Call for Papers Special Issue on"Gene Regulatory Network"

The journal Genomics Proteomics & Bioinformatics (GPB) is now inviting submissions for a special issue (to be published around April 2013) on the topic of ’’Gene Regulatory Network’’. The Gene Regulatory Network (GRN) is a collection of DNA sequences which interact with each other and other cellular components, thereby governing the rates at which genes in the network are transcribed to change the genomic activity. GRNs are evolutionally-conserved and play critical roles in various biological processes and attract     

Special Issue on “Gene Regulatory Network”

The journal Genomics Proteomics & Bioinformatics (GPB) is now inviting submissions for a special issue (to be published around summer 2013) on the topic of "Gene Regulatory Network". The Gene Regulatory Network (GRN) is a collection of DNA sequences which interact with each other and other cellular components, thereby governing the     

Special Issue on “Gene Regulatory Network”

The journal Genomics Proteomics & Bioinformatics(GPB) is now inviting submissions for a special issue(to be published around April 2013) on the topic of "Gene Regulatory Network". The Gene Regulatory Network(GRN) is a collection of DNA sequences which interact with each other and     

Identifying cancer genes from cancer mutation profiles by cancer functions

It is of great importance to identify new cancer genes from the data of large scale genome screenings of gene mutations in cancers. Considering the alternations of some essential functions are indispensable for oncogenesis, we define them as cancer functions and select, as their approximations, a group of detailed functions in GO (Gene Ontology) highly enriched with known cancer genes. To evaluate the efficiency of using cancer functions as features to identify cancer genes, we define, in the screened genes, the known protein kinase cancer genes as gold standard positives and the other kinase genes as gold standard negatives. The results show that cancer associated functions are more efficient in identifying cancer genes than the selection pressure feature. Furthermore, combining cancer functions with the number of non-silent mutations can generate more reliable positive predictions. Finally, with precision 0.42, we suggest a list of 46 kinase genes as candidate cancer genes which are annotated to cancer functions and carry at least 3 non-silent mutations.     

Comparisons of graph-structure clustering methods for gene expression data

Although many numerical clustering algorithms have been applied to gene expression dataanalysis,the essential step is still biological interpretation by manual inspection.The correlation betweengenetic co-regulation and affiliation to a common biological process is what biologists expect.Here,weintroduce some clustering algorithms that are based on graph structure constituted by biological knowledge.After applying a widely used dataset,we compared the result clusters of two of these algorithms in terms ofthe homogeneity of clusters and coherence of annotation and matching ratio.The results show that theclusters of knowledge-guided analysis are the kernel parts of the clusters of Gene Ontology (GO)-Clustersoftware,which contains the genes that are most expression correlative and most consistent with biologicalfunctions.Moreover,knowledge-guided analysis seems much more applicable than GO-Cluster in a largerdataset.     

Special Issue on “Gene Regulatory Network”

The journal Genomics Proteomics & Bioinformatics (GPB) is now inviting submissions for a special issue (to be published around April 2013) on the topic of ’’Gene Regulatory Network’’.     

A new distributed feature selection technique for classifying gene expression data

Classification of gene expression data is a pivotal research area that plays a substantial role in diagnosis and prediction of diseases. Generally, feature selection is one of the extensively used techniques in data mining approaches, especially in classification. Gene expression data are usually composed of dozens of samples characterized by thousands of genes. This increases the dimensionality coupled with the existence of irrelevant and redundant features. Accordingly, the selection of informative genes (features) becomes difficult, which badly affects the gene classification accuracy. In this paper, we consider the feature selection for classifying gene expression microarray datasets. The goal is to detect the most possibly cancer-related genes in a distributed manner, which helps in effectively classifying the samples. Initially, the available huge amount of considered features are subdivided and distributed among several processors. Then, a new filter selection method based on a fuzzy inference system is applied to each subset of the dataset. Finally, all the resulted features are ranked, then a wrapper-based selection method is applied. Experimental results showed that our proposed feature selection technique performs better than other techniques since it produces lower time latency and improves classification performance.     

Conservation needs of amphibians in China: A review

The conservation status of all the amphibians in China is analyzed,and the country is shown to be a global priority for conservation in comparison to many other countries of the world.Three Chinese regions are particularly rich in amphibian diversity:Hengduan,Nanling,and Wuyi mountains.Sala-manders are more threatened than frogs and toads.Several smaller families show a high propensity to become seriously threatened:Bombinatoridae,Cryptobranchidae,Hynobiidae and Salamandridae.Like other parts of the world,stream-breeding,high-elevation forest amphibians have a much higher likeli-hood of being seriously threatened.Habitat loss,pollution,and over-harvesting are the most serious threats to Chinese amphibians.Over-harvesting is a less pervasive threat than habitat loss,but it is more likely to drive a species into rapid decline.Five conservation challenges are mentioned with recommendations for the highest priority research and conservation actions.     

Special Issue on "Gene Regulatory Network"

The journal Genomics Proteomics & Bioinformatics(GPB) is now inviting submissions for a special issue(to be published around April 2013) on the topic of "Gene Regulatory Network".     

Physical processes and their role on the spatial and temporal variability of the spring phytoplankton bloom in the central Yellow Sea

The spatial and temporal variability of the spring phytoplankton bloom (SPB) in the central Yellow Sea is studied, using SeaWiFS surface chlorophyll remote-sensing data, AVHRR sea surface temperatures (SST), QuikSCAT sea surface wind speed (SSW) from 1998 to 2009 and the cruise survey data in 2007 and 2009. The influences of the hydrological conditions on the SPB are significant. (1) The rising SST and low SSW in spring play an important role in the development of the SPB. The SPB in the central Yellow Sea occurs primarily in April (from April 3 to April 24) and at this period the mean SST is generally greater than 10 °C, and 24 h averaged SSW is less than 5.4 m/s. The 99% of the SPB occurs when the SST is 9–14 °C and SSW is 0–7.9 m/s. (2) Specifically, the development of the SPB is from April 4 to April 7 and from April 4 to April 22 in 2007 and 2009 respectively. The longer duration of the SPB in 2009 than that in 2007 is related to the high SST in 2009, which is approximately 2 °C greater than that in 2007, and the weak SSW in 2009, which is much lower than that in 2007, which further indicate that these two factors are critical to the duration of the SPB. (3) The horizontal distribution of surface chlorophyll a in 2007 is found to be greater and located more northward than that in 2009. Comparing the spatial coverage of the SPB, the path of the Yellow Sea warm current, which is warmer and salty, and the location of maximum SST reveals that the spatial coverage of the SPB locates in the warmer temperature (>9 °C) and higher salinity (>33) waters in March and this warm and salty water was much more evident in 2007 than that in 2009 because a stronger warm current of the Yellow Sea in winter. (4) The vertical depths of the maximum chlorophyll layer (MCL) appear at three levels: surface, 10 m and 30 m in 2007, whereas the MCL occurs only at a sub-surface level in 2009 which is related to vertical stability of the water column, i.e., the water is vertically mixed in 2007 while vertically stratified in 2009.     

蕲州地区的蕲艾、青蒿、黄花蒿与茵陈的考订

<正> 蕲州是我国中药材主要产地之一,是湖北省蕲春县的一个集镇,,是我国中医药大师李时珍的家乡。该地区生长的菊科蒿属植物Artemisia Linn。颇多,其中入药的有十余种,并在李时珍《本草纲目》中曾有记载。这里仅对该地区常见入药的蕲艾、青蒿、黄花蒿与茵陈结合《本草纲目》记载的材料作初步的考订。     

Chinchilla "big" and "little" gastrins

Gastrin heptadecapeptides (gastrins I and II which differ in the presence of sulfate on the tyrosine of the latter) have been purified and sequenced from several mammalian species including pig, dog, cat, sheep, cow, human and rat. A 34 amino acid precursor ("big" gastrin), generally accounting for only 5% of total gastrin immunoreactivity, has been purified and sequenced only from the pig, human, dog and goat. Recently we have demonstrated that guinea pig (GP) "little" gastrin is a hexadecapeptide due to a deletion of a glutamic acid in the region 6-9 from its NH2-terminus and that GP "big" gastrin is a 33 amino acid peptide. The chinchilla, like the GP, is a New World hystricomorph. This report describes the extraction and purification of "little" and "big" gastrins from 31 chinchilla antra. Chinchilla "little" gastrin is a hexadecapeptide with a sequence identical to that of the GP and its "big" gastrin is a 33 amino acid peptide with the following sequence: (See text)