Survey of Natural Language Processing Techniques in Bioinformatics <s> Introduction <s> The flooding of low-quality user generated contents (UGC) in online social network (OSN) has been a threat to web knowledge management systems. Recently several domain-specific systems have been developed addressing this problem, for example, predict correct answer in QA community; recognise reliable comment in products review forums etc. Major drawback of most research efforts is the lack of a general framework applicable to all OSNs. In this study, the authors start by analysing the effects of distinguishing features on UGC quality in different types of OSNs. Extensive statistical analysis leads to the discovery of existence of diverse patterns of human information sharing activity in dissimilar OSNs. This discovery is employed as prior knowledge in the classification framework, which decompose the original highly imbalanced problem into several balanced sub-problems. Ensemble classifiers are adopted in samples from clusters generated by incompact features. Experiments show the proposed framework is both effective and efficient for several OSNs.Contributions of this study are two-fold: (i) model posting activity in different types of OSNs; (ii) propose novel classification framework to identify UGC quality. <s> BIB001 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Introduction <s> The microblog has become a new global hot spot. Information retrieval IR technologies are necessary for accessing the massive amounts of valuable user-generated contents in the microblog sphere. The challenge in searching relevant microblogs is that they are usually very short with sparse vocabulary and may fail to match queries. Pseudo-relevance feedback PRF via query-expansion has been proven in previous studies to successfully increase the number of matches in IR. However, a critical problem of PRF is that the pseudo-relevant feedback may not be truly relevant, and thus may introduce noise to query expansion. In this paper, we exploit the dynamic nature of microblogs to address this problem. We first present a novel dynamic PRF technique, which is capable of expanding queries with truly relevant keywords by extracting representative terms based on the query's temporal profile. Next we present query expansion from external knowledge sources based on negative and positive feedbacks. We further consider that the choice of PRF strategy is query-dependent. A two-level microblog search framework is presented. At the high level, a temporal profile is constructed and categorized for each query; at the low level, hybrid PRF query expansion combining dynamic and external PRF is adopted based on the query category. Experiments on a real data set demonstrate that the proposed method significantly increases the performance of microblog searching, compared with several traditional retrieval models, various query expansion methods and state-of-art recency-based models for microblog searching. <s> BIB002 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Introduction <s> Dear Editor ::: ::: Recently, substantial studies have begun to explore the functional diversity and mechanistic roles of ncRNAs in mammals.1 Now, it has become increasingly apparent that ncRNAs are involved in multiple major biological processes, such as developmental timing, fat metabolism and cell death.2 Furthermore, the epigenetic and genetic defects in ncRNAs and their processing machinery have been implicated in the etiology of many forms of diseases.3 Several databases that documented the relevance of the microRNAs(miRNAs) to diseases have been constructed and provided useful results.4, 5 However, miRNAs are just the tip of the iceberg, other ncRNAs such as long non-coding RNAs (lncRNAs), PIWI-interacting RNAs (piRNAs) and small nucleolar RNAs (snoRNAs) have also been demonstrated to contribute to diseases.3, 6 Accumulated evidence suggest the diverse non-coding RNAs (ncRNAs) involved in a wide variety of diseases progression.3, 6, 7 It is a key challenge for understanding the precise behavior of diverse ncRNAs in mammalian diseases and deciphering the cross-regulations among disease-associated ncRNAs. Because there was no repository focused on diverse ncRNA-disease relationships in mammals, we have developed a manually curated diverse ncRNA-disease repository (MNDR, www.rna-society.org/mndr/) by integrating evidence in three mammals. Totally, 807 lncRNA-associated, 229 miRNA-associated, 13 piRNA-associated and 100 snoRNA-associated entries for 1149 curated entries were documented for three mammals (866 Homo sapiens-associated, 251 Mus musculus-associated and 32 Rattus norvegicus-associated entries) (Table 1). ::: ::: ::: ::: Table 1 ::: ::: The statistics of the ncRNA-disease entries in MNDR database ::: ::: ::: ::: Recent investigations indicated there are complex regulations among diverse ncRNAs and protein-coding genes. Such as, PTEN gene and the PTEN pseudogenes (ptenp1, one of lncRNAs) share a high degree of sequence homology, changes in ptenp1 expression levels indirectly affect PTEN expression by sequestering PTEN-targeting miRNAs.8 Thus understanding the mutual regulating pattern among diverse ncRNAs and protein-coding genes, particularly in disease conditions, is a key challenge. Thus, MNDR is not only a knowledge depository but providing us a good opportunity to view the ncRNA-mediated disease network globally (in visualization page: www.rna-society.org/mndr/visualization.html). Diverse ncRNAs and interaction genes were represented as nodes and the regulations were denoted as edges. Based on such a simplified ncRNA-mediated disease network, interesting observations have been achieved. The result showed that snoRNA htr, as a hub node, has intensively linked to 21 interaction genes in the network. More important, through BCL2, BCL2L1 and BAX, the snoRNA htr can communicate with the lncRNA malat1 (Supplementary Figure 1). Another example is snoRNA htr and lncRNA h19 are linked by E2F1 and MYC. When combined with human disease-associated miRNA evidence from mir2disease database, lncRNAs, miRNAs and snoRNAs, together with their interaction/target genes, can be integrated into bigger expanding ncRNA-mediated disease network. The biggest sub-network has 129 nodes and 149 edges, involving 33 lncRNAs, 1 snoRNA, 19 miRNAs and 76 interaction protein-coding genes(Supplementary Figure 2). In this network, more regulations among diverse ncRNAs directly or indirectly via intermediate genes, lncRNA dgcr5, har1a and har1b, were connected with hsa-mir-21 via intermediate gene REST. Interestingly, hsa-mir-21 and snoRNA htr were linked by key anti-apoptosis gene BCL2. Similar results were observed that lncRNA dgcr5, har1a and har1b can also communicate with snoRNA htr through alternative route NFKB1-hsa-mir-9-REST. Hence, according to current data, the two pivot protein-coding genes (BCL2 and NFKB1) and several ncRNAs (lncRNA malat1, snoRNA htr and miRNA hsa-mir-21, has-mir-9) collectively play an important role in the ncRNA-mediated disease network (Supplementary Figure 2). Importantly, the crosstalk between lncRNA malat1 and miRNA hsa-mir-21 can be found conserved in mouse ncRNA-mediated disease network. Above observations indicated diverse ncRNAs could communicate with each other in disease state through some disease-associated genes in mammals, highlighting the complexity, conservative and plasticity of the regulatory relationships between diverse ncRNAs and protein-coding genes in diseases. <s> BIB003 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Introduction <s> In recent years, substantial research interests have arisen to decipher miRNA function in cell death modules such as apoptosis and autophagy. Despite enormous success, it is increasingly evident that systems biology approaches that analyze the intra- and inter-modular connectivity are essential to uncover the generic organizing principles of the miRNA-mediated cell death network. We therefore constructed the miRDeathDB database to archive experimentally confirmed programmed cell death (PCD)-associated miRNAs together with their target proteins. Based on global analysis of the miRNA-mediated cell death network, we find there are two classes of miRNAs important to the cell death output. One class tends to have more regulatory links to proteins within death modules, whereas the other one lies on the boundary between modules, and tends to connect with more protein pairs across the modules. These findings indicate that miRNAs provide a novel communication mechanism between PCD-related proteins involved in both intra- and inter-cell death modules. Furthermore, miRDeathDB can also facilitate researcher access to a variety of resources centered on PCD-associated miRNAs. <s> BIB004 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Introduction <s> Transcriptomic analyses have revealed an unexpected complexity in the eukaryote transcriptome, which includes not only protein-coding transcripts but also an expanding catalog of noncoding RNAs (ncRNAs). Diverse coding and noncoding RNAs (ncRNAs) perform functions through interaction with each other in various cellular processes. In this project, we have developed RAID (http://www.rna-society.org/raid), an RNA-associated (RNA–RNA/RNA–protein) interaction database. RAID intends to provide the scientific community with all-in-one resources for efficient browsing and extraction of the RNA-associated interactions in human. This version of RAID contains more than 6100 RNA-associated interactions obtained by manually reviewing more than 2100 published papers, including 4493 RNA–RNA interactions and 1619 RNA–protein interactions. Each entry contains detailed information on an RNA-associated interaction, including RAID ID, RNA/protein symbol, RNA/protein categories, validated method, expressing tissue, literature references (Pubmed IDs), and detailed functional description. Users can query, browse, analyze, and manipulate RNA-associated (RNA–RNA/RNA–protein) interaction. RAID provides a comprehensive resource of human RNA-associated (RNA–RNA/RNA–protein) interaction network. Furthermore, this resource will help in uncovering the generic organizing principles of cellular function network. <s> BIB005 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Introduction <s> Synthetic biologists have developed DNA/molecular modules that perform genetic logic operations in living cells to track key moments in a cell's life or change the fate of a cell. Increasing evidence has also revealed that diverse genetic logic gates capable of generating a Boolean function play critically important roles in synthetic biology. Basic genetic logic gates have been designed to combine biological science with digital logic. SynBioLGDB (http://bioinformatics.ac.cn/synbiolgdb/) aims to provide the synthetic biology community with a useful resource for efficient browsing and visualization of genetic logic gates. The current version of SynBioLGDB documents more than 189 genetic logic gates with experimental evidence involving 80 AND gates and 16 NOR gates, etc. in three species (Human, Escherichia coli and Bacillus clausii). SynBioLGDB provides a user-friendly interface through which conveniently to query and browse detailed information about these genetic logic gates. SynBioLGDB will enable more comprehensive understanding of the connection of genetic logic gates to execute complex cellular functions in living cells. <s> BIB006 </s> Survey of Natural Language Processing Techniques in Bioinformatics <s> Introduction <s> MicroRNAs constitute an important class of noncoding, single-stranded, ~22 nucleotide long RNA molecules encoded by endogenous genes. They play an important role in regulating gene transcription and the regulation of normal development. MicroRNAs can be associated with disease; however, only a few microRNA-disease associations have been confirmed by traditional experimental approaches. We introduce two methods to predict microRNA-disease association. The first method, KATZ, focuses on integrating the social network analysis method with machine learning and is based on networks derived from known microRNA-disease associations, disease-disease associations, and microRNA-microRNA associations. The other method, CATAPULT, is a supervised machine learning method. We applied the two methods to 242 known microRNA-disease associations and evaluated their performance using leave-one-out cross-validation and 3-fold cross-validation. Experiments proved that our methods outperformed the state-of-the-art methods. <s> BIB007
Text mining and natural language processing refer to comprehending and analyzing natural language by using computer algorithms and programs. It is an important research direction in the application field of artificial intelligence. Research on natural language processing and text mining has been reported as early as the emergence of computers. With continuous and extensive research on machine learning and data mining algorithms, existing text mining technologies have achieved good results in automatic abstraction, automatic question answering, web relational network analysis, and anaphora resolution BIB001 BIB002 . Bioinformatics is an interdiscipline that emerged with the progress and accomplishment of the Human Genome Project. It predicts and solves live science problems related to genetics by using computer and statistical informatics. Data storage, retrieval, and analysis are the key processes in bioinformatics  BIB006 BIB003 BIB005 BIB004 . The National Center for Biotechnology Information established various databases for biological data, including sequence databases for storing DNA and protein data (e.g., dbEST and dbSNP) [8,  , Online Mendelian Inheritance in Man database for storing disease data, Gene Expression Omnibus database for storing gene chip data, and PubMed database for storing biological and medical literature BIB007 . Text mining and natural language processing techniques are necessary to retrieve user preference knowledge from expanding databases. Therefore, researchers retrieve papers on certain topics of interest, such as determining proteinprotein interactions, from PubMed using computer algorithms and programs. With the cracking of genetic codes, researchers have determined that biological sequences, particularly protein sequences, are similar to human language in terms of composition. In addition to using text mining to retrieve bioinformatics articles directly, an increasing number of researchers are regarding protein sequences as a special "text" and analyzing them based on existing text mining technologies. The relationship between bioinformatics and natural language processing is shown in Figure 1 . Researchers have also predicted the structures and functions of proteins. Based on these two aspects, we summarize the text mining technologies used in bioinformatics research. We aim to present these technologies to more bioinformatics researchers and hope that the number of researchers who can use good text mining technologies in bioinformatics studies will increase. 