dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2010). These antibodies are assessed for specificity, quantification and sensitivity (dynamic range) in their application for protein extracts from cultured cells or tumor tissue. Antibodies are labeled as validated and used with caution based on degree of validation by criteria previously described  (Hennessy et al. , 2010). RPPA arrays were quantitated and processed (including normalization and load controlling) as described previously, using MicroVigene (VigeneTech, Inc. , Carlisle, MA) and the R package SuperCurve (version-1. 3), available at http://bioinformatics. mdanderson. org/ OOMPA  (Hu et al. , 2007; Tibes et al. , 2006). Raw data (level 1), SuperCurve nonparameteric model fitting on a single array (level 2), and loading corrected data (level 3) were deposited at the DCC. Data Normalization-We performed median centering across all the antibodies for each sample to correct for sample loading differences.
Cluster of Cluster Analysis-Cluster of Cluster Analysis (CoCA) was performed using data from methylation, miRNA, mRNA, copy number and RPPA platforms using a fuzzy clustering approach. Data matrix that was used to do platform specific subtype clustering and platform specific cluster assignments were used to generate centroids for each cluster. Membership of each sample to each cluster was then obtained by correlation of each sample values to centroid of cluster. This correlation matrix was used for consensus clustering. Consensus clustering was performed using R package ConsensusClusterPlus_1. 24. 01, with 90% resampling for 1000 iterations of hierarchical clustering based on pearson correlation distance  (Monti et al. , 2003; Wilkerson and Hayes, 2010). Kaplan Meier survival estimates for CoCA clusters were estimated using R package survival_2.
For each filter, we then calculated a reads-per-million (RPM) abundance metric as: Abundance metric = #reads mapped to the microbe #reads mapped to human in the sample * 10 6 To detect genomic integration of specific viruses we performed de novo assembly of RNAseq and DNA-seq sequence data with ABySS v1. 3. 4  (Simpson et al. , 2009) , using for each library the reads classified by BBT as human, the virus, multi-match and no match. We then merged the k-mer assemblies for each library with Trans-ABySS v1. 4. 8  (Robertson et al. , 2010)  to generate the working contig set. We re-ran BBT on these contigs, applying only human and specific virus filters, identifying contigs that matched to both filters. We identified any integration breakpoints in such multi-matched contigs by using BLAT v34  (Kent, 2002)  to align each contig to the human GRCh37/hg19 reference sequence, and to virus reference sequences.
, 2009). The PARADIGM graphical model represents such entities as nodes and generates an integrated pathway activity (IPA) for each entity of a patient. A gene IPA score, for example, refers to the final active protein inferred from copy number, expression, and signaling from other genes in the pathway. Here we use PARADIGM to generate IPA scores for each of the 117 TET patients. We clustered PARADIGM IPAs using the UCSC consensus clustering RData script v. 1. 0. 0 available though medbook. ucsc. edu. These PARADIGM inputs were merged real IPAs to be clustered by samples (patients). The clustering conditions used a k-means algorithm, average final linkage, and 500 repetitions with a k-max of 10. Clustered heatmaps of patient IPAs were graphed with attribute color assignments. As a quality check, we computed a silhouette score for each k to measure goodness of fit for each patient in a cluster.
Multi-Center Mutation Calling UCSC-Single nucleotide somatic mutations were identified by RADIA (RNA AND DNA Integrated Analysis), a method that combines the patient matched normal and tumor DNA whole exome sequencing (DNA-WES) with the tumor RNA sequencing (RNA-Seq) for somatic mutation detection  (Radenbaugh et al. , 2014)  (software available at: https:// github. com/aradenbaugh/radia/). The inclusion of the RNA-Seq data in RADIA increases the power to detect somatic mutations, especially at low DNA allelic frequencies. By integrating the DNA and RNA, mutations that would be missed by traditional mutation calling algorithms that only examine the DNA can be rescued back. RADIA classifies somatic mutations into 3 categories depending on the read support from the DNA and RNA: 1) DNA calls -mutations that had high support in the DNA, 2) RNA Confirmation calls -mutations that had high support in both the DNA and RNA, 3) RNA Rescue calls -mutations that had high support in the RNA and weak support in the DNA.
SNV Caller combination and filtering First, Samtools calls were retained if they met all of the following rules inspired by MAQ: Site is greater than 10bp from a predicted indel of quality 50 or greater. The maximum mapping quality at the site is ≥ 40. Fewer than 3 SNV calls in a 10 bp window around the site. Site is covered by at least 3 reads and less than 1,000,000,000 reads. Consensus quality ≥ 20. SNP quality ≥ 20. After these filters were applied, Samtools and SomaticSniper calls were unioned using joinx v1. 9 (https://github. com/genome/joinx; joinx sort -stable -unique). The resulting merged set of variants were additionally filtered to remove likely false positives2,4. We used bamreadcount v0. 4 (https://github. com/genome/bam-readcount) with a minimum base quality of 15 (-b 15) to generate metrics and retained sites based on the following requirements: Minimum variant base frequency at the site of 5%.
BC Cancer Agency Strelka Variant Caller: Strelka  (Saunders et al. , 2012) (v1. 0. 6 ) was used to identify somatic single nucleotide variants, and short insertions and deletions from the TCGA THYM exome dataset. All parameters were set to defaults, with the exception of "isSkipDepthFilters", which was set to 1 in order to skip depth filtration given the higher coverage in exome datasets. 123 pairs of libraries were analyzed. When a blood sample was available, it served as the matched normal specimen; otherwise, the matched normal tissue was used. The variants were subsequently annotated using SnpEff  (Cingolani et al. , 2012) , and the COSMIC (v61)  (Forbes et al. , 2010)  and dbSNP (v137)  (Smigielski et al. , 2000)  databases.
Acknowledgments This work was supported by  National Institutes of Health (NIH)  grants to The  Cancer Genome Atlas (TCGA)  project as follows:  U54 HG003273  ( Gibbs, Richard A. );  U54 HG003067  ( Gabriel, Stacey; Lander, Eric S. );  U54 HG003079  (Wilson, Richard K. );  U24 CA143799  (Speed, Terence Paul; Spellman, Paul T);  U24 CA143835  (Shmulevich, I);  U24 CA143840  ( Landanyi, Marc; Sander, Chris );  U24 CA143843  ( Gibbs, Richard A. ; Wheeler, David Andrew );  U24 CA143845  ( Chin, Lynda; Getz, Gad );  U24 CA143848  ( Hayes, David N; Perou, Charles M. );  U24 CA143858  (Stuart, Joshua;  Benz Christopher; Haussler , David H. );  U24 CA143866  ( Marra ,  Marco Antonio );  U24 CA143867  (Gabriel, Stacey; Meyerson, Matthew L. );  U24 CA143882  (Baylin,  Stephen B. ; Laird , Peter W. );  U24 CA143883  (Mills,  Gordon B.
targetscan. org/vert_71/ MapSplice 0. 7. 4 (Wang et al. , 2010b) http://www. netlab. uky. edu/p/bioinfo/MapSplice2 V2_MapSpliceRSEM workflow TCGA https://tcga-data. nci. nih. gov/tcgafiles/ftp_auth/distro_ftpusers/anonymous/tumor/thym/cgcc/unc. edu/illuminahiseq_rnaseqv2/rnaseqv2/unc. edu_THYM. IlluminaHiSeq_RNASeqV2. mage-tab. 1. 0. 0/DESCRIPTION. txt survival R package N/A https://www. rdocumentation. org/packages/survival/versions/2. 41-2 DAVID annotation database (Huang et al. , 2007a; Huang et al. , 2007b) https://david. ncifcrf. gov/ ClaNC R package (Dabney, 2006) http://www. stat. tamu. edu/~adabney/clanc/ VirusSeq (Chen et al. , 2013).
Mining association rules is an significant research area in Knowledge Extraction. Although the negative association rules have notable advantages, but they are less explored in comparaison with the positive association rules. In this paper, we propose a new approach allowing the mining of positive and negative rules. We define an efficient method of support counting, called reduction-access-database. Moreover, all the frequent itemsets can be obtained in a single scan over the whole database. As for the generating of interesting association rules, we introduce a new efficient technique, called reduction-rules-space. Therefore, only half of the candidate rules have to be studied. Some experiments will be conducted into such reference databases to complete our study.
Besides, the current approaches  [10, 11, 15, 16, 18]  are limited on the Apriori's data structure and support-confidence pair. While, this data structure imposes the repetitive accesses over the database, which can be costly. In addition, the support-confidence pair is questionable: (i) finding frequent itemsets is very complex in large databases and/or for low minimum support threshold; (ii) the number of rules that can be reduced nevertheless remains high that many prove uninteresting. In order to exceed these notable limits, we propose an efficient approach for mining positive and negative association rules using a new pair, support-M GK. We introduce a new economical technique of support counting, called reduction-access-database, based on the new data structure MatrixSupport and generator concepts.
Therefore, a simple pass allows us to extract all the frequent itemsets over the whole database. As for association rules generating, we introduce an efficient method, called reduction-rules-space, partitioning the search space rules. Therefore, only half of the candidate rules are to study. Bazed on these optimizations, we also propose Erapn algorithm, less consumer in memory. We present the experimental evaluation conducted with databases from the literature by showing performances compared to semantically close approach such that RAPN algorithm  [14]  and Wu's algorithm  [19]. The rest of this paper is organized as follows. Section 2 introduces the formal concepts. Section 3 details our approach. Section 4 summarizes our experimental results. Section 5 reviews the related work. A conclusion is given in Section 6.
Mining Positive and Negative Association Rules In this section, we introduce our approach for mining positive and negative association rules. It describes in a double problematic: finding frequent itemsets and generating potential valid association rules based on the previously extracted frequent itemsets. The first problem is often complex (in the worst case, it reaches 2 |I| ) and dramatic when one considers the negative items. With the small database from Table  1 , we have 1024 different items instead of 32 positive. The second problem is also complex (for an m-itemset, we have 5 m -2(3 m ) + 1 instead of 3 m -2 m+1 + 1). From Table  1 , we have 2640 different rules instead of 180 classical rules. In these dimensions, it is necessary to select only a part. In  [5, 8] , we have initiated the solution, which will be refined in Subsections 3.
Mining frequent itemsets: Reduction-Access-Database This is based on two steps: finding (in a single scans) frequent 1 and 2-itemsets, and frequent k-itemsets (k ≥ 3). After that first step, frequent 2-itemsets are used to generate candidate 3-itemsets. The process continues until no more candidate can be generated. Given a minsup, finding the set of frequent itemsets F, defined: F = {X ⊆ I|X = ∅ ∧ supp(X) ≥ minsup}. ( 1 ) As noted, mining frequent itemets is very complex. The worst case concerns the small itemsets (1 and 2-itemsets). To answer this, we develop a new data structure MatrixSupport. The following Table  3  describes its formalism on the small database from Table  1. It is a projection of database B in relation to its attributes. The idea is to acquire data as the structure develops and store it.
Algorithm 1 Eomf: Frequent Itemset Mining Require: A dataset B = (T , I, R), a minimum support minsup. Ensure: All frequent itemsets F. 1: MatriceSupport ← Scan(B); //Scan dataset B 2: F1 ← {c1 ∈ MatriceSupport|supp(c1) ≥ minsup}; //Generate 1-itemsets 3: F2 ← {c2 ∈ MatriceSupport|supp(c2) ≥ minsup}; //Generate 2-itemsets 4: for (k = 3; F k-1 = ∅; k + +) do 5: C k ← Eomf-Gen(F k-1 ); //New candidate (see algorithm 2).
2). A join between the elements of F k-1 is then made (algo. 2 lines 2 to 6). Indeed, two p and q items of F k-1 form a c if, and only if they contain common (k -2)-itemsets. For example, joining ABC and ABD gives ABCD. However, joining ABC and CDE does not work because they do not contain common 2-itemsets. Once C k has been established, it researches among the elements of C k. If this is the case, it calculates the support in two cases (algo. 1 lines 9 to 13): if c is generator, an access to the database is made to know its support (algo. 1 line 10), otherwise, it is derived from its subsets without going through the database (algo. 1 line 12). The support is then increased (algo. 1 line 14). And, only frequent itemsets are retained in F k (algo. 1 line 17). For the sake of comprehension, we apply this algorithm 1 on a small database B, shown in Table  1.
The minimum support is equal to 2/6, where Gen. designates a generator itemset. Results are shown in Fig. 1. After reading the dataset B, D is not frequent, its support is smaller than From this example, our approach does it in a single pass to the database, this is not the case for the existing ones, they do it in 4 passes. The following algorithm 3 embodies the different optimizations we have defined in above Subsection 3. 2. The algorithm 3 takes as argument a set F, thresholds minsup and minmgk, and returns a set E RAP N. It is initialized by the empty set in line 1. Next, for each itemset of F set, the set A is gererated (line 3). For each subset X k-1 of A (line 4), the algorithm proceeds in two recursive steps. The first consists in generating attractive class rules using the single.
Experimental results This section presents the experimental study conducted in order to evaluate the performances of our algorithm. The latter is implemented in R and tested on PC Core i3 and 4GB of RAM running under Windows system. We compare the results with those of Wu and RAPN, conducted out on four databases from UCI, such as Adult, German, Income and Iris. For each algorithm, we have chosen the same thresholds to avoid biasing the results. The following table  6  reports the characteristics of datasets, and the number of positive and negative rules by varying the minimum thresholds minsup and minmgk. Indeed, the first three columns indicate the data characteristics in question, the last fiveteen columns present the different results, where the column labelled "++" corresponds to the type X → Y , column "-+" to X → Y , column "+-" to X → Y , and "--" to X → Y.
The behaviour of algorithms varies according to data characteristics. The large database is much more time-consuming to run. In other words, the number of rules increases as thresholds decrease. Except for dense databases (Adult and German) and relatively low thresholds (minsup = 1% et minmgk = 60%), the number of rules (see Table  6 ) in Wu is 100581 and 89378 for RAPN. They are relatively large, due to the strong contribution of positive rules of type X → Y and negative rules of type X → Y (see table  6 ), than for Erapn (28784 rules). The rules of type X → Y and X → Y remain reasonable for each algorithm. On less dense databases (Income and Iris), these algorithm gives the reasonable number of rules. Note that RAPN, for Iris data, does not extract the type X → Y (see Table  6 ) for minsup (resp.
minmgk) over 3% (resp. 80%). Figure  2  below shows the response times by varying the minupp and keeping minmgk = 60%. They also increase when thresholds are lowered. The execution time of Erapn is faster than that of Wu and RAPN. Erapn gained 7 more times the best response in the worst cases. These different performances can be explained as follows. RAPN and Wu are limited on classical data structure, which requires repetitive access over the whole database. Wu has the lowest performance. One of the main reasons lies in the pruning technique. In this case, the interest measure does not have effective properties for frequent itemsets mining. In addition, the search space of valid association rules can be covered exhaustively. To this, our algorithm introduces the different optimizations. Therefore, the all frequent itemsets can be traversed only once.
Related Work Association rules mining is an active topic in Big Data. Apriori algorithm  [2]  is the first model that deals with this topic. On the other hand, it scans database multiple times as long as large frequent itemsets are generated. Apriori TID algorithm  [2]  generates candidate itemset before database is scanned with the help of Apriori-Gen function. Database is scanned only first time to count support, rather than scanning database it scans candidate itemsets. Despite their notable contributions, Apriori and Apriori TID  [2]  algorithms are limited on a single of classical (or positive) association rules. The negative association rules has not been studied. To this, outreach works has been proposed. Brin et al. [10]  propose a model generating negative association rules by using the χ 2 measure.
In  [16] , the authors propose a new algorithm SRM (substitution rules mining) for mining only negative association of the type X → Y. Although effective, SRM algorithm is limited of this only type. In  [11] , the authors propose the PNAR algorithm. Although obtaining notable contributions, PNAR suffers the high volume of results, due to support-confidence pair wase used. Wilhelmiina proposes the Kingfisher algorithm  [18]  using the Fisher test. A notable limitation of this lies in the computation of p-value imposing exhaustive passes over the whole database, which gives the high computational time. Guillaume and Papon  [14]  propose RAPN algorithm based on support-confidence pair and other measure, M G (M GK  [13]  modified). Although effective, RAPN suffers relatively the high computational cost on the search space frequent itemsets.
Note that the major handicap of these works stems mainly from the computational costs for frequent itemsets mining (repetitive passes over the whole database) and association rules mining (exhaustive passes over the search space). Recently, we proposed a new algorithm, Eomf  [5] , allowing the extraction of frequent itemsets. Therefore, a single pass over the database will extract all frequent itemsets, which significantly reduces the costs of calculation. As for association rules mining, we introduced in  [7, 8]  a new approach allowing the extraction of positive and negative association rules using a new pair, support-M GK. As a result, only half of all candidate rules are studied, which also reduces the search space significantly. In this paper, we combine our works  [5, 7, 8]. Ameliorations have been made, especially in terms of accuracy and simplicity.
In  [5] , the path of frequent itemsets space has been quite heavy: the non-generator itemsets are implicitly taken twice for each calculation step, which can be costly. This gap has been corrected in the current work. We introduced a new strategy of the search space via a notable property (cf. property 1) exploiting the monotony concepts of the generator itemsets, which consequently reduces the cost. Therefore, improvements have been added in algorithm 1 (lines 4 to 16), which makes the approach robust. In  [7, 8] , we used the parameter vc α (r) = 1 n n-n X n X n Y n-n Y χ 2 (α), to prune association rules. Nevertheless, this parameter presents a notable limit. It requires the exhaustive paths over the whole database to know its values for each candidate association rule, i. e. for a m-itemset, computable in O(2 m ) on its contingency table, it gives 4C k m 2 k , tradionally cost, for all k.
This parameter is not very selective, its sometimes eliminates the interesting rules (or robust), but considers the unintersing rules (far from the logical implication), because, of its critical value. In this paper, we try to close this limit using a simple parameter, minmgk ∈ [0, 1], that does not require access to the database. In addition, we introduced effective properties for the search space (cf. propositions 7 to 10).
Sample size This implementation research study uses a census methodology rather than a representative sample. The programmatic intervention aims to reach all individuals within the catchment area population given the research performance site's care mandate to serve this population. All individuals in the catchment area population, as defined under study site above, who meet the inclusion criteria will be approached for inclusion into the programmatic intervention, for which their de-identified clinical data will be analyzed towards the primary aim. The sample size is determined by the overall population covered in the two districts of 300,000, (rather than calculated via a sample size formula) within which we anticipate approximately 8000 deliveries per year or 12,000 during the first 18 months of the study during which enrollment will occur.
Quantitative data All data for evaluating effectiveness outcomes (Aim One) and implementation outcomes (Aim Two) will originate from Possible's CommCare system. At the community level, data are entered via the CommCare mobile application. The CommCare system automatically syncs data in near-real time on secure cloud servers. In addition, CommCare allows Possible to audit and track changes made in any record. All quantitative data will be collected during program implementation, and baseline/endline measurements will signify the start/endpoints of the intervention in a particular village cluster as determined by stepped-wise implementation. All data for evaluating implementation measures related to content protocol fidelity, participation, and coverage will originate either from the CHW CommCare application or from CHN observer checklists that are filled out by a supervising CHN.
In addition to KIIs and FGDs, ideals expressed in interviews and group discussions will be observed and compared to actual practice. The third phase consists of managing, organizing, and analyzing the qualitative data collected in phases one and two to develop a comprehensive list of strengths, weaknesses, and opportunities. The conclusions of these different methods will be used to help plan for future implementations of similar interventions. All qualitative data will be stored on a Research Electronic Data Capture (REDCap) database. REDCap is a secure, web-based application designed to support data capture for research studies, providing (1) an intuitive interface for validated data entry, (2) audit trails for tracking data manipulation and export procedures, (3) automated export procedures for seamless data downloads to common statistical packages, and (4) procedures for importing data from external sources.
REDCap user access will be defined so that researchers only have access to de-identified study data. Qualitative data will be reviewed after they are aggregated on a weekly basis following data collection events. Once data are stored into REDCap, all paper copies of data form will be stored in a locked cabinet within a locked room in Bayalpata Hospital or Charikot Hospital. Once all data are fully inputted and validated for quality, all paper copies will be destroyed. REDCap data will be deleted 1 year after study end.