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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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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NEW TRENDS IN BIOINFORMATICS AND BIOMEDICAL ENGINEERING (SELECTED ARTICLES FROM IWBBIO 2013)
Guest Editor(s): Ignacio Rojas, Francisco M. Ortuño
Tentative Publication Date: March-April, 2015
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Application of Computional Intelligence and Mathematics methods in Bioinformatics and Biomedicine
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34 Articles Ahead of Print are available electronically
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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