Literature Mining Tools Research Articles

AI-based mining of biomedical literature: Applications for drug repurposing for the treatment of dementia.

Neurodegenerative pathologies such as Alzheimer's disease, Parkinson's disease, Huntington's disease, Amyotrophic lateral sclerosis, Multiple sclerosis, HIV-associated neurocognitive disorder, and others significantly affect individuals, their families, caregivers, and healthcare systems. While there are no cures yet, researchers worldwide are actively working on the development of novel treatments that have the potential to slow disease progression, alleviate symptoms, and ultimately improve the overall health of patients. Huge volumes of new scientific information necessitate new analytical approaches for meaningful hypothesis generation. To enable the automatic analysis of biomedical data we introduced AGATHA, an effective AI-based literature mining tool that can navigate massive scientific literature databases, such as PubMed. The overarching goal of this effort is to adapt AGATHA for drug repurposing by revealing hidden connections between FDA-approved medications and a health condition of interest. Our tool converts the abstracts of peer-reviewed papers from PubMed into multidimensional space where each gene and health condition are represented by specific metrics. We implemented advanced statistical analysis to reveal distinct clusters of scientific terms within the virtual space created using AGATHA-calculated parameters for selected health conditions and genes. Partial Least Squares Discriminant Analysis was employed for categorizing and predicting samples (122 diseases and 20889 genes) fitted to specific classes. Advanced statistics were employed to build a discrimination model and extract lists of genes specific to each disease class. Here we focus on drugs that can be repurposed for dementia treatment as an outcome of neurodegenerative diseases. Therefore, we determined dementia-associated genes statistically highly ranked in other disease classes. Additionally, we report a mechanism for detecting genes common to multiple health conditions. These sets of genes were classified based on their presence in biological pathways, aiding in selecting candidates and biological processes that are exploitable with drug repurposing.

Exploring mechanobiology network of bone and dental tissue based on Natural Language Processing

Bone and cartilage tissues are physiologically dynamic organs that are systematically regulated by mechanical inputs. At cellular level, mechanical stimulation engages an intricate network where mechano-sensors and transmitters cooperate to manipulate downstream signaling. Despite accumulating evidence, there is a notable underutilization of available information, due to limited integration and analysis. In this context, we conceived an interactive web tool named MechanoBone to introduce a new avenue of literature-based discovery. Initially, we compiled a literature database by sourcing content from Pubmed and processing it through the Natural Language Toolkit project, Pubtator, and a custom library. We identified direct co-occurrence among entities based on existing evidence, archiving in a relational database via SQLite. Latent connections were then quantified by leveraging the Link Prediction algorithm. Secondly, mechanobiological pathway maps were generated, and an entity-pathway correlation scoring system was established through weighted algorithm based on our database, String, and KEGG, predicting potential functions of specific entities. Additionally, we established a mechanical circumstance-based exploration to sort genes by their relevance based on big data, revealing the potential mechanically sensitive factors in bone research and future clinical applications. In conclusion, MechanoBone enables: 1) interpreting mechanobiological processes; 2) identifying correlations and crosstalk among molecules and pathways under specific mechanical conditions; 3) connecting clinical applications with mechanobiological processes in bone research. It offers a literature mining tool with visualization and interactivity, facilitating targeted molecule navigation and prediction within the mechanobiological framework of bone-related cells, thereby enhancing knowledge sharing and big data analysis in the biomedical realm.

AI-based mining of biomedical literature: Applications for drug repurposing for the treatment of dementia.

This manuscript outlines our project involving the application of AGATHA, an AI-based literature mining tool, to discover drugs with the potential for repurposing in the context of neurocognitive disorders. The primary objective is to identify connections between approved medications and specific health conditions through advanced statistical analysis, including techniques like Partial Least Squares Discriminant Analysis (PLSDA) and unsupervised clustering. The methodology involves grouping scientific terms related to different health conditions and genes, followed by building discrimination models to extract lists of disease-specific genes. These genes are then analyzed through pathway analysis to select candidates for drug repurposing.

Reviewing Atrial Fibrillation Pathophysiology from a Network Medicine Perspective: The Relevance of Structural Remodeling, Inflammation, and the Immune System.

Atrial fibrillation (AF) is the most common type of sustained arrhythmia. The numerous gaps concerning the knowledge of its mechanism make improving clinical management difficult. As omics technologies allow more comprehensive insight into biology and disease at a molecular level, bioinformatics encompasses valuable tools for studying systems biology, as well as combining and modeling multi-omics data and networks. Network medicine is a subarea of network biology where disease traits are considered perturbations within the interactome. With this approach, potential disease drivers can be revealed, and the effect of drugs, novel or repurposed, used alone or in combination, may be studied. Thus, this work aims to review AF pathology from a network medicine perspective, helping researchers to comprehend the disease more deeply. Essential concepts involved in network medicine are highlighted, and specific research applying network medicine to study AF is discussed. Additionally, data integration through literature mining and bioinformatics tools, with network building, is exemplified. Together, all of the data show the substantial role of structural remodeling, the immune system, and inflammation in this disease etiology. Despite this, there are still gaps to be filled about AF.

Bibliometric and Text Analytics Approaches to Review COVID-19 Impacts on Supply Chains

The current COVID-19 pandemic has virtually disrupted supply chains worldwide. Thus, supply chain research has received significant attention. While the impacts have been immeasurable, organizations have realized the need to design strategies to overcome such unexpected events. Therefore, the supply chain research landscape has evolved to address the challenges during the pandemic. However, available literature surveys have not explored the power of text analytics. Hence, in this review, an analysis of the supply chain literature related to the impacts of COVID-19 is performed to identify the current research trends and future research avenues. To discover the frequent topics discussed in the literature, bibliometric analysis (i.e., keyword co-occurrence network) and text mining tools (i.e., N-gram analysis and topic modeling) are employed for the whole corpus and the top-three contributing journals (i.e., Sustainability, International Journal of Logistics Management, Operations Management Research). Moreover, text analytics (i.e., Term Frequency-Inverse Document Frequency: TF-IDF) is utilized to discover the distinctive topics in the corpus and per journals. A total of 574 papers published up to the first semester of 2022 were collected from the Scopus database to determine the research trends and opportunities. The keyword network identified four clusters considering the implementation of digitalization to achieve resilience and sustainability, the usage of additive manufacturing during the pandemic, the study of food supply chains, and the development of supply chain decision models to tackle the pandemic. Moreover, the segmented keyword network analysis and topic modeling were performed for the top three contributors. Although both analyses draw the research concentrations per journal, the keyword network tends to provide a more general scope, while the topic modeling gives more specific topics. Furthermore, TF-IDF scores unveiled topics rarely studied, such as the implications of the pandemic on plasma supply chains, cattle supply chains, and reshoring decisions, to mention a few. Additionally, it was observed how the different methodologies implemented allowed to complement the information provided by each method. Based on the findings, future research avenues are discussed. Therefore, this research will help supply chain practitioners and researchers to identify supply chain advancements, gaps in the literature and future research streams.

Abstract PO-126: Loss of HIF1A decreases resistance to radiation and invasiveness in pancreatic ductal adenocarcinoma

Abstract Pancreatic ductal adenocarcinoma (PDAC) is the fourth most lethal cancer in the United States with an estimated 60,430 new cases and 48,220 deaths in 2021. PDACs are characterized by extensive desmoplastic stroma and severe hypovascularity, resulting in an intra-tumoral hypoxic microenvironment. In response to hypoxic stress, cancer cells set off many adaptive responses including metabolism regulation, cell survival, and inflammation through the stabilization and activation of the hypoxia-inducible factor (HIF) family of transcription factors. In this study, we used the gene-editing tool CRISPR-cas9 to knock out HIF1A and investigate whether HIF1A affects the radioresistant and invasive characteristics of PDACs in a KPC cell line model. To first observe how HIF1A affects PDAC radioresistance, we performed a clonogenic survival assay with increasing doses of radiation on both wild-type and HIF1A knockout (KO) cells treated and untreated with CoCl2-induced hypoxia conditions (100 uM and 200 uM CoCl2). Our data showed that under hypoxia, KO cells exhibited significant cell death when treated with 6, 8, and 10 Gy of radiation as compared to wild-type KPC cells, emphasizing the role of HIF1A in radiation resistance. In addition, to understand the role of HIF1A in regulating the invasive behavior of PDACs, we performed a cell proliferation assay on wild-type and KO KPC cells. HIF1A KO cells treated with CoCl2 exhibited significantly reduced proliferation compared to wild-type cells also treated with CoCl2 (p<0.01), though no significant difference was observed between untreated HIF1A KO and untreated wild-type cells (p>0.05). Because western blot demonstrated increased HIF1A expression in wild-type cells following CoCl2 treatment, our results provided evidence for the role of HIF1A activation in promoting PDAC invasiveness. Through western blot, we also confirmed the association between HIF1A expression with p53 degradation in PDAC. We used COREMINE, a literature mining tool, to map a direct interaction between HIF1A with KRAS in PDACs (p=0.000016). We propose HIF1A as a switch to activate KRAS and degrade p53 under hypoxic conditions in PDAC proliferation. Thus, modulating the HIF1A switch may be an important mechanism to reduce the tumor-promoting microenvironment and inhibit cancer growth. Citation Format: Kevin J. Tu, Sanjit K. Roy, Binny Bhandary, Amit Sawant, Hem D. Shukla. Loss of HIF1A decreases resistance to radiation and invasiveness in pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the AACR Virtual Special Conference on Pancreatic Cancer; 2021 Sep 29-30. Philadelphia (PA): AACR; Cancer Res 2021;81(22 Suppl):Abstract nr PO-126.

Automatic literature mining tool to extract glycosylation information from literature

Automatic literature mining tool to extract glycosylation information from literature

G2PMineR: A Genome to Phenome Literature Review Approach.

There is a gap in the conceptual framework linking genes to phenotypes (G2P) for non-model organisms, as most non-model organisms do not yet have genomic resources readily available. To address this, researchers often perform literature reviews to understand G2P linkages by curating a list of likely gene candidates, hinging upon other studies already conducted in closely related systems. Sifting through hundreds to thousands of articles is a cumbersome task that slows down the scientific process and may introduce bias into a study. To fill this gap, we created G2PMineR, a free and open source literature mining tool developed specifically for G2P research. This R package uses automation to make the G2P review process efficient and unbiased, while also generating hypothesized associations between genes and phenotypes within a taxonomical framework. We applied the package to a literature review for drought-tolerance in plants. The analysis provides biologically meaningful results within the known framework of drought tolerance in plants. Overall, the package is useful for conducting literature reviews for genome to phenome projects, and also has broad appeal to scientists investigating a wide range of study systems as it can conduct analyses under the auspices of three different kingdoms (Plantae, Animalia, and Fungi).

BioLitMine: Advanced Mining of Biomedical and Biological Literature About Human Genes and Genes from Major Model Organisms

The accumulation of biological and biomedical literature outpaces the ability of most researchers and clinicians to stay abreast of their own immediate fields, let alone a broader range of topics. Although available search tools support identification of relevant literature, finding relevant and key publications is not always straightforward. For example, important publications might be missed in searches with an official gene name due to gene synonyms. Moreover, ambiguity of gene names can result in retrieval of a large number of irrelevant publications. To address these issues and help researchers and physicians quickly identify relevant publications, we developed BioLitMine, an advanced literature mining tool that takes advantage of the medical subject heading (MeSH) index and gene-to-publication annotations already available for PubMed literature. Using BioLitMine, a user can identify what MeSH terms are represented in the set of publications associated with a given gene of the interest, or start with a term and identify relevant publications. Users can also use the tool to find co-cited genes and a build a literature co-citation network. In addition, BioLitMine can help users build a gene list relevant to a MeSH term, such as a list of genes relevant to “stem cells” or “breast neoplasms.” Users can also start with a gene or pathway of interest and identify authors associated with that gene or pathway, a feature that makes it easier to identify experts who might serve as collaborators or reviewers. Altogether, BioLitMine extends the value of PubMed-indexed literature and its existing expert curation by providing a robust and gene-centric approach to retrieval of relevant information.

Recent advances of automated methods for searching and extracting genomic variant information from biomedical literature.

MotivationTo obtain key information for personalized medicine and cancer research, clinicians and researchers in the biomedical field are in great need of searching genomic variant information from the biomedical literature now than ever before. Due to the various written forms of genomic variants, however, it is difficult to locate the right information from the literature when using a general literature search system. To address the difficulty of locating genomic variant information from the literature, researchers have suggested various solutions based on automated literature-mining techniques. There is, however, no study for summarizing and comparing existing tools for genomic variant literature mining in terms of how to search easily for information in the literature on genomic variants.ResultsIn this article, we systematically compared currently available genomic variant recognition and normalization tools as well as the literature search engines that adopted these literature-mining techniques. First, we explain the problems that are caused by the use of non-standard formats of genomic variants in the PubMed literature by considering examples from the literature and show the prevalence of the problem. Second, we review literature-mining tools that address the problem by recognizing and normalizing the various forms of genomic variants in the literature and systematically compare them. Third, we present and compare existing literature search engines that are designed for a genomic variant search by using the literature-mining techniques. We expect this work to be helpful for researchers who seek information about genomic variants from the literature, developers who integrate genomic variant information from the literature and beyond.

Does testosterone mediate the relationship between vitamin D and prostate cancer? A systematic review and meta-analysis protocol

BackgroundEvidence from studies on prostate cancer progression have identified vitamin D to be a potentially important nutrient. However, the World Cancer Research Fund and American Institute for Cancer Research have reported the quality of this evidence to be limited and warrant further investigation. We plan to use the recently developed WCRF International/University of Bristol mechanistic systematic review framework to determine whether the observed association between vitamin D and prostate cancer exists through a plausible biological pathway.MethodsThis protocol sets out how we will perform a systematic review of the literature in human and animal studies. We will search the electronic databases MEDLINE, EMBASE, PubMed, and BIOSIS Citation Index without restrictions on year of publication or language. We will extract data from observational and experimental studies examining two inter-linked pathways in the relationship between vitamin D and prostate cancer progression: (1) vitamin D and testosterone, and (2) testosterone and prostate cancer progression. We focus on testosterone as its actions form a potentially novel intermediate mechanism that was identified via our online literature mining tools. The outcomes of interest include incidence or prevalence of prostate cancer, measures of prostate cancer progression (including biochemical recurrence, local, or distal metastases), and prostate cancer-specific mortality. We will assess study quality and the level of certainty of the evidence. We will analyse data where possible, using meta-analysis with forest plots or albatross plots; otherwise, a narrative synthesis will be performed.DiscussionTo our knowledge, this will be the first systematic synthesis of the evidence underpinning the vitamin D-testosterone-prostate cancer mechanistic pathway. The results of the review may inform future research, intervention trials, and public health messages.

Process discovery from event data: Relating models and logs through abstractions

Event data are collected in logistics, manufacturing, finance, health care, customer relationship management, e‐learning, e‐government, and many other domains. The events found in these domains typically refer to activities executed by resources at particular times and for a particular case (i.e., process instances). Process mining techniques are able to exploit such data. In this article, we focus on process discovery. However, process mining also includes conformance checking, performance analysis, decision mining, organizational mining, predictions, recommendations, and so on. These techniques help to diagnose problems and improve processes. All process mining techniques involve both event data and process models. Therefore, a typical first step is to automatically learn a control‐flow model from the event data. This is very challenging, but in recent years, many powerful discovery techniques have been developed. It is not easy to compare these techniques since they use different representations and make different assumptions. Users often need to resort to trying different algorithms in an ad‐hoc manner. Developers of new techniques are often trying to solve specific instances of a more general problem. Therefore, we aim to unify existing approaches by focusing on log and model abstractions. These abstractions link observed and modeled behavior: Concrete behaviors recorded in event logs are related to possible behaviors represented by process models. Hence, such behavioral abstractions provide an “interface” between both of them. We discuss four discovery approaches involving three abstractions and different types of process models (Petri nets, block‐structured models, and declarative models). The goal is to provide a comprehensive understanding of process discovery and show how to develop new techniques. Examples illustrate the different approaches and pointers to software are given. The discussion on abstractions and process representations is also presented to reflect on the gap between process mining literature and commercial process mining tools. This facilitates users to select an appropriate process discovery technique. Moreover, structuring the role of internal abstractions and representations helps broaden the view and facilitates the creation of new discovery approaches.This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Application Areas > Business and Industry Technologies > Machine Learning Application Areas > Data Mining Software Tools

Reducing the Search Space in Literature-Based Discovery by Exploring Outlier Documents: a Case Study in Finding Links Between Gut Microbiome and Alzheimer’s Disease

Literature-based discovery tools have been often used to overcome the problem of fragmentation of science and to assist researchers in their process of cross-domain knowledge discovery. In this paper we propose a methodology for cross-domain literature-based discovery that focuses on outlier documents to reduce the search space of potential cross-domain links and to improve search efficiency. In a previous study, literature mining tools OntoGen for document clustering and CrossBee for cross-domain bridging term exploration were combined to search for hidden relations in scientific papers from two different domains of interest, where the utility of the approach was demonstrated in a study involving PubMed papers about Alzheimer’s disease and gut microbiome. This paper extends the approach by proposing a methodology, implemented as a repeatable workflow in a web-based text mining platform TextFlows, which enables easy access and execution of the methodology for the interested researcher.

MiRiaD: A Text Mining Tool for Detecting Associations of microRNAs with Diseases.

BackgroundMicroRNAs are increasingly being appreciated as critical players in human diseases, and questions concerning the role of microRNAs arise in many areas of biomedical research. There are several manually curated databases of microRNA-disease associations gathered from the biomedical literature; however, it is difficult for curators of these databases to keep up with the explosion of publications in the microRNA-disease field. Moreover, automated literature mining tools that assist manual curation of microRNA-disease associations currently capture only one microRNA property (expression) in the context of one disease (cancer). Thus, there is a clear need to develop more sophisticated automated literature mining tools that capture a variety of microRNA properties and relations in the context of multiple diseases to provide researchers with fast access to the most recent published information and to streamline and accelerate manual curation.MethodsWe have developed miRiaD (microRNAs in association with Disease), a text-mining tool that automatically extracts associations between microRNAs and diseases from the literature. These associations are often not directly linked, and the intermediate relations are often highly informative for the biomedical researcher. Thus, miRiaD extracts the miR-disease pairs together with an explanation for their association. We also developed a procedure that assigns scores to sentences, marking their informativeness, based on the microRNA-disease relation observed within the sentence.ResultsmiRiaD was applied to the entire Medline corpus, identifying 8301 PMIDs with miR-disease associations. These abstracts and the miR-disease associations are available for browsing at http://biotm.cis.udel.edu/miRiaD. We evaluated the recall and precision of miRiaD with respect to information of high interest to public microRNA-disease database curators (expression and target gene associations), obtaining a recall of 88.46–90.78. When we expanded the evaluation to include sentences with a wide range of microRNA-disease information that may be of interest to biomedical researchers, miRiaD also performed very well with a F-score of 89.4. The informativeness ranking of sentences was evaluated in terms of nDCG (0.977) and correlation metrics (0.678-0.727) when compared to an annotator’s ranked list.ConclusionsmiRiaD, a high performance system that can capture a wide variety of microRNA-disease related information, extends beyond the scope of existing microRNA-disease resources. It can be incorporated into manual curation pipelines and serve as a resource for biomedical researchers interested in the role of microRNAs in disease. In our ongoing work we are developing an improved miRiaD web interface that will facilitate complex queries about microRNA-disease relationships, such as “In what diseases does microRNA regulation of apoptosis play a role?” or “Is there overlap in the sets of genes targeted by microRNAs in different types of dementia?”.”Electronic supplementary materialThe online version of this article (doi:10.1186/s13326-015-0044-y) contains supplementary material, which is available to authorized users.

Biomolecular Relationships Discovered from Biological Labyrinth and Lost in Ocean of Literature: Community Efforts Can Rescue Until Automated Artificial Intelligence Takes Over

Many brilliant minds are at work to decipher the biological labyrinth and as a result immense amount of information about biological entities and their relationships is getting accumulated in the form of published literature (Hunter and Cohen, 2006). To cater the needs of a researcher, many tools are designed to perform tasks of Named Entity Recognition (NER), Information Retrieval (IR), and Information Extraction (IE) viz. A Combined Clinical Concept Annotator (Kang et al., 2012), BANNER (Leaman and Gonzalez, 2008), Biblio-MetReS (Usie et al., 2014), BioTextQuest+ (Papanikolaou et al., 2014), BIOSMILE Web Search (Dai et al., 2008), E3Miner (Lee et al., 2008), EBIMed (Rebholz-Schuhmann et al., 2007), eFIP (Arighi et al., 2011), FACTA+ (Tsuruoka et al., 2008), GNSuite1, iHOP (Hoffmann and Valencia, 2004), MyMiner (Salgado et al., 2012), RLIMS-P(Hu et al., 2005), Anni (Jelier et al., 2008), CoPub (Frijters et al., 2008), MedScan (Novichkova et al., 2003), PPInterFinder (Raja et al., 2012), pGenN (Ding et al., 2015), SciMiner (Hur et al., 2009), BIGNER (Li et al., 2009), hybrid named entity tagger (Raja et al., 2014), and more such tools can be obtained from BIONLP resource2 and in detail analysis of many NLP tools is given by Krallinger et al. (2008) and Fleuren and Alkema (2015). Table ​Table11 gives an informational and statistical insight into some of these literature mining tools, shedding light on their efficiency translated by statistical parameters viz. F-score, recall, and precision. Many tools are domain specific like kinase family specific but still calls for human intervention for exactitude and thus limit their usage. Moreover, the data output formats are sometimes too vague as name highlighting; to be put to use for bigger literature searches. Table 1 Informational (viz. data used, parameters for evaluation and working platform) and statistical (viz. f-value, recall and precision) insights for a few literature mining tools with their brief description and links to the tools' home page. The naming ambiguity in scientific literature is one of the major concerns for NER and sentence structure for IR and IE. Presently, NER tools need to maintain a comprehensive dictionary of all names, aliases and web-repository specific IDs or have their AI (Artificial Intelligence) defined algorithms trained on many test data sets. Many such dictionaries are available but the list is ever-increasing and so is the training data set. This results into investing more money, time and effort in obtaining a comprehensive list of names, aliases and IDs. A very comprehensive work on NLP can be found on BioNLP3. The availability of manpower or intellect is huge but there is acute scarcity of funds (Bourne et al., 2015), so we have to device optimized approaches to take care of the issues discussed in subsequent section.

BioGyan: A Tool to Identify Gene Functions from Literature

Background: The primary objective of life science research is to understand complex cellular mechanisms and the interplay of various genes/proteins in multiple cellular processes. For this, PubMed is still the primary source of biomedical information even though multiple other databases such as UniProt, Protein Data Bank (PDB) and Reactome exist. Objective: With the available large volume data from high-throughput technologies and multiple databases, finding relevant information for gene-process-phenotype has now become extremely challenging and tedious. No tool is currently available to simultaneously search PubMed and multiple other databases to get holistic information. Moreover, a typical PubMed search returns large number of articles, which need to be manually screened for identifying relevant literature. Hence, we developed BioGyan, a literature mining tool to simplify the combinatorial search for genes, celltypes and cellular processes in PubMed and other relevant databases. Methods: BioGyan uses a robust scoring method to rank articles relevant to user search terms. The scoring method is based on the weighted sum of co-occurrence of gene, process and interactions terms in an abstract. Results: BioGyan retrieves PubMed articles supporting association between queried genes and processes, relevant pathways from pathway databases and 3-dimensional structures from PDB. For easy viewing, all information to the user is available in single window. BioGyan showed an accuracy of 85.46% in predicting relevance of articles to a gene-process association, and performed better than PESCADOR. Conclusion: BioGyan has several key features such as batch query of genes as well as processes, offline reading of articles, export of list of articles as bibliography and flexibility for user to revise the article relevance, making it a vital tool for literature search. Thus, BioGyan is a unique tool that offers holistic search across multiple databases while greatly automating the entire process.

Leveraging Naked Mole Rat (Heterocephalus glaber) Comparative Genomics to Identify Canine Genes Modulating Susceptibility to Tumorigenesis and Cancer Phenotypes

We utilized a comparative genomics approach to analyze a core set of tumorigenesis orthologs among human, mouse, dog and naked mole rat. The analysis identified cancer orthologs that are both conserved and divergent between dog and the cancer resistant species naked mole rat. These tumorigenesis orthologous are associated with phenotypes that modulate cancer susceptibility, cardiac development, craniofacial development, brain development, skeletal development, and immune function, to name a few. This bioinformatics approach employed a variety of literature mining tools to further uncover relationships between the tumorigenesis orthologs. Together, these results shed light on the relationship between breed formations, breed associated morphological traits and breed associated susceptibility to tumorigenesis. These findings support the use of a comparative genomic analysis between species with dramatically different disease phenotypes as a gene discovery tool. A total of 146 proteins coding SNPs were identified in these tumorigenesis orthologs representing missense variations, frame shift variations and nonsense variations. The genes identified in this study can serve as a list of candidates for subsequent laboratory and clinical study. Furthermore, the identification of SNPs impacting the primary structure of the tumorigenesis orthologs may provide clues about the basis of cancer susceptibility between dog breeds.

Empirical study using network of semantically related associations in bridging the knowledge gap.

BackgroundThe data overload has created a new set of challenges in finding meaningful and relevant information with minimal cognitive effort. However designing robust and scalable knowledge discovery systems remains a challenge. Recent innovations in the (biological) literature mining tools have opened new avenues to understand the confluence of various diseases, genes, risk factors as well as biological processes in bridging the gaps between the massive amounts of scientific data and harvesting useful knowledge.MethodsIn this paper, we highlight some of the findings using a text analytics tool, called ARIANA - Adaptive Robust and Integrative Analysis for finding Novel Associations.ResultsEmpirical study using ARIANA reveals knowledge discovery instances that illustrate the efficacy of such tool. For example, ARIANA can capture the connection between the drug hexamethonium and pulmonary inflammation and fibrosis that caused the tragic death of a healthy volunteer in a 2001 John Hopkins asthma study, even though the abstract of the study was not part of the semantic model.ConclusionAn integrated system, such as ARIANA, could assist the human expert in exploratory literature search by bringing forward hidden associations, promoting data reuse and knowledge discovery as well as stimulating interdisciplinary projects by connecting information across the disciplines.

Literature mining of genetic variants for curation: quantifying the importance of supplementary material

A major focus of modern biological research is the understanding of how genomic variation relates to disease. Although there are significant ongoing efforts to capture this understanding in curated resources, much of the information remains locked in unstructured sources, in particular, the scientific literature. Thus, there have been several text mining systems developed to target extraction of mutations and other genetic variation from the literature. We have performed the first study of the use of text mining for the recovery of genetic variants curated directly from the literature. We consider two curated databases, COSMIC (Catalogue Of Somatic Mutations In Cancer) and InSiGHT (International Society for Gastro-intestinal Hereditary Tumours), that contain explicit links to the source literature for each included mutation. Our analysis shows that the recall of the mutations catalogued in the databases using a text mining tool is very low, despite the well-established good performance of the tool and even when the full text of the associated article is available for processing. We demonstrate that this discrepancy can be explained by considering the supplementary material linked to the published articles, not previously considered by text mining tools. Although it is anecdotally known that supplementary material contains ‘all of the information’, and some researchers have speculated about the role of supplementary material (Schenck et al. Extraction of genetic mutations associated with cancer from public literature. J Health Med Inform 2012;S2:2.), our analysis substantiates the significant extent to which this material is critical. Our results highlight the need for literature mining tools to consider not only the narrative content of a publication but also the full set of material related to a publication.

Protection Genes in Nucleus Accumbens Shell Affect Vulnerability to Nicotine Self-Administration across Isogenic Strains of Adolescent Rat

Classical genetic studies show the heritability of cigarette smoking is 0.4–0.6, and that multiple genes confer susceptibility and resistance to smoking. Despite recent advances in identifying genes associated with smoking behaviors, the major source of this heritability and its impact on susceptibility and resistance are largely unknown. Operant self-administration (SA) of intravenous nicotine is an established model for smoking behavior. We recently confirmed that genetic factors exert strong control over nicotine intake in isogenic rat strains. Because the processing of afferent dopaminergic signals by nucleus accumbens shell (AcbS) is critical for acquisition and maintenance of motivated behaviors reinforced by nicotine, we hypothesized that differential basal gene expression in AcbS accounts for much of the strain-to-strain variation in nicotine SA. We therefore sequenced the transcriptome of AcbS samples obtained by laser capture microdissection from 10 isogenic adolescent rat strains and compared all RNA transcript levels with behavior. Weighted gene co-expression network analysis, a systems biology method, found 12 modules (i.e., unique sets of genes that covary across all samples) that correlated (p<0.05) with amount of self-administered nicotine; 9 of 12 correlated negatively, implying a protective role. PCR confirmed selected genes from these modules. Chilibot, a literature mining tool, identified 15 genes within 1 module that were nominally associated with cigarette smoking, thereby providing strong support for the analytical approach. This is the first report demonstrating that nicotine intake by adolescent rodents is associated with the expression of specific genes in AcbS of the mesolimbic system, which controls motivated behaviors. These findings provide new insights into genetic mechanisms that predispose or protect against tobacco addiction.