimental data available to
the public, making our special issue more attractive.
The editors expect to collect a set of recent advances in the related topics, to provide a
platform for researchers to exchange their innovative ideas and genomics data.
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