ISSN (Print): 1574-8936
ISSN (Online): 2212-392X
Volume 13, 6 Issues, 2018
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ISSN (Print): 1574-8936
ISSN (Online): 2212-392X
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Quan ZouSchool of Computer Science and TechnologyTianjin UniversityTianjinChina
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Special Issue Submission
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)
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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