dataesr/scientific-paragraphs-categorization huggingface dataset:
Functional enrichment analysis The gene ontology (GO) enrichment analysis was carried out using MSigDB v6. 2 web-based gene set investigation tool whereas the pathway enrichments were performed using WebGestalt. 29 Enrichment ratio (r) representing the proportion of genes overlapping with genes in a GO/ pathway category (k) compared to an expected value of k (k e ). The expected value of k was calculated as number of module genes (n) divided by total number of genes in the reference gene set (m) multiplied by number of genes in the GO/pathway category (j), therefore k e = (n/m) * j and r = k/k e. 25 In the WebGestalt analysis, background of 13,284 genes expressed in AA and LGA was used as reference, whereas in MSigDB analysis, all known human gene symbols (m = 45,956) were considered.
Module preservation Expression data for additional glioma subtypes, other epithelial cancers and healthy tissues were downloaded from various sources and processed as described in Supplementary Table  S6. Preservation of the differentially coexpressed modules in other datasets was determined by calculating the Z summary preservation score. 30 For Z summary scores > 10, module can be interpreted as preserved while Z summary scores < 2 suggest no preservation. Z summary scores between 2 and 10 indicate weak to moderate module preservation.
Cancer predisposition genes Cancer predisposition gene lists were retrieved from the literature. Genes in which germline mutations increase the risk of developing cancer (n = 114) were acquired from Supplementary Table  S1  in Rahman  33  and from the Catalogue Of Somatic Mutations In Cancer (COS-MIC) v76 Cancer Gene Census catalogue (n = 84) (http://cancer. sanger. ac. uk/census). 30 Additionally, a set of cancer-associated genes harboring rare germline truncation variants across 12 tumor types (n = 249) was attained from Supplementary Data 2 in Lu et al. 34  The statistical significance of overrepresentation of cancer predisposition and cancer-associated genes was calculated using Fisher's Exact test (FET) and the odds ratios (ORs) were aggregated into a summary estimate using the fixed effects model in the metafor R-package.
Transcription factor binding site enrichment To identify transcription factor (TF) binding sites (TFBS) overrepresented in the promoters of M2 module genes, two databases were used. WebGestalt  29  was accessed online at http://www. webgestalt. org. In parallel, the TRANS-FACâ database release 2017. 1. was queried, as experimental evidence of binding and regulation can be obtained in addition to TFregulatory site binding predictions. We employed the "FMatch" analysis to identify overrepresented TFs in the gene set list of interest. Best promoters supported with binding site defined À500 to +200 bp from the transcription start site were set as parameters.
Cell division cycle gene sets Full list of human cell division cycle genes was downloaded from GO database AmiGO v1. 8  36  accession GO:0007049 (http://amigo. geneontology. org/amigo) (n = 1,768). The rest of the cell cycle gene sets were obtained from the Molecular Signature Database (MSigDB v6. 2) (http://sof tware. broadinstitute. org/gsea/msigdb/index. jsp).
Literature-based discovery of FoxM1 and B-Myb targets FoxM1 and B-Myb binding data were collected from the literature. Chromatin immunoprecipitation (ChIP) data for FoxM1 binding peaks were retrieved from the Supplementary Table  S1   43  and Supplementary Table  S8  in Fisher et al. 44. ChIP peaks for B-Myb were obtained from deposited Supplementary Table  S2  in Sadasivam et al. 45  and processed ENCODE B-Myb binding data Supplementary Table  S8  in Fisher et al. 44  We considered gene a target if it appeared in two-thirds of FoxM1 binding peak lists and in 2/2 of B-Myb binding studies.
E2F2 ChIP-seq data analysis Raw ChIP-seq data were downloaded for Gene Expression Omnibus (GEO) database (accession number GSM1208606 for E2F2 and GSM1208813 for IgG control). 46 The reads were aligned to human genome NCBI GRCh38 using the bowtie aligner version 1. 0. 0 and prebuilt index file sourced from http://bowtie-bio. sourcef orge. net/manual. shtml#what-is-bowtie on 3/07/2017. Reads with more than one reportable alignment were omitted from further analysis. ChIP-seq quality was assessed by applying Phantompeakqualtools 46 version 1. 1 and the quality metrics of experiments satisfied the recommendations by ENCODE and modENCODE consortia. 47 Peaks were called using the Macs2 software version 2. 1. 1. , with False Discovery Rate ( FDR) cut-off of 0. 01 to call significant regions. 48 Called peaks annotated using the annotatePeak function from the ChIPseeker Rpackage.
Connectivity Map analysis Connectivity Map (CMap) is a public database containing genome-wide expression profiles for three human cancer cell lines (MCF7, PC3, HL60), 6 hours after treatment with different concentrations of ~1300 small chemical compounds. Raw. CEL files and pre-ranked data are available for download at https://portals. broadinstitute. org/cmap/. Expression was profiled using one of Human Affymetrix U133A platforms (HT_HG-U133A, HT_HG-U133A_EA, HG-U133A). Raw. CEL files were processed from probe to gene-level using custom (CDF) probe annotations to GENCODE gene ids. 50, 51 Background correction and normalization of expression were performed using RMA and variance stabilization normalization (vsn) methods as implemented in the affy R-package. 52, 53 To conduct differential expression analysis, assays with less than two control or drug treatment samples were excluded.
For ease of interpretation, GENCODE gene IDs were converted HUGO gene IDs. Differential expression was quantified using the limma package,  54  while accounting for batch effects within the model. To perform module enrichments for genes whose expression is modulated by drug treatment, we first looked for drugs that modulate at least five genes at 10% FDR in the dataset of interest. Subsequently, significance of the overlap of module genes with the list of genes up or down-regulated by the drug was calculated using one-tailed FET. P-values of enrichment were corrected for multiple testing using Benjamini-Hochberg FDR procedure.
Gene set enrichment analysis Gene set enrichment analysis (GSEA) was performed using GSEA Java desktop application by the Broad Institute (version 2. 2. 4). The significance of module M2 gene set enrichments in the extremities of ranked list with values ordered by -log 10 (P-value) 9 log 2 (fold change) was assessed by permuting the rank list 10,000 times. The ranked list was ordered from the most up-regulated to the most down-regulated genes so that positive normalized enrichment score would indicate overrepresentation of the gene set among the most down-regulated genes. GSEA of genes up-and down-regulated by resveratrol in different concentrations, the Hallmark Gene Set from MSigDB were used. 55.
Gene network and differential coexpression analysis in astrocytomas Transcriptional changes can be related to a disease state via two paradigms, the analysis of gene covariation (differential gene co-expression) and gene expression levels (differential gene expression). 57 In order to identify sets of genes differentially covarying in the progression of LGA, we selected supratentorial IDH1 mutant and 1p/19q euploid adult astrocytoma samples from the TCGA database (Supplementary Table  S1 ). The cohort included 49  LGA and 70 AA samples (Supplementary Fig. S1 ). We used DiffCoEx algorithm  25  to identify set of genes with differential co-expression between the tumor grades (see Methods), yielding two modules of significantly differentially co-expressed genes: M1 (n genes = 272) (Pvalue < 0.
The average expression of module M2 genes was significantly higher in AAs (Pvalue = 1. 32 9 10 À5 , Mann-Whitney U-test), while the average expression of M1 did not vary significantly between LGAs and AAs (P-value = 0. 60) (Supplementary Fig. S4 ). As co-expressed genes may share related biological functions,  58, 59  we investigated whether the two differentially coexpressed modules were enriched for genes relating to specific biological pathways or GO terms using web-based tools -WebGestalt  29  and MSigDB. 60 Module M1 genes were enriched for GO terms related to neuronal constituents such as "neuron part" (Enrichment ratio (r) = 11. 62, FDR = 1. 89 9 10 À63 ) and "synapse" (r = 14. 57, FDR = 3. 10 9 10 À52 ), while M2 was highly enriched for cell division genes such as GO term "cell cycle" (r = 16.
82, FDR = 3. 83 9 10 À160 ) (Fig. 2A  and 2 ) (Supplementary Tables  S4  and S5 ), suggesting a functional distinction between these two modules. In keeping with their functional specialization from the analysis of GO terms, M1 and M2 were highly enriched for biological pathways such as "neuronal" system (M1, r = 5. 46, FDR = 2. 98 9 10 À6 ) and "cell cycle" (M2, r = 17. 98, FDR = 1. 18 9 10 À68 ), respectively (Supplementary Fig. S5 ). Next, to provide evidence for the biological validity of modules M1 and M2, we undertook a series of comparative network analyses assessing the conservation of co-expression between module genes in unrelated gene expression datasets. We explored module preservation in other malignancies such as oligodendroglioma, IDH1-wild type astrocytoma, GBM, colon adenocarcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, as well as in healthy tissue with proliferative potential (colon, lung, liver), and normal cerebral cortex tissue (Supplementary Table  S6 ).
We used the module composite preservation statistic Z summary score, where Z summary scores >10 indicate strong module preservation, Z summary scores < 2 suggest no preservation and Z summary scores between 2 and 10 indicate weak to moderate module preservation. 30 Since module M1 genes were on average more highly co-expressed in LGA and M2 genes more strongly co-expressed in AA, the reference datasets to test module preservation were set to LGA for M1 and AA for M2. Taking this Z summary score approach, we found that co-expression of M1 was preserved only in neural tissue, in keeping with its functional specificity ("neuronal processes") (Table  1 ), while module M2 ("cell cycle") was highly preserved in proliferating tissues, including different types of cancers as well as renewing healthy tissue.
PPI from the STRING database  56  for the total 1,768 cell cycle genes defined by the GO biological process annotation (GO:0007049, n = 1,768), we found that the most highly connected cell cycle genes were significantly (P-value < 10 À4 ) overrepresented in module M2 (Supplementary Fig. S6A  and B ). These results highlight that the genes constituting M2 represent central hubs of the overall set of genes governing the cell cycle and point toward an important influence of M2 on this biological process.
92, 95% CI [2. 42-16. 21], FDR = 0. 001) and a significant overrepresentation of rare germline truncation variants identified in 249 cancer-associated genes across 4,034 cancer cases from TCGA  34  (OR = 4. 31, 95% CI [2. 08-8. 09], FDR = 0. 0003) (Fig. 2D ). The biological functions of the cancer predisposition genes overlapping M2 genes were related to cell division and DNA repair (Supplementary Table  S7 ), therefore the enrichment of these genes in module M2 is influenced by overrepresentation of cell cycle genes among module M2 genes. These results highlight that M2 is significantly enriched for cancer predisposing genes in excess of random expectation suggesting M2 captures molecular processes associated with the development and maintenance of cancer. Next, we explored the relationship between M2 expression in the human glioma tissue sample and patient survival.
We next explored the effect of copy number change (i. e. , gene dosage) on the genes presenting the alteration. We focused on the known oncogenes and TSG obtained from the COSMIC list of genes with somatic mutations causally implicated in cancer (Cancer Gene Census). 61 Out of 484 oncogenes and TSG from the COSMIC Cancer Gene Census database, 338 (69. 8%) were affected by CNVs in our sample set (based on the GISTIC analysis, see Methods). As not all CNVs induce aberrant activity of their gene products, we selected these oncogenes and TSG-altering CNVs, whose ploidy influences expression of that gene (functional copy number alterations, FCNAs). 63 Among the 338 oncogenes and TSGs altered by CNVs, 156 genes (46. 2%) were considered influenced by FCNAs, that is, their gene dosage had a significant (FDR < 0.
Transcriptional regulation of M2 gene network We first investigated whether the gene co-expression might result from the regulatory control of a common TF; this may be reflected by enrichment for TFBS sequences in a set of co-expressed genes. 69 Analysis for enrichment of TFBS among M2 genes using the web-based gene set enrichment analysis tools -WebGestalt  70  and the TRANSFACâ 71 database revealed an overrepresentation of TFBS in M2 for the E2F-family TFs (WebGestalt, FDR = 3. 42 9 10 À17 ) (TRANSFACâ, P-value = 2. 16 9 10 À4 ). The E2F-family of TFs has an established direct role in cell cycle control and loss of function in E2F-binding suppressor protein Rb leads to uncontrolled cell proliferation. 72, 73 Additionally, we observed enriched TFBS among M2 genes for CUX1 (Cut Like Homeobox 1), Myb-like factors and NF-Y (Nuclear Factor Y) TF family, which have been previously associated with the regulation of cell division  [74] [75] [76]  (Supplementary Table  S10 ).
In addition, this approach allowed us to investigate the relationship of this empirically derived tissue-specific regulome and module M2 (and M1) expression. ARACNe uses mutual information to infer statistical dependency between gene expression profiles of TFs and their target genes. Significant mutual information between a given TF and a gene's expression is interpreted as a functional relationship between the two. 79 We applied the ARACNe-AP algorithm to LGA and AA gene expression profiles combined and obtained a regulome with 1,293 regulators, 13,260 targets and 171,429 interactions (see Methods). In the second step, we used the Master Regulator Inference Algorithm (MaRInA)  52, 53  to prioritize TFs influencing the transcriptional programs associated with progressing glioma. The MaRInA method makes use of differentially expressed genes between two conditions (i.
06 9 10 À34 , FDR = 1. 74 9 10 À92 and FDR = 1. 10 9 10 À21 , respectively) (Supplementary Table  S12 ). A considerable proportion of ChIP-seq detected FoxM1, B-Myb, and E2F2 targets in the M2 module had been predicted to be positively regulated by these TFs in the MaRInA analysis (Fig. 5A-C ) (Supplementary Fig. S10 ). The large overlap between the predictions of MaRInA approach and experimentally detected target gene sets for the FoxM1 and B-Figure  4. Significant MRs determined by MaRInA analysis for the IDH1 mut astrocytoma-specific transcriptional signature (P-value < 0. 01). Each row of the plot corresponds to the MaRInA results obtained for the respective MR. The narrow heatmap column on the right illustrates the predicted MR activity, with red indicating an increase of activity in AA samples (positive NES) and blue indicating a decrease (negative NES).
The x-axis represents the AA-specific gene expression signature, rank-ordered from the most down-regulated to most up-regulated gene in AA, compared to LGA. The red and blue bar code lines correspond to the ARACNe-predicted target genes of a given MR. Positively regulated target genes are shown in red and negatively regulated target genes are shown in blue. Module M2 genes are noted as black lines. Increased activity of an MR is implied by the enrichment of its positive targets among the overexpressed genes in the AA signature and of its negative targets among the underexpressed genes in the AA signature. Conversely, decreased MR activity is indicated by the enrichment of its positive targets among the underexpressed genes in the AA signature and of its negative targets among the overexpressed genes in the AA signature.
We identified 26 proteins that bind one or all three of the MRs, among which several protein kinases (Fig. 6A ). We queried the Kinase Profiling Database (see Methods) to identify kinases strongly inhibited by resveratrol in low concentrations. We identified MELK as the only kinase, which activity was reduced over 50% by exposure to resveratrol concentrations as low as 1 lmol/L (Fig. 6B ). Serine-threonine kinase MELK has been shown to phosphorylate and activate FoxM1 in a PLK1-dependent manner. 88 We therefore propose MELK inhibition and subsequent dephosphorylation of FoxM1 as one of the potential mechanisms by which resveratrol could exert its effect on module M2.
Resveratrol treatment of human primary anaplastic astrocytoma cell cultures To validate the in silico predictions of drugs targeting M2 network from the analysis of drug perturbation data (above), we chose to study the of effect of resveratrol on M2 module genes in human low-passage primary anaplastic astrocytoma cell culture as resveratrol has not previously been implicated in prevention of glioma progression. We chose first to study resveratrol at 20 µmol/L dissolved in DMSO to match the concentrations and solvent used in the CMap database. We used low-passage IDH1 mutant human tumor-derived TB98 cells and exposed them to 20 µmol/L resveratrol or DMSO or left the cells untreated in triplicates for 24 h in vitro. Genome-wide transcriptional changes in response to exposure were captured using RNA-seq.
90 We therefore explored the transcriptional signature of TB98 cells following exposure to 2 nmol/L and 10 nmol/L resveratrol. Analysis of the change in expression of M2 using GSEA revealed that M2 remains highly significantly enriched (Pvalue < 10 À5 ) for genes down-regulated at both 2 nmol/L and 10 nmol/L resveratrol concentrations (Fig. 7A-B ) (Supplementary Fig. S12A, B ; Supplementary Tables  S15, S16 ). In particular, genes with expression peaks during G 2 to M phase of the cell cycle, such as TOP2A and PLK1 were consistently down-regulated after resveratrol exposure, including at nanomolar concentrations. Consistent with resveratrol acting on G 2 to M progression, using the hallmark gene set collection from the Molecular Signature Database,  91  we further confirmed that genes encoding G 2 M progression, E2F targets and MYC targets were consistently and significantly (FDR < 0.
In this study, we took advantage of publicly available glioma data from TCGA database. In order to reduce inter-patient variability, a paired study design, that is, analysis of tumor samples from the same patient before and after progression would be desirable. However, currently available paired studies  19, 96  lack a sufficient sample size to perform differential co-expression analysis. Using the TCGA dataset, we uncovered two modules of differentially co-expressed genes between low and high-grade IDH1-mutated and 1p/19q euploid astrocytomas (M1 and M2). Functionally, the modules are enriched for neuronal and cell cycle genes, respectively. M2 had higher intramodular gene correlation in AA compared to LGA and the average expression of M2 module was significantly increased in samples with loss of chromosome 10q -an event previously identified as prognostically unfavorable in 1p/19q euploid low-grade gliomas.
30eservation of differential co-expression networksPreservation of modules M1 and M2 in neural and non-neural malignant and non-cancerous tissues assessed by the composite network preservation statistic Z summary. 30Z summary scores >10 represent strong evidence for preservation, Z summary < 2 is interpreted as no preservation and Z summary 2-10 suggests weak to moderate preservation. LGA expression was used as the reference set to investigate M1 preservation and AA to explore module M2 preservation in other datasets.
Acknowledgments This study was supported by  Imperial College NIHR Biomedical Research Centre (BRC) Scheme , the  Brain Tumour Research Campaign (BTRC) ,  Brain Tumour Research (BTR) Centres of Excellence and Imperial College London President 's PhD Scholarship Programme. The  Imperial BRC Genomics Facility  has provided resources and support that have contributed to the research results reported within this paper. The authors thank  Simona Parinello  and  Lucy Brooks  ( University College London, UK ) for their assistance with in vitro experiments and valuable discussions.
Data Availability Statement Gene expression, somatic mutations, and CNV data in grade II and III gliomas were acquired from TCGA and are publicly available at Genomic Data Commons Data Portal: (https://portal. gdc. cancer. gov). Datasets used for module preservation analysis are also publicly available in TCGA and Gene Expression Omnibus (see Supplementary Table  S6 ). ChIP-seq data of E2F2 binding have been previously published and are available at GEO under accession GSE49402. RNA-seq data of astrocytoma cell line TB98 response to 20 µmol/L, 10 nmol/L and 2 nmol/ L resveratrol is available in GEO under accession GSE117620. ª 2019 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals, Inc on behalf of American Neurological Association.
4 18. 9 Neural Malignant TCGA Glioblastoma 23. 0 67. 1 Neural Malignant GEO Glioblastoma (GSE77530) 12. 8 29. 9 Neural Malignant GEO Glioblastoma (non-enhancing) (GSE59612) 30. 0 18. 9 Neural Malignant GEO Glioblastoma (enhancing) (GSE59612) 3. 7 45. 9 Nonneural Malignant TCGA Colon adenocarcinoma 0. 8 33. 8 Nonneural Malignant TCGA Liver hepatocellular carcinoma 1. 4 58. 8 Nonneural Malignant TCGA Lung adenocarcinoma 2. 0 101. 9 Nonneural Nonmalignant GTEx Colon 1. 1 15. 0 Nonneural Nonmalignant GTEx Lung 0. 5 16. 6 Nonneural Nonmalignant GTEx Liver 0. 1 6. 9 Neural Nonmalignant GTEx Cerebral cortex 11. 8 -1. 3.
We develop an algorithm for probabilistic linkage of de-identified research datasets at the patient level, when only diagnosis codes with discrepancies and no personal health identifiers such as name or date of birth are available. It relies on Bayesian modelling of binarized diagnosis codes, and provides a posterior probability of matching for each patient pair, while considering all the data at once. Both in our simulation study (using an administrative claims dataset for data generation) and in two real usecases linking patient electronic health records from a large tertiary care network, our method exhibits good performance and compares favourably to the standard baseline Fellegi-Sunter algorithm. We propose a scalable, fast and efficient open-source implementation in the ludic R package available on CRAN, which also includes the anonymized diagnosis code data from our real use-case.
Introduction An increasing quantity and variety of health data, including administrative claims data, electronic health records (EHR) data, and data generated from biomedical research studies, are becoming available for discovery research. For a subset of patients who contribute information to multiple datasets, synthesizing information from these multiple datasets may provide a more complete picture of the patients' health condition and enable more comprehensive population studies. For example, if research database A contains genomic data and a small set of phenotypic data and claims database B contains a complete list of diagnostic codes associated with patients' medical histories, linking databases A and B would enable researchers to investigate the role of genomics in a wide range of phenotypic conditions.