article: MicroRNAs (miRNAs) regulate a wide range of cellular and developmental processes through gene expression suppression or mRNA degradation. Experimentally validated miRNA gene targets are often reported in the literature. In this paper, we describe miRTex, a text mining system that extracts miRNA-target relations, as well as miRNA-gene and gene-miRNA regulation relations. The system achieves good precision and recall when evaluated on a literature corpus of 150 abstracts with F-scores close to 0. 90 on the three different types of relations. We conducted full-scale text mining using miRTex to process all the Medline abstracts and all the full-length articles in the PubMed Central Open Access Subset. The results for all the Medline abstracts are stored in a database for interactive query and file download via the website at http: //proteininformationresource. org/mirtex. Using miRTex, we identified genes potentially regulated by miRNAs in Triple Negative Breast Cancer, as well as miRNA-gene relations that, in conjunction with kinase-substrate relations, regulate the response to abiotic stress in Arabidopsis thaliana. These two use cases demonstrate the usefulness of miRTex text mining in the analysis of miRNA-regulated biological processes.
  MicroRNAs (miRNAs) are a class of 21-25nt non-coding RNAs that negatively regulate gene expression via complementary pairing to mRNA. Since the first miRNA lin-4 was discovered in Caenorhabditis elegans in 1993 [1], thousands of miRNAs have been identified in multiple animal and plant species as well as viruses. miRNAs have been found to regulate a wide range of cellular and development processes through gene expression suppression or mRNA degradation [2]. Identification of miRNA target genes is thus crucial for understanding the role of miRNA in various biological processes. Potential miRNA targets can be identified by miRNA target prediction algorithms [3–6], with limited precision [6]. Therefore, experimental validation of the predicted targets is necessary. Experimental validation methods include gene-specific techniques, e. g. , reporter gene assays and assessment of miRNA and target mRNA co-expression [7], or high-throughput techniques. Validated miRNA targets are usually reported in the literature, and several databases have been built to store manually curated miRNA-target pairs, allowing researchers to access the latest results of experimentally validated miRNA targets. However, the amount of miRNA-related literature is increasing rapidly, which makes it difficult for researchers and curators to keep up to date. Fig 1 shows the trend in the numbers of papers published from the year 2001 to 2014 obtained by a PubMed search for the query ‘miRNA’ (performed in May 2015). Significant research effort [8–10] has been made to develop automatic methods to extract relations between biological entities (e. g. , gene and disease) from text. Three main kinds of methods have attracted the most attention: co-occurrence-based, rule-based and machine learning. In co-occurrence-based methods (e. g. , [11–13]), the co-occurrence of entities of interest in the same sentence or paragraph is considered to indicate a relation. Co-occurrence of other types of evidences can also be used to increase extraction confidence or enrich the relation, e. g. , species names, cell lines and/or experimental methods [11]. In rule-based methods, linguistics and/or biological knowledge is encoded in rules that can be implemented as an automatic program to extract relations from text. Such knowledge includes plain-text/syntactic patterns, specific words that indicate biological relations and filtering heuristics, and can be translated into deterministic steps to extract relations [14–17]. Machine learning methods view relation extraction as a classification problem and try to apply machine learning algorithms, e. g. , Support Vector Machines [18] or Naive Bayes classifier [19], to extract relations. In well-studied tasks, such as protein-protein interaction extraction [20,21], or Genia Event Extraction in BioNLP [9], machine learning methods have been widely applied. Even in these settings, there have been some successful rule-based systems, such as RLIMS-P [14] and BioSEM [17]. Co-occurrence-based systems are widely used especially in emerging tasks such as mutation-disease relation extraction [13]. We have used a rule-based approach to develop miRTex, since we have developed several rule-based relation extraction systems for molecular entity relations with good results [14–16]. Furthermore, we have constructed significant infrastructure that supports generalizable and scalable development of rule-based systems [16,22]. To the best of our knowledge, only two previous papers discussed how to extract miRNA-gene associated pairs from text [11,23]. miRSel [11] detects sentences that contain both a miRNA and a gene and classifies them into one of five categories of miRNA-gene associations: physical target, repression, co-expression, induction and cleavage. This classification is based on detecting presence of words (e. g. , “target” and “repress”) that indicate these types of associations anywhere in the sentence that also mentions a miRNA and a gene. An evaluation on a set of 50 abstracts containing 103 miRNA-gene associations showed a 0. 78 F-score with 0. 89 recall and 0. 70 precision for detecting miRNA-gene associations, and a 0. 73 F-score with 0. 87 recall and 0. 62 precision for classifying association categories. Another text mining system [23] uses co-occurrence-based methods similar to the one used in [11] and machine learning approaches to extract miRNA-gene associated pairs. The machine learning approaches use linear classification (LIBLINEAR [24]) and linear SVM (LIBSVM [25]) with lexical features and shortest-path features from the dependency tree. Compared to the co-occurrence-based approach, the machine learning approaches performed better and achieved a 0. 76 F-score with 0. 67 precision and 0. 87 recall on a test set of 100 abstracts with 123 miRNA-gene associated pairs. However, neither this system nor miRSel captures the direction of the extracted miRNA-gene relations, i. e. , whether the miRNA regulates the gene or the gene regulates the miRNA. In addition to these two systems that detect miRNA-gene associations from text, there are several databases containing miRNA-target information. Among them, TarBase [26], miRWalk [27] and miRTarBase [28,29] use text mining methods to assist in human curation. TarBase and miRWalk used a co-occurrence-based method that used gene names, miRNA terms and other information (e. g. , interaction terms, experimental methods, pathways) to rank abstracts in a triage step. miRTarBase [28] does not provide details of the text mining method used in the triage step. Although no formal evaluation of the triage task was conducted, Vlachos et al. [26] reported that the triage step in TarBase significantly increased the efficiency of curation. The database miRecords [30] also contains experimentally validated miRNA targets, but they did not report on the use of text mining for either extraction or triage. In addition, there are other miRNA databases that are not limited to experimentally validated miRNA targets. For example, miRDB [31] contains predicted miRNA targets, miRBase [32] contains miRNA sequence information and miR2Disease [33] focuses on miRNA-disease associations. Although text mining techniques have been used in previous works to extract miRNA-gene associations or identify potentially informative articles for curation, most of these systems used co-occurrence-based methods so, while a good performance on recall can be achieved, they generally suffer from low precision. In this paper, we present miRTex, a text mining system that extracts regulation relations between miRNA and gene. In contrast to previous systems, miRTex uses more elaborate processing based on lexico-syntactic information and several linguistic generalizations, which we believe contributes to higher precision than the co-occurrence-based methods (see Results and Discussion Section). In addition, miRTex can capture and distinguish between a “miRNA-gene regulation” relation (i. e. , the miRNA regulates the gene) and a “gene-miRNA regulation” relation (i. e. , the gene regulates the miRNA), a unique aspect of miRTex to our knowledge. Furthermore, in the cases of miRNA-gene regulation relations, miRTex also detects if the miRNA-gene regulation relation is direct, i. e. , the miRNA regulates the gene expression via direct binding to the gene’s mRNA. In this paper, when the miRNA-gene regulation is direct, we call this a “miRNA-target” relation. The remaining cases of miRNA-gene regulation correspond to cases where the regulation is indirect or when it is unclear from the text whether or not it is direct. The following examples elaborate the different kinds of relations we aim to extract. Example 1. miRNA-gene regulation (PMID-22569260): MicroRNA-223 regulates FOXO1 expression and cell proliferation. Here the miRNA “MicroRNA-223” regulates the gene “FOXO1”. As it is unclear whether the regulation is direct and hence we simply call this case an example of a miRNA-gene regulation relation. Example 2. miRNA-gene regulation (PMID-23861775): CyclinB1, cyclinA, bcl-xl and AKt are indirectly regulated by miR-26a in a CKS2-dependent manner. Here the miRNA “miR-26a” indirectly regulates the genes “CyclinB1” etc. , and is another example of a miRNA-gene regulation relation. Example 3. miRNA-target (PMID-23579289): The direct binding of miR-214 to the Osx 3' untranslated region (3' UTR) was demonstrated by a luciferase reporter assay using a construct containing the Osx 3' UTR. Example 3 illustrates the case of a direct miRNA-gene regulation relation, i. e. , a miRNA-target relation. In this example, the miRNA “miR-214” directly binds to the gene “Osx” 3' UTR. Some other examples of direct miRNA-gene regulation relations include the sentences where the gene is mentioned explicitly to be a target of the miRNA, as seen in Example 4. Example 4. miRNA-target (PMID-23041385): TGF-betaR2 mRNA, a validated miR-21 target, showed the highest expression in the leukocytes from a subset of the octogenarians. In summary, the set of miRNA-target relations is a subset of miRNA-gene regulation relations. Example 5. gene-miRNA regulation (PMID-23190608): We identified Sp1, a transcription factor endowed with oncogenic activity, as a negative regulator of miR-29b expression in MM cells. Finally, in Example 5, the gene “Sp1” is a regulator of the miRNA “miR-29b”. We call this kind of relation a gene-miRNA regulation relation. These five examples demonstrate the kinds of relations we want to extract and illustrate some of the variety of ways in which they may be described in text. miRTex was developed to extract these relations after examination of a development literature corpus. We created an annotated literature corpus of 350 abstracts for the development and evaluation of miRTex. 200 of these abstracts were used for development and the remaining 150 were used for evaluation. miRTex achieves F-scores close to 0. 90 on this test set. We further showed that the recall of miRNA-gene regulation relation extraction can be improved by 34 percentage points (pps) by using several linguistic generalizations and relaxing a rule constraint. We applied miRTex to the entire set of Medline abstracts as well as all the full-length articles in the PubMed Central (PMC) Open Access Subset, showing the scalability and robustness of miRTex. The full-scale processing of all the Medline abstracts also allowed us to create a database of the text mining results both for online search and file download via the website at http: //proteininformationresource. org/mirtex. It is worth noting that for 75% of the full-length articles containing miRNA-gene regulation relations, miRTex extracted such relations from the full-text bodies, but not from the abstracts, indicating that full-text processing is needed for comprehensive information extraction and that miRTex is effective for full-text relation extraction. Finally, we present two use cases that show how the text mining results from the full-scale extraction in the website can be used in combination with other tools or resources for knowledge discovery. The first use case utilizes miRTex in conjunction with gene expression data to identify candidate genes that may be regulated by miRNAs in Triple Negative Breast Cancer (TNBC, Disease Ontology [34] ID: 0060081), an aggressive form of breast cancer with limited treatment options [35]. Better understanding of the molecular mechanisms that drive TNBC, including the role of miRNAs, is crucial for the development of more effective therapy. We found that more than 35% of the miRNA-target relations extracted by miRTex, and reviewed by a curator, were not found in corresponding knowledge bases; moreover, miRTex provides valuable biological context information that is not captured by the databases. The second use case focused on the response to abiotic (non-pathogen) stress in Arabidopsis thaliana. Both miRNAs (e. g. , [36,37]) and protein kinases (e. g. , [38,39]) have been implicated in the abiotic stress response; however, the cross-talk between these regulatory mechanisms remains largely unexplored. To identify points where these regulatory pathways converge, we used miRTex results along with information from other text mining tools and databases to construct a network of miRNA-gene, kinase-substrate, and protein-protein interaction relations involving genes that have been implicated in the abiotic stress.
  In pre-processing, we first split text into individual sentences and recognize named entities, i. e. , miRNA and gene. We use the Stanford sentence splitter [40], and a well-known name detector, BANNER [41], for gene mention. Unlike gene mention detection, miRNA mention detection is an easier task because miRNA nomenclature is well defined [42,43] and closely followed in the literature. The basic pattern of miRNA mentions is one prefix “miR” (or “miRNA”, “microRNA”) followed by a unique identifying number, which is assigned based on sequence similarity (e. g. , “miR-1”). Additionally, the name might include one suffix such as “-a”, “-1”, “-3p” or “-5p”, and/or include a prefix that denotes the species. Most systems, including miRTex, use simple regular expressions to identify miRNA mentions. Following name detection, parse trees of the sentences are generated to match with our lexico-syntactic rules. In this paper, we used the Charniak-Johnson parser with David McClosky’s biomedical model [44,45]. In previous subsection we described how to extract the agent and the theme of a trigger word. Consider the fragment “a target for miR-142-3p” in Example 10 below. Two rules apply to the phrase, one of which will extract “miR-142-3p” as the agent of the target relation, and the other will extract “a target” as the theme. We call such phrases as referential phrases since they do not provide the actual argument but instead refer to the actual argument. Example 10. (PMID-22870299): We show that the proinflammatory cytokine interleukin 1 alpha (IL1A) is a target for miR-142-3p. In Example 10, after exploiting the “is-a” relation mentioned in the sentence, we can extract the actual target as “proinflammatory cytokine interleukin 1 alpha”/“IL1A”. We call the process of linking the referential phrase to the actual argument “referential linking”. Since the referential phrase can also be an anaphor (e. g. , a pronoun), the linking can also take place via anaphora resolution. We also observed miRNA-gene regulation relations are not always described by a single trigger. For example, Example 15. (PMID-17891175): we have identified additional potential mRNA targets of one of the miRNAs, (miR-125b) that are upregulated in prostate cancer and confirmed increased expression of one of these targets, EIF4EBP1. The miRNA “miR-125b” and the gene “EIF4EBP1” are in a regulation relation, but their trigger words are not the same. Recall that our rules require the arguments to have the same trigger word; thus, this example will be neglected by the system. Moreover, even if two arguments share the same trigger word, unseen pattern or parsing errors may sometimes prevent the rule-based system from extracting both arguments for the trigger word. We relax the same-trigger constraint to solve this problem. The intuition is that if a miRNA is the agent of regulation and a gene is the theme of regulation but described by a different trigger word in the same sentence, and they are not extracted by any of the previous rules, it is highly likely that they are in regulation relation. Thus, if two entities are not considered for extraction using the same trigger word in a sentence, we relax the same-trigger constraint so that a miRNA-gene pair can be extracted even they have different trigger words. Note that the original rules with same-trigger constraint are still used to extract miRNA-gene regulation relations with high confidence, while the relaxed rules only operate on the entities that are not in relations extracted by the same-trigger rules. For example, Example 16. (PMID-16784027): the miRNA (miR) -17-92 cluster has been characterized as an oncogene, while let-7 represses Ras and miR-15a/-16-1 represses Bcl-2, thereby acting as tumor suppressors. Three miRNA-gene regulation relations can be extracted by the rules with same-trigger constraint from this sentence, i. e. , “let-7” represses “Ras”, “miR-15a” represses “Bcl-2” and “miR-16-1” represses “Bcl-2”. The rules with relaxed constraint cannot extract any relations since there is only one entity “ (miR) -17-92” that does not participate in the three extracted relations. If we only apply the rules with relaxed constraint, six relations will be extracted between the three miRNAs and the two genes in a combinatorial manner; however, only three of them are correct. In our experiments, we evaluate the same-trigger rules alone and together with rules with the relaxed constraint. We prove that the system using both sets of rules obtains a higher recall without significantly compromising precision. When we extract miRNA-gene regulation relations, we further detect if the relation is direct, thus considered as a miRNA-target relation. Usually we can find evidence indicating the directness of the relation in the sentence from which it is extracted. Below is a list of evidence that we consider for directness of the relation. If one of such evidence can be detected for an extracted miRNA-gene regulation pair, the pair is assumed to be in a direct (miRNA-target) relation. We evaluated miRTex on two different corpora. First, we evaluated the extraction of all three types of relations on a test set containing 150 abstracts of an in-house developed corpus. The evaluation was at the abstract level meaning that only unique miRNA-gene pairs in an abstract are counted. Second, we evaluated miRTex on Bagewadi et al. ’s corpus [23]. It contains 200 and 100 abstracts for training set and test set, respectively. We inspected the training set and found that the annotated miRNA-gene pairs for specific miRNA names (SpMiR-GP relations [23]) corresponded to both miRNA-gene regulation and gene-miRNA regulation relations in miRTex. Thus, we combined miRTex results for both miRNA-gene regulation and gene-miRNA regulation relations when evaluating using this corpus. In addition, the evaluation on this corpus was at the mention level, as in [23], because this corpus annotates duplicated miRNA-gene associated pairs in abstracts. As there is no public corpus available for comparative evaluation of the three types of miRNA-gene relation extraction, we developed an in-house corpus consisting of 350 abstracts for both system development and evaluation. First, the query ‘microRNA[TIAB] OR miRNA[TIAB] OR miR[TIAB]’ was searched in PubMed to obtain potential abstracts containing miRNA information (performed in May 2014). This gave us about 30,000 abstracts. Second, we used our miRNA mention recognizer to extract miRNA mentions in the abstracts, and randomly selected 350 abstracts from those that have at least one miRNA mention. These abstracts were further processed by BANNER [41] to annotate gene mentions. The automatically annotated miRNA and gene mentions were manually checked and corrected by an annotator. Finally, these 350 abstracts were divided into a development set of 200 abstracts and a test set of 150 abstracts and annotated by two other annotators. Prior to formal annotation, the two annotators were asked to annotate independently a common set of 10 abstracts and compare their annotations. The aim of the comparison was to develop common annotation guidelines (S1 Dataset). Then 20 abstracts were annotated by both annotators independently based on the guidelines and the results were used to calculate the inter-annotator agreement. The remaining 320 abstracts were split into two distinct subsets and they were annotated by the two annotators individually. The annotation was conducted in an information-centric manner, i. e. , only distinct miRNA-gene or gene-miRNA regulation relations were extracted from each abstract, regardless of how many times the relation was mentioned. The annotators were asked to indicate the directness of the miRNA-gene regulation, i. e. , direct or unknown, from reading of the abstract. If it was direct, the miRNA-gene regulation pair was also a miRNA-target relation, in which the miRNA targets the gene directly. The characteristics of the corpus and evaluation results will be discussed in the next section. To extract the large amount of information buried in the miRNA-related literature as well as to validate the scalability and robustness of our system, we conducted two types of full-scale processing. First, we extracted relations from all the abstracts in Medline (~13M abstracts, as of May 2015). These abstracts have been downloaded (see download instructions at http: //www. nlm. nih. gov/bsd/licensee/medpmmenu. html) and indexed by Lucene (http: //lucene. apache. org) on our local machine. We selected the abstracts that contain specific miRNA mentions by using regular expressions to capture different forms of their mentions and then applied miRTex on this subset of abstracts. The miRTex extractions including text evidence are stored in a database called miRTexDB. Second, we applied miRTex to all the articles in the PMC Open Access Subset downloaded from http: //www. ncbi. nlm. nih. gov/pmc/tools/ftp (~1M full-length articles, as of May 2015). Based on our experience with the extraction of phosphorylation information [14], we feel that it is important to process full-length text because many of the relations are mentioned in full-length text only and not present in the abstract. Additionally, a previous study [47] noted that there are differences between the text in abstracts and full-length article bodies; thus we wanted to verify the effectiveness of our system on full-length text. As before, we applied miRTex only on the subset of articles that contain at least one or more miRNA mention. Each full-length article was divided into individual sections and subsections. Each of the subsections was treated as an independent unit of text so that the scope of anaphora resolution is limited to that subsection. The statistics of the two full-scale extractions are reported in next section, including a discussion of information found in the bodies of full-length articles as compared to their abstracts. We have developed a website for interactive query of the miRTex extractions in miRTexDB. The web interface accepts PubMed-like queries as input, supporting user queries such as a miRNA, a gene name, or even a general concept. For example, a user interested in miRNA-gene regulation in the context of Triple Negative Breast Cancer (TNBC) can search in the interface using the query ‘ “triple negative breast cancer” OR TNBC’. The query will be appended with the “AND” operator and miRNA keywords connected by the “OR” operator, i. e. , ‘query AND (miRNA[TIAB] OR microRNA[TIAB] OR miR[TIAB]) ’, since only the abstracts containing miRNA-related information have been processed by miRTex. The system, in turn, submits the query to PubMed and retrieves the list of PMIDs that satisfy the query. Since miRTex has already processed all the Medline abstracts, the system uses the PMID list to extract the information stored in miRTexDB for these abstracts. Fig 3 shows a screenshot of the displayed result (partial) for the query ‘ “triple negative breast cancer” OR TNBC’. In total, 75 abstracts are found for the query appended with miRNA keywords, from which 30 contain extracted miRNA-gene pairs: 61 miRNA-target relations and 36 miRNA-gene regulation relations. The first three columns show the PMID, miRNA and gene name. The fourth column indicates the directness of the extracted miRNA-gene regulation relation. The final two columns allow the user to see the literature evidence for the extracted pairs, at either the sentence or abstract level. Fig 3B and 3C depict examples of sentences for two cases, a direct target relation pair and a miRNA-gene regulation relation pair, respectively. Clicking on the last column displays the entire abstract with all the miRNA-gene pairs and trigger words highlighted. Results can be sorted by PMID, miRNA, gene, or number of positive sentences by clicking on the blue arrows next to the corresponding column headings. Note that since the results are already pre-computed and stored, no text processing is necessary for any query and hence the results are immediately available. The full results of a specific query can be downloaded as a CSV file and used for further analysis or input into another tool for further processing (e. g. , enrichment analysis). We present two use case studies utilizing miRTex information obtained through the website. The two use cases explore the role of miRNA-gene regulation in two scenarios: an animal disease and an environmental response in plants.
  Major characteristics of our in-house annotated corpus are listed in Table 1. For example, 229 miRNA-gene regulation pairs were annotated in 86 of the 150 abstracts from the test set; of these, 128 were marked as direct regulation. The corpus is available in S1 Dataset. We calculated the inter-annotator agreement in terms of F-score as was done in [54], where one of the annotators’ annotation is taken as gold standard, and the other’s annotation is contrasted with it. The inter-annotator agreement for the relations annotated on a common set of 20 abstracts is 86%. We performed full-scale processing of all the Medline abstracts and all the full-length articles in the PMC Open Access Subset. First, we extracted relations from all the Medline abstracts as of May 2015. The statistics of the extraction are listed in Table 3. In Table 4, it is interesting to note that abstracts positive for miRNA-gene relations contain ~2. 5 unique miRNA-gene regulation relations and ~2 unique miRNA-target relations on average. In contrast, a full-length article body positive for miRNA-gene relations contains ~7 unique miRNA-gene regulation pairs and ~5 unique miRNA-target relations on an average. Among the 8,618 full-length articles detected with miRNA-gene regulation relations, 6,442 (75%) do not have any relations extracted from their abstracts or titles. Instead, all relations were found only in the body of these 6,442 articles, which highlights the importance of mining from full-length articles. Additional analysis of the results reveals the information distribution in different types of sections (Table 5). The distribution is similar to the distribution of mentions of phosphorylation as determined by RLIMS-P [14]. The full-scale processing results of Medline abstracts are available at the website as we discussed in previous section. We are in the process of making the results from full-length articles also available at the website. We conducted two use cases, one involving Triple Negative Breast Cancer and the other involving the response to abiotic stress in Arabidopsis thaliana, to demonstrate how the integration of miRTex results with the output of other text mining tools, knowledge in curated databases, and high-throughput datasets can provide insight into complex biological processes. In this paper, we described the text mining system miRTex for extraction of miRNA-target relations as well as miRNA-gene and gene-miRNA regulation relations. We combined rule-based extraction with the linking process and a relaxed same-trigger constraint to achieve high recall while preserving the high precision of the rule-based system. The system achieved the state-of-the-art performance on a test set of 150 abstracts with the evaluation results showing that the precision of our system greatly outperforms the co-occurrence-based method with a comparable recall. The scalability and robustness of miRTex were validated by applying it to extract miRNA-gene relations from all the abstracts in Medline and all the full-length articles from the PMC Open Access Subset. The text mining results for all the Medline abstracts are stored in a database that can be searched through the website at http: //proteininformationresource. org/mirtex. Two use cases studying miRNA targets in the context of Triple Negative Breast Cancer and the abiotic stress response in plants were conducted to show how to use the website and integrate the results with other bioinformatics information to address complex biological questions. The results demonstrate that miRTex extracts miRNA-gene regulation relations and miRNA-target relations not found in curated miRNA databases. Moreover, because miRTex results are extracted directly from scientific text, it is possible to make queries that identify miRNA-gene relations relevant to specific biological and disease contexts. The corpus for system development and testing is available in S1 Dataset. We believe the release of this corpus will facilitate future comparisons of miRNA-target relation extraction systems. With the rapid increase of the miRNA-related literature, we anticipate that the full-scale extraction results of miRTex will be a useful resource for miRNA researchers and that miRTex itself can be integrated into literature-based curation pipelines. In the future, we plan to generalize our method to extract other kinds of biomedical relations, particularly regulation-based relations.
summary: MicroRNAs (miRNAs) are an important class of RNAs that regulate a wide range of biological processes by post-transcriptional regulation of gene expression. The amount of literature describing experimentally validated miRNA targets is increasing rapidly, which poses a challenge to researchers and biocurators to stay up-to-date with the available information. Text mining methods have been used to extract miRNA-gene associated pairs and assist in curation. In this paper, we describe miRTex, a text mining system that extracts miRNA-target, miRNA-gene regulation and gene-miRNA regulation relations. We evaluate miRTex performance on two corpora, and show that the elaborate use of lexico-syntactic information and linguistic generalizations enables it to achieve the state-of-the-art performance. We have processed the all the Medline abstracts and all the full-length articles in the PubMed Central Open Access Subset with miRTex, and provide a website to access the extraction results from all the Medline abstracts. The full-scale text mining results will be a useful resource for miRNA researchers, while the miRTex tool itself can be integrated into literature-based curation pipelines. We present two use cases (for animal and plant miRNAs, respectively) that show how the full-scale text mining can be used in combination with other bioinformatics resources to gain insight into biological processes.
title: miRTex: A Text Mining System for miRNA-Gene Relation Extraction