ISSN (Print): 1574-8936
ISSN (Online): 2212-392X
Volume 14, 8 Issues, 2019
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ISSN (Print): 1574-8936
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In-Depth Exploration of miRNA: A New Approach to Study miRNA at the miRNA/isomiR Levels, 2014 : 9 5; 522- 530
Li Guo , Hui Zhang, Yang Zhao, Sheng Yang and Feng Chen
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Plant Bioinformatics: from genome to phenome
Guest Editor(s): Ming Chen, Ziding Zhang, Youhuang Bai
Tentative Publication Date: March-April, 2015
Application of Novel Computational Methods in Molecular Biology, Biomedicine and Biopharmacy
Current Bioinformatics, Volume 11, Number 1
Guest Editor(s): Yudong Cai
I am very much satisfied with the review process and the communication made by the team for my manuscript. I have struggled a lot to prepare the pictures as per the requirements. But your team has patiently given feedback about the corrections and finally agreed to approve. Really, i am very much thankful to that.
Dr. R. R. Rajalaxmia (Department of CSE, Kongu Engineering College, Perundurai, Erode, TamilNadu, India)
24 Abstract Ahead of Print are available electronically
24 Articles Ahead of Print are available electronically
In recent years, the use of computational tools and models in conjunction with medical imaging techniques has been
effectively employed for analyzing various health conditions. The entropy is a mathematical tool for quantifying the disorder or
randomness associated with a system. The entropy can also be interpreted as the degree of uncertainty and hence is a measure
of the information content of a signal [1, 2]. The concept of entropy is derived from the second law of thermodynamics and its
applications have branched into several areas of science and technology such as signal and image processing and mathematical
Recently, various entropy measures such as Tsallis entropy, Renyi entropy, etc. have been successfully utilized for the
development of medical diagnostic systems and decision support systems [6, 7]. In the field of image analysis, entropy can
provide a good level of information to describe a given image . Entropy measures are highly useful for segmentation, feature
extraction and texture characterization of images . Entropy measures have proved to be highly useful for the segmentation of
medical images and extraction of valuable features from biomedical signals and images for the development of systems for
classification of normal and abnormal cases and also for the analysis of severity of diseases [10, 11].
The Current Bioinformatics Journal offers an excellent forum for dissemination and understanding of interdisciplinary
knowledge between the biomedical researchers and the healthcare industry. This special issue of current Bioinformatics entitled
“Entropy measures for medical image analysis” unites the authors’ effort to bring recent technological discussion on the
applications of entropy measures in the field of medical image processing. This issue comprises three excellent contributions
from varied geographical locations around the globe and presents the applications of entropy in diverse medical images such as
Microscopic Images, echocardiography ultrasound images and Magnetic Resonance Images (MRI), obtained from various
Liver fibrosis is the formation of scar tissue in dysfunctional liver and is a major health problem in recent years. One of the
major causes of liver fibrosis is alcoholism, however several factors such as infections, hereditary causes etc. may also lead to
fibrosis of liver. The first paper of this special issue, authored by Yu Wang et al. employs entropy-based features extracted
from microscopic images of mice liver for analysis of fibrosis of liver.
The second paper in this special issue, authored by Luminita Moraru et al. addresses another important problem dealing
with the age-related heart muscle damage. The authors have utilized fuzzy c-means classification algorithm along with texturebased
image features such as entropy and homogeneity for classification of healthy subjects and patients with myocardial
In the final paper of this special issue, the authors Suresh Chandra Satapathy et al. have presented a semi-automated
examination procedure for inspecting the severity of stroke lesion using MRI images. Also, the authors have employed a novel
and new heuristic optimization algorithm namely Social Group Optimization (SGO) algorithm and a thresholding procedure
based on Shannon’s entropy, Kapur’s entropy and Otsu’s function for preprocessing the adopted images.
This special issue clearly magnifies the applicability and significance of entropy measures for the analysis of medical
images. We are indebted to the Editor-in-Chief, Prof. Dr. Yi-Ping Phoebe Chen for her valuable support. We thank Ms. Nida
Hatif, Assistant Manager Publications, Bentham Science Publishers for her assistance and support. The editors would like to
thank the reviewers of this special issue, for their excellent reviews and comments.
“Prediction”, which is the main task in machine learning research, has now become popular in biomedicine and
bioinformatics recently, since Obama proposed the precision medicine project. Precision medicine would combine biomedicine
and bioinformatics together, and employ advanced machine learning techniques, especially big data analysis approaches.
Traditional medical big data would be calculated with different statistics testing methods, which are difficult to deal with multimodal
complex data and the features relationship.
Machine learning was considered as a “black box” in the past years. However, it works better than the simple statistic
testing methods. In recent years, deep learning and big data machine learning techniques developed fast and showed amazing
performance in internet application, understanding images, and auto speech recognition. We can conclude that precision
medicine would be the next novel application for the big data machine learning. However, we found that traditional classifiers,
such as SVM, random forest, still dominate in the recent biomedicine and bioinformatics works. Therefore, we plan to organize
the special issue to bridge the latest methods and the biological applications.
In this thematic issue, eight outstanding reviews have been presented from different countries and regions, including P.R.
China, USA, and Germany.
Jing et al. conducted a systematic review of protein inter-residue contacts prediction methods. As a low-dimensional
representation of protein tertiary structure, protein inter-residue contacts could help de novo protein structure prediction
methods to reduce the conformational search space. Various methods have been developed for prediction of protein interresidue
contacts over the past two decades, and those methods could be roughly classified into five categories: correlated
mutations methods, machine-learning methods, fusion methods, template-based methods and 3D model-based methods. In this
article, they reviewed three main methods for prediction of protein inter-residue contacts and analyzed constraints of each
category. They also compared several representative methods and discussed performances of these methods in detail .
Zhang et al. performed a comprehensive review of the recent developments of feature extraction methods for protein
sequences, including composition-based features, autocorrelation-based features, and profile-based features. Their concepts and
the main equations were introduced and discussed. Finally, some existing computational tools of generating these features were
introduced. This article is especially useful for the researchers who are interested in computationally studying the structures and
functions of proteins .
Liu et al. analyzed the role of bioinformatics in modernization of Traditional Chinese Medicine. Traditional Chinese
Medicine (TCM) has a wide range of medicine practices, which was developed in China and other East Asian countries for
over 2000 years. The aim of modernization of TCM is to utilize modern biological and medicinal knowledge to research the
treatment mechanism of herbal medicine. Combined with rich practice experience of TCM, it can efficiently accumulate
development of modern drug. During this process, the bioinformatics as a rising subject can aid to discover and analyze TCM.
Currently, bioinformatics has played an important role to understand biological data. In this article, they try to introduce and
discuss the applications of bioinformatics techniques in TCM for interested readers and look ahead the potential development
of bioinformatics in TCM .
Lei et al. conducted a survey on the prediction of essential proteins and genes, which discusses not only the current
developments of diverse computational methods, but also the challenge for future work. It is well known that the prediction of
essential proteins using computational methods is quite significant in the field of bioinformatics, such as the disease research
and drug design. The authors introduced the recent studies for predicting essential proteins and genes by analyzing different
kinds of computational methods including topology-based methods, integrated methods, machine learning methods and reliable
network methods. In addition, they showed the evaluation techniques of predictive performance and some available databases
of essential genes, as well as the future research directions. This review presents the systematic knowledge of the essential
proteins and genes prediction which can help the studies in the related fields .
Jia and Gong summarized the computational methods in linear B-cell epitope prediction. The determination of B-cell
epitopes is thus important for the synthesis of peptide vaccines in the preparation of diagnostic reagents, and the screening of
monoclonal antibodies. For identifying such epitopes, many computational predictors have been proposed in the past 10 years.
In this review, we summarized the representative computational approaches developed for the identification of linear B-cell
epitopes. They mainly discussed the datasets, feature extraction methods and classification methods used in the previous work.
Moreover, they also consider existing challenges and future perspectives for developing reliable methods for predicting linear
B-cell epitopes .
Yang et al. summarized the application of machine learning methods in protein sub-Golgi localization. The distribution of
proteins in cell correlates with their functions. Due to its important roles in protein storage, package and distribution, the
identification of protein sub-Golgi localization is very important for the in-depth understanding of proteins function. Because
transitional biochemical experiments are time-consuming and require expensive materials, machine learning method is used to
identify the protein location in Golgi apparatus. In the review, Yang et al. briefly introduced the recent progresses of protein
sub-Golgi apparatus localization prediction from the database construction, feature extraction and optimization, prediction
algorithms and webserver establishment. The advantages and disadvantages of these methods were discussed. They also
provided the prospect of protein sub-Golgi localization prediction using machine learning methods .
Zhang et al. analyzed the cross-kingdom regulation of exogenous plant miRNAs in humans. As a new signaling molecule,
exogenous miRNAs regulate and influence the physiological functions of inhalers, revealing the effects on human health and
metabolism at the gene molecular level. During the process, the combination of bioinformatics and computer methods can help
find and analyze the cross-kingdom regulation of exogenous plant miRNA in the human body. In this article, they made attempt
to introduce and discuss recent advances of exogenous plant miRNAs into humans and their cross-kingdom regulation and look
ahead the potential development of cross-kingdom regulation of exogenous plant miRNAs .
Qu et al. analyzed the methods of prediction the DNA-binding protein. DNA-binding protein is an important component of
prokaryotic and eukaryotic proteomes, which can participate in many life activities. Due to the importance of DNA-binding
protein, there are many researches on prediction DNA-binding, and many methods are mentioned. In the earlier years, the
biochemical methods are often used for identification of these proteins, but with the continuous development of machine
learning, many machine-learning methods are used to predict DNA-binding proteins. In this article, they analyzed the feature
representation methods and common classifiers, summarized the evaluation methods and existing websites, and compared the
results of different methods .
Each paper in this special issue was extensively peer-reviewed by more than two external reviewers. I would like to thank
all the authors for contributing their work to our hot thematic issue and all the reviewers for their time and efforts.
The success of Bioinformatics in recent years has been prompted by research in Molecular Biology and Molecular Medicine
in several initiatives. These initiatives gave rise to an exponential increase in the volume and diversification of data, including
next generation sequencing data and their annotations, high-throughput experimental (omics) data, biomedical literature, among
many others. Systems Biology is a related research area that has been replacing the reductionist” view that dominated Biology
research in the last decades, requiring the coordinated efforts of biological researchers with those related to data analysis,
mathematical modeling, computer simulation and optimization.
The accumulation and exploitation of large-scale databases prompt the development of new computational technology and
research on these issues. In this context, many widely successful computational models and tools used by biologists in these
initiatives, such as clustering and classification methods for omics data, are based on Computer Science/ Artificial Intelligence
(CS/AI) techniques. In fact, these methods have been helping in tasks related to knowledge discovery, modeling and
optimization tasks, aiming at the development of computational models so that the response of biological complex systems to
any perturbation can be predicted.
In this context, the interaction of researchers from different scientific fields is, more than ever, of foremost importance,
boosting the research efforts in the field and contributing to the education of a new generation of bioinformatics scientists. The
Practical Applications in Computational Biology and Bioinformatics (PACBB) conference has been contributing to this effort,
promoting this fruitful interaction over the last 7 years. This special issue gathers four contributions, selected and significantly
extended from the rich PACBB'15 technical program, which included papers spanning many different sub-fields in
bioinformatics and computational biology.
This volume gathers four extended articles selected from the work presented at the PACBB’2015 conference, showing
distinct and meaningful practical applications of bioinformatics and computational biology. These range from text mining, to
next generation sequencing and gene expression data applications.
Calderon-Mantilla et al. propose a pipeline architecture for inferring and visualizing gene networks from expression data,
applied to the specific case of coffee plants . Rodriguez-Gonzalez et al. present a comparison of two distinct text mining
approaches for extracting diagnostic related knowledge from MedLine Plus articles . The work by Graña et al. proposes
nextpresso, a pipeline for the analysis of next generation sequencing (RNA-seq) data that covers the most common
requirements of these experimental data . Finally, the work by Fernandez-Gonzalez et al. addresses a relevant problem in
text retrieval, namely the influence of class imbalance when developing classifier models for assessing relevant biomedical
The major feature of our current life sciences, is the rapid increase of biological data, which are presented in many forms,
and reflect the characteristics of biological systems at various levels, including genome, transcriptome, epigenome, proteome
and metabolome etc. This is the so-called biological big data we are facing. Biological big data bring both challenges and
opportunities to bioinformatics. Tools and techniques for analyzing big biological data enable us to translate massive amount of
information into a better understanding of the basic biomedical mechanisms, which can further be applied to translational or
This thematic issue with a theme of “Bioinformatics in Biological Big Data Era” aims at extensively showing the latest
development and achievements in Bioinformatics in this biological big data era. The ten papers in this thematic issue were
selected from the 1st CCF Bioinformatics Conference (CBC 2016), which was sponsored by China Computer Federation (CCF).
The selected papers cover methods and algorithms for processing biological big data in addressing various bioinformatics
issues. Before submitted to the special issue, these papers have gone through strict reviewing organized by CBC 2016. Further
reviewing was organized by the guest editors.
In what follows, we give a brief review of the 10 papers included in this thematic issue.
Liu et al. in their paper “Sparse linear modeling kinase inhibition network for predicting combinatorial drug sensitivity in
cancer cells” used a sparse linear model called uncertain group sparse representation (UGSR) to infer essential kinases
governing the cellular responses to drug treatments, based on the massively collected drug-kinase interactions and drug
sensitivity datasets over hundreds of cancer cell lines .
In the paper “TagNovo: A dictionary based approach to predict peptide theroy spectra” Wang et al. presented a new
theoretical spectrum prediction model called TagNovo, which builds a “tag dictionary" from exiting spectrum library and is
used for theory spectrum prediction .
In the paper “Large-scale Investigation of Long Noncoding RNA Secondary Structures in Human and Mouse” by Guo et al.
the authors conducted a large-scale investigation of lncRNA secondary structures especially for hairpin structural motif in
human and mouse based on computational prediction using the RNAfold software, and found that the secondary structures of
lncRNAs have many characteristics, most of which are similar with those in mRNAs .
Nie et al. in their paper “Prediction of protein S-Sulfenylation sites using a deep belief network” developed a computational
method DBN-Sulf to effectively predict S-sulfenylation sites by using optimally extracted properties based on Deep Belief
Network (DBN) with Restricted Boltzmann Machines (RBMs). DBN-Suf shows significantly better performance than the
existing methods .
In the paper “Feature identification for phenotypic classification based on genes and gene pairs”, Su, Zhang and Pan
proposed a new algorithm called FSGGP to select both feature genes and feature gene pairs on the binary-value gene
expression data .
Chan et al. in their paper “MyPhi: Efficient Levenshtein Distance Computation on Xeon Phi based Architectures”
introduced MyPhi, an ultra-fast implementation of the Myers algorithm on Intel Xeon Phi based architectures for efficiently
computing Levenshtein Distance between genome sequences .
The paper “A Metric on the Space of Rooted Phylogenetic Trees” by Wang and Guo proposed a new metric on the space of
rooted phylogenetic trees, which can be calculated in polynomial time with the size of the compared trees .
Liao et al. developed a method to classify Small GTPases and non-small GTPases in their paper “Classification of small
GTPases with hybrid protein features and advanced machine learning techniques” . In the paper “Identification of Attention
Deficit/Hyperactivity Disorder in Children Using Multiple ERP Features”, Li et al. used non-invasive event-related potential
(ERP) features for Attention deficit hyperactivity disorder (ADHD) prediction . The paper “Low Rank Representation and
its application in bioinformatics” by You, Cai and Huang reviews the theoretical and numerical models based on low rank
representation and their applications in bioinformatics area .
This thematic issue is the result of many people’s contribution, support and cooperation. We appreciate the authors for
submitting their works to this thematic issue, and we thank the reviewers for their hard work in reviewing the papers. We also
thank the Editor in Chief and the staff of Current Bioinformatics for their valuable help to make this issue possible.
With the development of sequencing technologies, a wide variety of biological data including DNA, RNA and protein sequences and gene expression profiles were generated and accumulated. These data are an external manifestation of acting mechanism of the cell. How to discover cellular mechanism through these data is a vast challenge that current scientists are faced with. The computational approaches including bioinformatics and system biology have proved essential to analyze these complicated data, as Markowetz declared that all biology is computational biology . Recently a number of computational techniques and theories such as BLAST [2, 3] machine learning [4-7] and network theory [8-10], have facilitated the discovery of molecular structures and functions. Therefore, this thematic issue is intended to summarize recent progress of these computational techniques and theories in genomics and proteomics. Comparison between molecular sequences is very helpful to explore evolutionary relationship among different tissues, organisms or species and further to functional analysis. It is not an exaggeration to say that comparison between molecules is an important foundation of life exploration. Natural vector is a method of characterizing protein or DNA sequences and is applicable to classification and evolutionary analysis [11, 12]. Yu  reviewed the natural vectors method and application of it in the virus phylogenetic Classification.
Biological images are useful especially to phenotype quantification. High-throughput and quantitative biological phenotypes from images are increasingly becoming important to both the quantification of phenotypes and the visualization of biological molecular structure and activity. Bioimage informatics is becoming a new area of exploring life . Chen et al.  discussed the major studies based on biological images and summarized the computational techniques of biological image analysis.
Long noncoding RNAs (lncRNAs) are transcripts with more than 200 nucleotides, and belong to a type of non-protein coding RNA. LncRNAs have recently been discovered to perform a variety of functions . However, the identification of lncRNA is challenging . Yao et al.  reviewed the computational strategies of recognizing lncRNA and current progresses, and discussed existing difficulties in the prediction of the lncRNAs especially by using machine learning methods.
Biomedical data are commonly big data which require both high-performance computers and high-effective computational methods. Deep learning proposed by Hinton et al.  is becoming a dazzling research field and makes machine intelligence advance a big step. Peng et al.  reviewed the application of deep learning in the omics data processing, biological image processing and biomedical diagnosis and discussed challenges.
With the development of meteorological science, a large number of meteorology data, such as temperature, humidity, rainfall, air pressure, wind speed and so on, have been collected. So how to identify the key meteorological indexes causing an epidemic? How to integrate these key meteorological indexes to predict the epidemic? These become a vital task for us now. Liu et al.  reviewed two categories of model of prediction of the epidemics related to meteorological factors: deterministic models and stochastic models.
Single-nucleotide polymorphism known as SNP is referred to as the variation of a single nucleotide that occurs at a specific position in the genome. SNP was found to be associated with a wide range of disease or traits , such as inflammatory and autoimmune disorders , Alzheimer's disease  and breast cancer . The study of SNP-disease associations will facilitate the promise of precision medicine . Li  reviewed computational methods of identifying SNP-disease association and discussed improvement directions: data quality improvement, high-performance computing platform and advanced computational method.
Although most genes have been detected, little was known about its functions. Numerous computational methods have recently been proposed to find gene functions. Loh et al.  summarized these computational methods, compared them and analyzed their strength and weakness.
Protein post-translational modification (PTM) is a biochemical reaction which occurs after translation and before protein synthesis, covalently modified by different functional groups. The PTMs involved in every cellular process of life is a key regulating mechanism in the cell [29-32]. The first important step to explore PTMs is to identify PTM types and sites. Currently, the biochemical or biophysical experiments and the computational approaches are parallel to complement one another for identifying PTMs. The computational predictionsgenerally consisted of data collection, representation of PTMs (feature extraction), training the known samples and prediction new samples. Therefore, the feature extraction occupies a central position in the computational prediction of PTMs. Huang  reviewed the methods of feature extraction which have recently been developed for PTMs prediction and discussed several properties of it.
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