Literature-based discovery of diabetes

activated atomic result 8 species (ROS) argon cognise mediators of cellular teleph hotshotular alter in triple diseases including diabetic crookednesss. Despite its importance, no comprehensive database is presently available for the elements associated with ROS. Methods We present ROS- and diabetes- tie in bums (genes/proteins) stash away from the biomedical lit through a school textbook tap technology. A web-establish publications tap tool, SciMiner, was applied to 54 biomedical written enumeration indexed with diabetes and ROS by PubMed to identify relevant markings.Over- be targets in the ROS-diabetes publications were obtained through comparisons against indiscriminately selected literature. The mirror image levels of baseball club genes, selected from the clear up rank ROS-diabetes caboodle, were cargonful in the dorsal root ganglia (DRG) of diabetic and non-diabetic DBA/2J mice in order to evaluate the biologic relevance of literature- deri ved targets in the pathogenesis of diabetic neuropathy. Results SciMiner set 1,026 ROS- and diabetes-related targets from the 54 biomedical document (http//Jdrf. eurology. med. umich. edu/ROSDiabetes/ webcite). 53 targets were importantly over- equal in the ROS-diabetes literature ompared to every which way selected literature. These over-represented targets included well-known members of the aerobic emphasize response including catalase, the NADPH oxidase family, and the superoxide anion anion dismutase family of proteins. Eight of the nine selected genes exhibited signifi kittyt distinguishableial font ming take with diabetic and non-diabetic mice.For six genes, the direction of chation change in diabetes parallel of latitudeed enhanced aerophilous tension in the DRG. Conclusions literary productions exploit compiled ROS-diabetes related targets from the biomedical literature and led us to evaluate the biological relevance of selected targets in the athogenesis of d iabetic neuropathy. Diabetes is a metabolous disease in which the body does not produce or properly reply to insulin, a hormone required to alter carbohydrates into nada for daily life. According to the Ameri female genitals Diabetes Association, 23. million children and adults, virtually 7. 8% of the universe of discourse in the United States, apply diabetes 1. The woo of diabetes in 2007 was estimated to be $174 billion 1. The micro- and macro-vascular complications of diabetes are the roughly common causes of renal tailure, cecity and amputations prima(p) to signifi tail assemblyt morta y, morbidity light quality of life however, incomplete misgiving of the causes of diabetic complications hinders the developing of mechanism- found therapies.In vivo and in vitro experiments include a number of enzymatic and non-enzymatic metabolic pathways in the beginning and progression of diabetic complications 2 including (1) make up polyol pathway body summons leaders to sorbitol and fructose accumulation, NAD(P)-redox imbalances and changes in signal transduction (2) non- enzymatic glycation of proteins yielding advanced glycation end-products (AGES) (3) ctivation of protein kinase C (PKC), initiating a cascade of intracellular tenor responses and (4) increased hexosamine pathway flux 2,3.Only recently has a link among these pathways been established that provides a unify mechanism of t electric exposelet damage. Each of these pathways this instant and indirectly leads to overproduction of reactive oxygen species (ROS) 23. ROS are super reactive ions or small molecules including oxygen ions, bare(a) radicals and peroxides, form as natural byproducts of cellular energy metabolism. ROS are implicated in multiple cellular pathways much(prenominal) as mitogen-activated protein kinase MAPK) polarity, c-Jun amino-terminal kinase ONK), cell proliferation and apoptosis 4-6.Due to the passing reactive properties of ROS, ebullient ROS may cause mo numental damage to proteins, DNA, RNA and lipids. All cells express enzymes heart-to-heart of neutralizing ROS. In addition to the primary(prenominal)tenance of antioxidant schemes such as glutathione and thioredoxins, primary sensory neurons express two main detoxifying enzymes superoxide dismutase ( sod) 7 and catalase 8. bugger converts superoxide (02-) to H202, which is reduced to H20 by glutathione and catalase 8.SODI is the main form of SOD in the cytol SOD2 is located in spite of appearance the itochondria. In neurons, SODI activity represents approximately 90% of check SOD activity and SOD2 approximately 10% 9. infra diabetic conditions, this protective mechanism is overwhelmed payable to the substantial increase in ROS, leading to cellular damage and dysfunction 10. The report that increased ROS and oxidative focal point carry to the pathogenesis of diabetic complications has led scientists to investigate distinguishable oxidative focal point pathways 7,11.Inh ibition of ROS or upkeep of euglycemia restores metabolic and vascular imbalances and blocks two the initiation and progression of omplications 1 2,13. Despite the significant implications and extensive research into the role of ROS in diabetes, no comprehensive database regarding ROS-related genes or proteins is flowly available. In the present study, a comprehensive inc lineage of ROS- and diabetes-related targets (genes/proteins) was compiled from the biomedical literature through text mining technology.SciMiner, a web-based literature mining tool 14, was employ to mobilize and process documents and identify targets from the text. SciMiner provides a convenient web-based computer program for target- realisation within the biomedical iterature, similar to an new(prenominal)(a)(prenominal) tools including EBIMed 1 5, ALI BABA 16, and Polysearch 1 7 however, SciMiner is uncomparable in that it searches tull text documents, suppo free-text PubMed interrogative sentence st yle, and allows the comparison of target lists from multiple queries.The ROS-diabetes targets collected by SciMiner were further tasteed against randomly selected non-ROS-diabetes literature to identify targets that are significantly over- represented in the ROS-diabetes literature. operable enrichment analyses were performed on these targets to identify significantly over-represented biological unctions in damage of constituent Ontology (GO) terms and pathways. In order to confirm the biological relevance of the over-represented ROS-diabetes targets, the gene view levels of nine selected targets were measured in dorsal root ganglia (DRG) from mice with and without diabetes.DRG consist primary sensory neurons that relay development from the periphery to the central nervous system (CNS) unalike the CNS, DRG are not protect by a blood-nerve barrier, and are thus vulnerable to metabolic and toxic disfigurement 19. We hypothesize that contraryial facial mental synthesis of throttle targets in DRG would confirm heir intimacy in the pathogenesis of diabetic neuropathy. delineate ROS-diabetes literature To retrieve the list of biomedical literature associated with ROS and diabetes, PubMed was queried victimisation ( oxidizable Oxygen SpeciesMeSH AND Diabetes MellitusMeSH).This interrogative yielded 54 articles as of April 27, 2009. SciMiner, a web-based literature mining tool 14, was use to retrieve and process the abstracts and available luxuriant text documents to identify targets (full text documents were available for approximately 40% of the 1 , 1 54 articles). SciMiner- determine targets, eported in the form of HGNC HUGO (Human Genome Organization) constituent speech communication delegation genes, were sustain by manual(a) critique of the text. Comparison with human curated data (NCBI element2PubMed) The NCBI component database provides cerebrate among gene and PubMed.The links are the result of (1) manual curation within the N CBI via literature analysis as spark of generating a constituent record, (2) integration of t for each one(prenominal)ing from otherwise public databases, and (3) brokerRlF (Gene Reference Into Function) in which human experts provide a drawing summary of gene functions and make the connections between citation PubMed) and Gene databases. For the 54 ROS-diabetes articles, gene-paper associations were retrieved from the NCBI Gene database. Non-human genes were mapped to homologous human genes through the NCBI HomoloGene database.The retrieved genes were compared against the SciMiner derived targets. every(prenominal) genes preoccupied by SciMiner were added to the ROS-diabetes target set. Protein-protein interactions among ROS-diabetes targets To indirectly examine the association of literature derived targets (by SciMiner and NCBI Gene2PubMed) with ROS and diabetes, protein-protein interactions (PPIs) mong the targets were surveyed This was based on an assumption that target s are to a greater extent likely to maintain PPIs with each other if they are truly associated within the alike(p) biological functions/pathways.A PPI meshwork of the ROS-diabetes targets was generated using the dough molecular Interactions (MIMI, http//mimi. ncibi. org/ webcite) database 20 and compared against light speed PPI networks of randomly wasted sets (the corresponding number of the ROS-diabetes target set) from HUGO. A standard Z-test and one sample T-test were apply to calculate the statistical significance of the ROS- diabetes PPI network with respect to the random PPI networks. in operation(p) enrichment analysis Literature derived ROS-diabetes targets (by SciMiner and NCBI Gene2PubMed) were subject to functional enrichment analyses to identify significantly over-represented biological functions in terms of Gene Ontology 21, pathways (Kyoto Encyclopedia of Genes and Genomes (KEGG, http//www. genome. p/kegg/ webcite) 22 and Reactome http//www. reactome. org/ webcite23). Fishers exact test 24 was used to calculate the statistical significance of these biological functions with BenJamini-Hochberg (BH) familiarised p-value 0. 5 25 as the cut-off. Over-represented ROS-diabetes targets Defining priming coat corpora To identify a subset of targets that are highly over-represented within the ROS- diabetes targets, the frequency of each target ( defined as the number of documents in which the target was identify divided by the number of essence documents in the query) was compared against the frequencies in randomly selected mount corpora.Depending on how the mount set is defined, over-represented targets may digress astray therefore, to maintain the priming coat corpora close to the ROS and diabetes context, documents were selected from the akin Journal, volume, and issue f the 54 ROS-diabetes documents, alone were NOT indexed with responsive Oxygen SpeciesMeSH nor Diabetes MellitusMeSH. For example, one of the ROS-diabetes articles (PMID 18227068), was publish in the Journal of biologic Chemistry, Volume 283, Issue 16. This issue contained 85 paper, 78 of which were not indexed with either Reactive Oxygen SpeciesMeSH or Diabetes MellitusMeSH indexed.One of these 78 written document was randomly selected as a primer document. Three sets of 54 documents were selected using this procession and processed using SciMiner. Identified targets were confirmed by manual canvass for truth. Identifying significantly over-represented targets ROS-diabetes targets were tested for over-representation against targets identify from the tercet minimize sets. Fishers exact test was used to determine if the frequency of each target in the ROS-diabetes target set was significantly different from that of the scene sets. Any targets with a BH adjusted p-value < 0. 5 in at to the lowest degree two of the three comparisons were deemed to be an over-represented ROS- diabetes target. usable enrichment analyses were performed on these over- represented ROS-diabetes targets as describe above. Selecting targets tor real-time R A subset of targets were selected for RT-PCR from the peak 10 over-represented ROS- diabetes targets excluding insulin and NADPH oxidase 5 (NOX5), which does not have a mouse ortholog. Nitric oxide synthase 1 (NOSI), the main generator of nitrous oxide, ranked at the 1 5th power and was additionally selected for inclusion in the test set.Differential gene looking by real-time RT-PCR Mice DBA/2J mice were purchased from the Jackson Laboratory (Bar Harbor, ME). Mice were housed in a pathogen-free environment and cared for following the University of Michigan Committee on the Care and Use of Animals guidelines. Mice were cater AIN76A chow (Research Diets, New Brunswick, NJ). Male mice were used for this study. Induction of diabetes deuce treatment groups were defined control (n = 4) and diabetic (n = 4). Diabetes was induced at 13 weeks of age by low-dose streptozotocin (STZ) in jections, 50 mg/kg/day for five consecutive days.All diabetic mice received LinBit prolong release insulin implants (LinShin, Toronto, Canada) at 8 weeks post-STZ treatment. Insulin implants were replaced every 4 weeks, at 12 and 16 weeks post-STZ treatment. At 20 weeks post-STZ treatment, mice were euthanized by sodium pentobarbital overdose and DRG were harvested as previously described 26. Real-time RT-PCR The gene carriage of the selected nine literature-derived ROS-diabetes targets in DRG was measured using real-time RT-PCR in duplicate.The amount of mRNA isolated from each DRG was normalized to an endogenous recognition Tbp TATA box binding protein A bout threshold (CT). Identification of ROS-diabetes targets A total of 1,021 laughable targets were identified by SciMiner from the 1,154 ROS- diabetes papers defined by the query of (Reactive Oxygen SpeciesMeSH AND Diabetes MellitusMeSH) and confirmed by manual review. prorogue 1 contains the op 10 intimately oftentime s mentioned targets in the ROS-diabetes papers. Insulin was the around shoply mentioned target, followed by superoxide dismutase 1 and catalase. instrument panel 1 .Top 10 about frequent ROS-diabetes targets The NCBI Gene2PubMed database, containing expert-curated associations between the NCBI Gene and PubMed databases, revealed 90 unique genes associated with the 54 ROS-diabetes papers ( redundant appoint 1). SciMiner identified 85 out of these 90 targets, indicating a 94% think of rate. Five targets preoccupied by SciMiner were added to the initial ROS-diabetes target set to result in 1,026 unique targets ( special rouse 2). Additional tile 1. The list ot 90 genes trom the NCBI Gene2PubMed database tor the ROS-Diabetes literature (1 , 1 54 papers). fix up XLS surface 35KB transfer bill This blame can be viewed with Microsoft outdo Vieweropen entropy Additional saddle 2. The list of 1,026 ROS-Diabetes targets. stage XLS surface 229KB transfer accuse This wedge c an be viewed with Microsoft leap out Vieweropen entropy PPI network of the ROS-diabetes targets The PPI network among the ROS-diabetes targets was evaluated using MIMI interaction data. This was based on the assumption that targets commonly related to certain national are much likely to have frequent interactions with each other.One hundred PPI networks were generated for comparison using the aforesaid(prenominal) number of genes (1,026) randomly selected from the complete HUGO gene set (25,254). The PPI network of the ROS-diabetes targets was significantly different from the randomly generated networks indicating their strong association with the egress ROS and Diabetes. Table 2 demonstrates that the hatch number of targets with any PPI interaction in the randomly generated target sets was 528. 9 (approximately 52% of 1,026 targets), slice the number of targets with any PPI interaction in the ROS- iabetes target was 983 (96%).The number of targets interacting with each other was also significantly different between the random networks (mean = 155. 4) and the ROS-diabetes network (mean = 879). Figure 1 illustrates the distributions of these measurements from the nose candy random networks with the ROS-diabetes set show as a red just line. It is obvious that the PPI network of the ROS-diabetes targets is significantly different from the random networks. Table 2. Summary of degree centigrade randomly generated PPI networks thumbnailFigure 1 . Histograms of randomly generated PPI networks.The histograms llustrate the distributions of 100 randomly generated networks, piece the red line indicates the ROS-diabetes targets. The network of the ROS-diabetes targets is significantly different from the 100 randomly generated networks, indicating the overlap of ROS-diabetes targets with respect to the topic Reactive Oxygen Species and Diabetes. Functional enrichment analyses of the ROS-diabetes targets Functional enrichment analyses of the 1,026 ROS-diabetes targets were performed to identify over-represented biological functions of the ROS-diabetes targets.After BenJamini-Hochberg correction, a total of 189 molecular functions, 450 biological rocesses, 73 cellular components and 341 pathways were significantly enriched in the ROS-diabetes targets when compared against all the HUGO genes (see Additional Files 3, 4, 5 and 6 for the full lists). Table 3 lists the top 3 approximately over-represented GO terms and pathways ranked by p-values of Fishers exact test e. g. , apoptosis, oxidoreductase activity and insulin signaling pathway. Additional burden 3. The enriched Molecular Functions Gene Ontology Terms in the 1,026 ROS-Diabetes targets. set XLS surface 91 KB Download institutionalise This show can be viewed with Microsoft Excel Vieweropen info Additional record 4. The nriched Biological Processes Gene Ontology Terms in the 1,026 ROS-Diabetes targets. Format XLS surface 95KB Download appoint This tile can be viewed wit Micros ott Excel Vieweropen info Additional tile enriched Cellular Components Gene Ontology Terms in the 1,026 ROS-Diabetes targets. Format XLS Size 61 KB Download file This file can be viewed with Microsoft Excel Vieweropen data Additional file 6. The enriched pathways in the 1,026 ROS-Diabetes targets.Format XLS Size 104KB Download file This file can be viewed with Microsoft Excel Vieweropen data Table 3. Enriched functions of 1,026 ROS-diabetes targets Identification of over-represented ROS-diabetes targets To identify the ROS-diabetes targets highly over-represented in ROS-diabetes literature, three sets of background corpora of the same size (n = 1 , 1 54 documents) were generated using the same Journal, volume and issue come along. The overlap among the three background sets in terms of documents and identified targets are illustrated in Figure 2.Approximately 90% of the selected background documents were unique to the individual set, temporary hookup 50% of the identified targe ts were identified in at least(prenominal)(prenominal) one of the three background document sets. The frequencies of the identified targets were compared among the background sets for significant differences. no(prenominal) of the targets had a BH adjusted p-value 0. 05, indicating no significant difference among the targets from the three different background sets (See Additional File 7). thumbnailFigure 2. Venn diagrams of document compositions and identified targets of the randomly generated background sets.Approximately 90% of the selected background documents were unique to individual set (A), while 50% of the identified targets were identified in at least one of the three background document sets (B). Additional file 7. Comparisons of target frequencies among three background sets. Format XLS Size 22KB Download file This file can be viewed with Microsoft Excel Vieweropen Data Comparisons of the ROS-diabetes targets against these background sets revealed 53 highly over- rep resented ROS-diabetes targets as listed in Table 4.These 53 targets were significant (p-value 0. 05) against all three background sets and significant following BenJamini-Hochberg multiple examen correction (BH adjusted p-value 0. 05) against at least two of the three background sets. SODI was the most over-represented in he ROS-diabetes targets. Table 4. 53 targets over-represented in ROS-diabetes literature Functional enrichment analyses of the over-represented ROS-diabetes targets Functional enrichment analyses of the 53 ROS-diabetes targets were performed to identify over- represented biological functions.Following BenJamini-Hochberg correction, a total of 65 molecular functions, 209 biological processes, 26 cellular components and 108 pathways were significantly over-represented when compared against all the HUGO genes (see Additional Files 8, 9, 10 and 11 for the full lists). Table 5 shows the top 3 ost significantly over-represented GO terms and pathways ranked by p-values of Fishers exact test. GO terms related to oxidative nisus such as superoxide metabolic process, superoxide release, negatron carrier activity and mitochondrion were highly over-represented 53 ROS-diabetes targets Additional file 8.The enriched Molecular Functions Gene Ontology Terms in the Over- represented 53 ROS-Diabetes targets. Format XLS Size 46KB Download file This file can be viewed with Microsoft Excel Vieweropen Data Additional file 9. The enriched Biological Processes Gene Ontology Terms in the Over-represented 53 ROS- Diabetes targets. Format XLS Size 95KB Download file This file can be viewed with Microsoft Excel Vieweropen Data Additional file 10. The enriched Cellular Components Gene Ontology Terms in the Over-represented 53 ROS-Diabetes targets.Format XLS Size 66KB Download file This file can be viewed with Microsoft Excel Vieweropen Data Additional file 1 1 . The enriched pathways in the Over-represented 53 ROS-Diabetes targets. Format XLS Size 75KB Download file This file can be viewed with Microsoft Excel Vieweropen Data Table 5. Enriched functions of the 53 over-represented targets in diabetes Gene carriage change in iabetes Two groups of DBA/2J mice exhibited significantly different levels of glycosylated hemoglobin (%GHb). The mean ? SEM were 6. 2 ? 0. for the non-diabetic control group and for 14. 0 ? 0. 8 for the diabetic group (p-value < 0. 001), fact mood of prolonged hyperglycemia in the diabetic group 26. DRG were harvested from these animals for gene facial expression assays. lodge genes were selected from the top ranked ROS-diabetes targets superoxide dismutase 1 (Sodl), catalase (Cat), xanthine dehydrogenase (Xdh), protein kinase C alpha (Prkca), neutrophil cytosolic broker 1 Ncfl), nitric oxide synthase 3 (Nos3), superoxide dismutase 2 (Sod2), cytochrome b-245 alpha (Cyba), and nitric oxide synthase 1 (Nosl).Eight genes exhibited differential expression between diabetic and non-diabetic mice (p-value < 0. 05) as show n in Figure 3. Cat, Sodl, Sod2, Prkca, and NOSI expression levels were decreased, while Ncfl , Xdh, and Cyba expression levels were increased in diabetes. thumbnailFigure 3. Gene expression levels of selected ROS-diabetes targets in DRG examined by real-time RT-PCR. Expression levels are relative to Tbp, an internal control (error bar = SEM) (*, p < 0. 05 **, p < 0. 01 ***, p < 0. 01). Eight (Cat, Sodl, Ncfl , Xdh, Sod2, Cyba, Prkca, and Nosl) out of the nine selected ROS-diabetes genes were significantly regulated by diabetes. Discussion Reactive oxygen species (ROS) are products of normal energy metabolism and symbolize important roles in many other biological processes such as the resistant response and signaling cascades 4-6. As mediators of cellular damage, ROS are implicated in pathogenesis of multiple diseases including diabetic complications 27-30.With the aid of literature mining technology, we collected 1 ,026 achievable ROS-related targets from a set of biomedica l literature indexed with both ROS and diabetes. Fifty-three targets were significantly over-represented in the ROS-diabetes papers when compared against three background sets. Depending on how the background set is defined, the over-represented targets may vary widely. An ideal background set would be the entire PubMed set however, this is not realistic due to limited access to tull texts and concentrated data processing.An alternative method wou d be to use only abstracts in PubMed, but this may not full represent the literature. Using only the abstracts, our target identification method resulted in 21 (39%) of the 53 key ROS- iabetes targets (Additional File 12), suggesting the public assistance of rich information in full text documents. In the present study, background documents were randomly selected from the same Journal, volume, and issue of the 54 ROS-diabetes documents, which were not indexed with Reactive Oxygen SpeciesMeSH nor Diabetes MellitusMeSH.This approach maint ained the background corpora not take from the ROS and diabetes context. Additional file 12. The Key 53 ROS-Diabetes Targets Identifiable Using Only the Abstracts. Format XLS Size 23KB Download file This file can be viewed with Microsoft Excel Vieweropen Data The gene expression evels of nine targets selected from the 53 over-represented ROS-diabetes targets were measured in diabetic and non-diabetic DRG. Our testing ground is particularly interested in deciphering the vestigial mechanisms of diabetic neuropathy, a major complication of diabetes.Data published by our laboratory both in vitro and in vivo confirm the shun impact of oxidative stress in complication-prone neuron tissues like DRG In an swither to obtain diabetic neuropathy specific targets, SciMiner was active to further analyze a subset of the ROS-diabetes papers (data not shown). Nerve growth factor in (nerve growth factor) was identified as the most over- epresented target in this subset when compared to the full ROS-diabetes set however, NGF did not have statistical significance (BH adjusted p-value = 0. 06). The relatively small numbers racket of papers and associated targets may have contributed to this non-significance.Therefore, the candidate targets for gene expression validation were selected from among the 53 over-represented ROS-diabetes targets derived from the full ROS-diabetes corpus. Among the tested genes, the expression levels of Cat, Sodl , Sod2, Prkca, and NOSI were decreased, while the expression levels of Ncfl , Xdh, and Cyba were increased nder diabetic conditions. Cat, Sodl , and Sod2 are responsible for protecting cells from oxidative stress by destroying superoxides and hydrogen peroxides 8-11. Decreased expression of these genes may result in oxidative stress 32.Increased expression of Cyba and Ncfl , subunits of superoxide-generating nicotinamide adenine dinucleotide phosphate (NADPH) oxidase complex 30, also supports enhanced oxidative stress. Xdh and its inte r-convertible form, Xanthine oxidase (Xod), showed increased activity in dissimilar rat tissues down the stairs oxidative stress conditions ith diabetes 33, and also showed increased expression in diabetic DRG in the current study. Unlike the above concordant genes, protein kinase C and nitric oxide synthases did not exhibit predicted expression changes in diabetes.Protein kinase C activates NADPH oxidase, further promoting oxidative stress in the cell 34,35. Decreased expression of Prkca in our diabetic DRG is not parallel with expression levels of other enzymes expected to increase oxidative stress. Between the two nitric oxide synthases tested in the present study, NOSI ( nervous) expression was significantly decreased (p-value < 0. 01) in diabetes, while Nos3 (endothelial) expression was not significant (p-value = 0. 06). The neuronal NOSI is expected to play a major role in producing nitric oxide, other type of highly reactive free radical.Thus, with some exceptions, the m ajority of the differentially denotative genes in DRG show parallel results to the known activities of these targets in diabetes, suggesting enhanced oxidative stress in the diabetic DRG. discernment of antioxidant enzyme expression in diabetes has yielded a variety of results 36-40 depending upon the duration of diabetes, the tissue studied and other factors. In diabetic mice and rats, it is commonly reported that superoxide dismutases are down-regulated 37-40, where data regarding catalase are protean 36,40.PKC is activated in diabetes, but most papers that examined mRNA demonstrated that its expression is largely unchanged 41. Among the 53 over-represented ROS-diabetes targets, SODI was the most over- represented and was differentially expressed under diabetic and non-diabetic conditions. To the best of our knowledge, no published study has investigated the role of SODI in the approach and/or progression of diabetic neuropathy. Mutations of SODI have long been associated wit h the inherited form of amyotrophic squinty sclerosis (ALS) 42 and the theory of oxidative stress-based maturement 43.Early reports indicate that knockout of the SODI gene does not affect nervous system development 44, although recovery following injury is obtuse and incomplete 45,46. With respect to diabetes, SODI KO accelerates the development of diabetic nephropathy 47 and cataract formation 48. Thus, examining the SODI KO mouse as a form of diabetic neuropathy would be a liable follow-up study. One limitation of the current approach using literature mining technology is incorrect r missed identification of the mentioned targets within the literature.Based on a performance evaluation using a standard text set BioCreAtlvE (Critical Assessment of Information Extraction systems in Biology) reading 2 49, SciMiner achieved 87. 1% recall (percentage identification of targets in the given text), 71. 3% preciseness (percentage the true of identified target) and 75. 8% F-measure (h armonious bonny of recall and precision = (2 x recall x precision)/(recall + precision)) before manual revisal 14. In order to improve the accuracy of SciMiners results, each target was anually reviewed and corrected by checking the sentences in which each target was identified.Approximately, cxx targets (10% of the initially identified targets from the ROS-diabetes papers) were removed during the manual review process. The overall accuracy is expected to improve through the review process however, the review process did not address targets missed by SciMiner, since we did not thoroughly review individual papers. Instead, 5 missed targets, whose associations with ROS-diabetes literature were available in the NCBI Gene2PubMed database, were added to the final ROS-diabetes target list (Additional File 2).