ISSN (Print): 1570-1646
ISSN (Online): 1875-6247
Volume 18, 5 Issues, 2021
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ISSN (Print): 1570-1646
ISSN (Online): 1875-6247
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Special Issue Submission
"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.
2 Abstract Ahead of Print are available electronically
16 Articles Ahead of Print are available electronically
In recent years, protein-related data have grown rapidly with the application of novel methods and techniques. Several
online public databases have been set up, and investigators can easily retrieve various data reported in them. Traditional
computational methods to deal with these data are becoming more and more inappropriate because they are in different forms.
Thus, novel data-driven computational methods are increasingly needed. This thematic issue collects six excellent papers, out
of which three papers reviewed newly proposed methods of essential problems in computational proteomics and three research
articles proposed novel computational methods to deal with specific problems.
Three reviews were collected in this thematic issue. Random walk is one of the important tools which has been developed
over the past one hundred years to analyze complicated systems, including molecular biological system. Zhang et al.
summarized eight popular models of random walks, including simple random walk, random walk with a start, PageRank,
limited random walk, local random walk, bi-random walk, repeated random walks, and signed random walk . They also
reviewed the applications of these models and their variants to some problems, such as node classification, node representation,
link prediction, and clustering or community detection in the biomedical network. Li et al. gave a review, aiming to
complement and extend the existing efforts through an in-depth study of machine learning approaches based on (mass
spectrometry) MS-based proteomics . Firstly, the authors introduced MS-based proteomics and briefly reviewed its
development. Then, the applications of five kinds of machine learning methods to mass spectrometry data analysis were
reviewed and organized from an algorithmic point of view. With the rapid growth of protein sequence data, computational
methods are essential for quick and accurate determination of the structural classes of proteins. Liu et al. introduced the recent
approaches of protein structural class prediction, focusing on the steps from information extraction to classification algorithm
In addition, three other research articles were also included in this thematic issue. Chen and Wei presented a DNA-binding
protein prediction method, called GCN-DBP , which used GCN to avoid the cumbersomeness of feature engineering. They
treated each protein sequence as a document and segmented the words according to the concept of k-mer so that they can divide
a sequence into some words. A single text graph was built for the corpus based on word co-occurrence and document word
relationships, and then protein sequence was learned by Graph Convolutional Network. An effective prediction model for
nitration PTM sites was built by Liu et al. . It adopted several machine learning methods, such as the FCBF feature ranking
method, over- and under-sampling technique, and a stacking model composed of multiple base classifiers. A better recall rate
can be achieved when the ratio of positive and negative samples was highly imbalanced. Liu et al. gave a novel model for the
identification of protein subcellular location , which used the protein features derived from one or more networks via
network embedding algorithms. The performance of such a model was quite competitive compared to some classic models
when a proper network embedding algorithm was selected.
These studies are helpful for understanding some problems in computational proteomics. It is hopeful that more and more
investigators can give their contributions in this area.
New molecules' discovery is still an important and challenging task. With the accumulation of data
in proteomics, researchers deal with several types of proteomics data, such as sequences, structures
and post-translational modifications. It is an important problem of how to effectively use this data in
the fields of biology and biomedicine. For some special protein molecules, it is time-consuming and
costly to detect new ones by traditional wet experimental technologies. Many researchers employ
computational methods to identify some candidates for these special proteins [1-4].
In the universe of proteins, enzymes are receptor molecules binding with a ligand compound,
exhibiting diverse biological activities, and also the structure and function of proteins are involved in
the cytoskeleton and molecular motors. Lots of computational approaches have been developed for
identifying protein and peptide molecules [5-9]. These machine learning approaches [10-17] typically extracted specific
features from protein sequences, secondary structures, 3D structures and physical-chemical properties, and then implemented
special protein molecules classification. Furthermore, several effective feature selection strategies are used to improve the
prediction accuracy. The novel methods presented in these studies would be new tools for tackling different problems in
proteomics and the new findings would provide new insights into biologists and medical scientists.
In this issue, all researchers are focused on studies relating to novel methods and golden benchmark datasets for special
protein molecules computational identification. Xu et al. constructed a sequence-based feature vector to represent each pair of
proteins, via Multivariate Mutual Information (MMI) and Normalized Moreau-Broto Autocorrelation (NMBAC), and feed
these features into an integrated learning model for judging interaction pairs and non-interaction pairs. Furthermore, this
integrated model embeds Random Forest in AdaBoost framework and turns weak classifiers into a single strong classifier. Yu
et al. produced a novel computational technique to promote ligand-receptor interactions research. They extract features from
ligand and receptor sequences by Histogram of Oriented Gradient (HOG) and Discrete Cosine Transform (DCT), and use the
Fuzzy C-Means (FCM) clustering algorithm to generate the same number of sub-classifiers, then choose the optimal subclassifier
for predicting ligand-receptor interactions according to the similarity from one sample to training subsets. Wu et al.
proposed a novel computational method to predict DNA-binding proteins by Multiple Kernel Support Vector Machine (MKSVM)
on sequence information. Their approach is comparable, even better than other methods on some data sets. Tang et al.
reviewed many computational methods that can predict DNA-binding proteins via different features and different classifier
models. They highlighted some advantages and disadvantages of various machine learning methods. They have given attention
to the analysis of protein sequence, structure and function, collect related data sets, obtain outstanding performance, and
develop user-friendly software tools or web servers.
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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