dataesr/scientific-paragraphs-categorization huggingface dataset:
Note that the results are relatively high in the unsupervised case, despite the fact that it relies solely on rough association data provided by the SNPedia knowledge base. The result of this approach outperforms by more than three times the baseline and the performance of the supervised approach is four times better than the baseline. Our non-optimized implementation of the perfectmatching attack took 2. 7 seconds (on average) to complete on a high-end laptop for n = 80. For the susceptibility score of Alzheimer's disease, the baseline score consists in the expected distance between the actual values of SNPs rs7412 and rs429358 of the target and the probabilistic distribution of these two SNPs computed over the same gender population as the target. For a database of size 10, the inaccuracy of the susceptibility score is divided by two between the baseline and our identification attack (dropping from 16% to 8% in the unsupervised case, and from 15% to 7%).
This demonstrates that the identification attack can be successfully used to infer private information from an individual's (identified) genotype, especially with small genomic databases for which the proportions of correct matches are relatively high. In the case of an identification attack that targets a single phenotype, the genotypes in the database can be sorted by decreasing order of likelihood. The matched genotype is the first in the sorted list. To gain insight into the performance of the identification attack, we look at the rank of the target individual's genotype in the sorted list, for different values of the size of the genomic database. The experimental probability density function and the cumulative distribution function of the rank are depicted in Figure  5 , for different sizes of the genomic database (i.
e. , {10,20,40}). It can be observed that in the cases where the target individual's genotype is not in first position, which corresponds to an incorrect match, it often appears at the very beginning of the sorted list of genotypes. For example, in the supervised case with a genomic database of size 20, the target individual's genotype appears in the first 2 elements of the list in 65% of the cases and in the first 5 (out of 20) in 86% of the cases. Again, the results are better in the supervised case, especially when the number of genotypes increase. We now consider the perfect matching attack in which the adversary tries to match n genotypes to n phenotypes. We first look at the success of the attack in terms of the number of phenotypes that are matched to the correct genotypes for different sizes of the genomicphenotypic database (as in Figure  4 ), and we show the results in Figure  6.
It can be observed that the performance is very similar to the one of the identification attack, with an even slightly higher proportion of correct matches. For instance, for databases of 80 genomes/phenotypes, the proportion of correct matches is around 16% in the supervised case vs. 8% in the unsupervised case. For a database size of 10, the proportion of correct matches is around 58% in the unsupervised case (65% in the supervised case). We see here, too, that the supervised approach outperforms the unsupervised attack, and we note that the more participants there are in the databases, the higher the success ratio (which tends to 2 when n increases) between the two approaches is. This means that the more participants there are in both databases, the more relevant and helpful is the supervised approach.
We plot the results, in the form of boxplots showing the first, second (i. e. , median) and third quartiles as well as the confidence intervals and the average (red solid line), in Figure  7 , for a genomic-phenotypic database of 10 individuals. The labels on the x-axis are cumulative: the label "Skin" means that the adversary knows the sex and the skin color of the target individuals. Note that in the unsupervised case, some of the traits are not used. It can be observed that the performance increases with the adversary's level of knowledge. We also notice that (quasi-)deterministic traits (e. g. , blood type) provide substantial improvements in the proportion of correct matches. In the supervised case, the adversary achieves a performance of almost 40% success with only visible phenotypic traits that can be observed on a picture.
The small decreases in performance in the unsupervised case are caused by the noise in our dataset and the limited reliable association information available on SNPedia. Finally, we note that in both unsupervised and supervised approaches, the proportion of correct matches with all phenotypes is around four times higher than the proportion with the sex information only (which constitutes a baseline). Finally, we analyze the distinguishability between individuals and its effect on the performance of the attack. To do so, we introduce the notion of dissimilarity between any two individuals. We express this dissimilarity as the minimum value between the Hamming distance on the phenotypes of the individuals and the Hamming distance on their genotypes. As there are fewer phenotypic traits than SNPs, this metric most often represents the dissimilarity between two phenotypes.
Our evaluation also brings up an important point that is that the more distinguishable (genetically or phenotypically) two individuals are, the more likely they are to be de-anonymized. This clearly means that the more SNP-trait associations there are, the more successful will the de-anonymization attacks will be. And it is very likely that, with the progress of genomic research, knowledge databases like SNPedia will grow in size and precision, thus enabling successful attacks against larger databases.
Limitations The purpose of our evaluation is to assess the potential of the attacks described in the paper. Although the results provided in the previous section are sufficient to demonstrate that the threat is real, our evaluation The stairstep lines represent the proportion of pairs of individuals (in our dataset) with a given distance. The supervised approach contains higher distance between two individuals because it considers more traits than the unsupervised one. and our datasets still have some limitations that we discuss below. A first limitation is the limited size of our dataset (although the size of the dataset used in our evaluation corresponds to practical attack scenarios), which is caused by the low quality of the OpenSNP dataset in terms of the proportion of specified phenotypic traits.
As part of future work, we intend to collect larger datasets and perform further experiments on them. Another limitation is the fact that, in our supervised approach, we estimated the conditional probabilities on the entire dataset. The main reason for this is that splitting the dataset into training and testing sets would have further reduced the number n of genotypes/phenotypes considered in the evaluation of the attack. As part of future work, we will study the effect of the training set size on the performance of the attack by taking a rigorous cross-validation approach in the supervised case. Moreover, in order to assess the extent to which the results of the supervised approach can be generalized to other datasets, we intend to collect multiple datasets and run the supervised attack, trained on a given dataset, on a different one.
e. , probabilistic dependencies between different SNPs of the same genome) between these subsets, so that the records of the different datasets cannot easily be matched. In this way the adversary could not link the various parts of genomic data with each other, thus dramatically reducing the performance of the deanonymization attacks. Nevertheless, any association study between phenotypes and genotypes could still be carried out, but on smaller parts of the genome. Another simple technique for thwarting deanonymization attacks is to selectively share the SNPs; in particular hide those related to phenotypic traits. By removing the SNPs associated with visible traits, we could already significantly decrease the risk of deanonymization attack that relies on pictures, e. g. , gathered on online social networks.
Related Work Anonymization was one of the first mechanisms proposed to protect the genomic privacy of participants in genetic databases. Unfortunately, the removal of quasiidentifying attributes (e. g. , birth date or ZIP code) has been proven ineffective for protecting the anonymity of participants in such databases  [12, 15, 16]. For instance, genomic variants on the Ychromosome being correlated with last names (for males), these can be inferred using public genealogy databases. With further effort (e. g. , using voter registration forms) the complete identity of the individual can also be revealed  [14]. Also, unique features in patient-location visit patterns in a distributed healthcare environment can be used in publicly available records to link the genomic data to the identity of the individuals  [25].
Wang et al. [39]  show a higher risk where individuals can actually be identified from a relatively small set of statistics such as those routinely published in GWAS papers. In particular, they show that the presence of an individual in the case group can be determined based upon the pairwise correlation (i. e. , linkage disequilibrium) among as few as a couple of hundred SNPs. Whereas the methodology introduced in  [17]  requires on the order of 10,000 SNPs (of the target individual), this new attack requires only on the order of hundreds of SNPs. In another recent study  [12] , Gitschier shows that a combination of information, from genealogical registries and a haplotype analysis of the Y-chromosome collected for the HapMap Project, enables the prediction of the last names of a number of individuals from the HapMap database.
Therefore, we can expect that the matching attack evaluated in this paper would become successful with many more individuals in the future. De-anonymization attacks have been used to jeopardize the anonymity of participants in other contexts. A de-anonymization attack against a large Netflix database successfully re-identified Netflix records of known users by relying upon IMDB as background knowledge  [27]. Narayanan and Shmatikov also propose de-anonymizing users of an online social network (Twitter in their case) by making use of another online social network (Flickr) as the source of auxiliary information  [28]. Another attack shows that location traces could be de-anonymized by relying upon the social graphs of the traces' owners  [33]. In the context of anonymous communication, perfect matching attacks have been proven effective to de-anonymize mixing rounds  [35].
Our work differs from previous work as it studies deanonymization attacks against genomic or phenotypic datasets by leveraging the associations between the genomic and phenotypic data. Moreover, it relies only upon the most common variants currently provided by the major direct-to-consumer genetic testing providers, thus upon the variants that are most accessible online. Finally, it provides a thorough evaluation of the factors that play a role in the success of the de-anonymization attacks, and it paves the way for more investigations on the risky relationship between genomic data and phenotypes.
Conclusion and Future Work In this work, we have thoroughly evaluated two new de-anonymization attacks, by making use of two types of background knowledge. We observe that the super-vised approach outperforms the unsupervised approach, and that the success ratio between the two approaches increases with the number of participants, tending to two with 80 participants. We also notice that the proportion of correct matches of our identification attack is three to eight times higher than the baseline. We have demonstrated a decrease of 50% of inference error on the predisposition to Alzheimer's disease with a database of size 10. We notice a slight and unexpected increase of correct matches in the perfect matching attack compared to the identification attack. In particular, the proportion of perfect correct matches reaches 16% with the supervised approach.
Our results clearly show the extent of the threat of de-anonymization attacks that rely upon genomic-phenotypic statistical relationships. Finally, our results demonstrate that the more distinguishable two individuals are, the more successful the perfect matching is. This leads us to conclude that the matching risk will continuously increase with the progress of genomic knowledge, which raises serious questions about the genomic privacy of participants in genomic datasets. We should also recall that, once an individual's genomic data is identified, the genomic privacy of all his close family members is also potentially threatened. In future work, we intend to enhance the matching algorithm and performance of the de-anonymization attacks by learning the conditional probabilities of traits given all their relevant SNPs together as well as the joint probabilities for correlated SNPs, in the supervised approach.
The science requirements of DES drove the construction of a new camera, the Dark Energy Survey camera (DECam), which had its first light in 2012 September and the survey has started in 2013 September. As part of the process of testing and validation of the DES Data Management (DESDM) system  (Mohr et al. 2012) , a series of detailed simulations have been designed to serve as a test-bench for the development of the pipelines and for verifying the scientific reach of the experimental channels. Each of these iterations of the simulations are dubbed 'Data Challenges' (DC). The simulation starts with the creation of galaxy catalogues stemming from an N-body simulation  (Busha et al. 2013 ) and detailed models of the Milky Way galaxy  (Rossetto et al. 2011)  for the star component. These are merged and fed to an image simulator which includes atmospheric and instrumental effects.
The resulting images serve as inputs for DESDM and are processed as the data will be: the code SEXTRACTOR  (Bertin & Arnouts 1996)  produces a catalogue of more than 300 parameters encapsulating information about each detected object. The most relevant features of these simulations for our study are as follows: (i) the seeing is introduced as a function of observing time; (ii) the galaxy shapes have been implemented using a Sérsic profile which matches the observed profile; (iii) the point spread function (PSF) takes into consideration the seeing for that time, the optics and the distortion as a function of separation from the optical axis. The results shown in this paper are based on the latest release (internal to the DES Collaboration) of simulated data, DC6, which covers approximately 140 deg 2 to the full DES depth, corresponding to about 10 nights of observations.
True Galaxies True stars Objects classified as galaxies N G M S Objects classified as stars M G N S One should note that there are some inefficiencies in the image pipeline, which are studied in DC6 and which we do not deal with in this analysis. Instead, we define the latter parameters with respect to the mock galaxy samples used to produce the image simulations. With real DES data, our results could be tested e. g. on Hubble Space Telescope (HST) data in the same fields.
To estimate C s (l), we use the same stellar catalogue as used for the DES simulated sky survey produced by  Busha et al. (2013). We then calculate C s (l) using the approach from Thomas, Abdalla & Lahav (2010) and an adaptation of the HEALPIX code  (Gorski et al. 2005). We estimate the C ij (l) and ∂C g mn (l) ∂pα terms using the same code and setup as for the WL case. Fig. 5  shows the systematic parameter shift induced by the stellar contamination, for each of the cosmological parameters of our model p α = {w o , w a , m , H, σ 8 , b , n s , b g }. In particular, for the equation-of-state parameters w o and w a , we find that we require f s ≤ 0. 015. This translates into the following requirement on p g = 1f s , the purity provided by the star/galaxy classifier: p g ≥ 98. 5 per cent. The requirement on star/galaxy separation in a DE experiment is dictated by the need to accurately measure w o and w a.
In many cases, using PCA for data reduction consists of selecting only the principal components with the highest variance and approximating the data by its projection on this smaller set of variables. This encompasses the assumption that the important information is represented by the components with the highest variances. In the case of star/galaxy separation, this assumption is too simplistic. Indeed, the class of an object is only one possible source of variance and high variance could also be due to differences between objects in a given class. Therefore, when looking for the most relevant components for star/galaxy separation, we need another criterion to quantify their aptitude to separate between the classes. We calculate the Fisher discriminant  (Fisher 1936)  for each of the new parameters, defined as the inter-class variance over the intra-class variance, F i = (X G,i -X S,i ) 2 σ 2 G,i + σ 2 S,i , ( 36 ) where X A,i is the empirical mean value of the ith parameter for class A and σ 2 A,i is its empirical variance.
As the network complexity is increased, the accuracy eventually converges so that no further improvement is gained by adding additional nodes. We chose a network architecture with an input layer of 15 parameters (or twenty, as explained in the next section) and two hidden layers of twenty nodes, which turns out to be sufficiently complex for such convergence to be achieved. Training on real data, as opposed to simulations, is preferable, yet more challenging. One option would be to use data from spacebased surveys, as in space the PSF is not affected by the seeing. Data from the HST could be used to train our tool for the real DES survey data.
For both the LSS and WL probes, it allows us to widen the range of both magnitude and completeness where the derived science requirements are achieved. For magnitudes fainter than 23, multi_class improves the purity achieved by spread_model by up to 12 per cent for galaxies and by up to 20 per cent for stars. DES began survey operations in 2013 September, and will be running for five years. Therefore, we should be able to test the results shown in this paper on real data in the near future. The faint magnitudes reached by this new classifier constitute an important asset, which should allow us to achieve the science requirements on star/galaxy separation in the next generation of wide-field photometric surveys.
Background: Transposable elements are mobile DNA repeat sequences, known to have high impact on genes, genome structure and evolution. This has stimulated broad interest in the detailed biological studies of transposable elements. Hence, we have developed an easy-to-use tool for the comparative analysis of the structural organization and functional relationships of transposable elements, to help understand their functional role in genomes. Results: We named our new software VisualTE and describe it here. VisualTE is a JAVA stand-alone graphical interface that allows users to visualize and analyze all occurrences of transposable element families in annotated genomes. VisualTE reads and extracts transposable elements and genomic information from annotation and repeat data. Result analyses are displayed in several graphical panels that include location and distribution on the chromosome, the occurrence of transposable elements in the genome, their size distribution, and neighboring genes' features and ontologies.
Background Transposable elements (TEs) are repeated DNA sequences that can represent a large fraction of the genomic DNA in eukaryotic species  [1]. The sequencing and annotation of complete prokaryotic and eukaryotic genomes has revealed the massive impact of TEs on genomic structure, evolution, and gene regulation  [2] [3] [4]. Currently, most bioinformatics tools related to transposable elements are TE databases that collect and organize TE families in genomes (e. g. Repbase  [5]  and ISFinder  [6]  for eukaryotic and prokaryotic TEs, respectively); or detection methods (e. g. RepeatMasker  [7] , Censor  [8] , Repet  [9] ) that look for TE copies in sequences. To our knowledge, the UCSC Genome Browser (genome. ucsc. edu/index. html) [10] , ENSEMBL site (www. ensembl. org/index. html) [11] , and DFAM database (www.
dfam. org) [12]  are the only Web browsers available that allow for the visualization and exploration of *Correspondence: talla@imm. cnrs. fr Aix Marseille Université, CNRS, LCB UMR 7283, 13402 Marseille, France TE annotations. These browsers can display TEs with different resolutions, but they do not permit analyses and comparisons of individual TE families and superfamilies. Moreover, these browsers do not display similarities of TEs compared with their consensus sequences, which is essential for dating different generations of TEs. Previously, we developed VisualRepbase (www. girinst. org/ downloads/software/)  [13] , a JAVA interface that browses for occurrences of TEs in annotated genomes based on their family name and their similarity to recognized consensus sequences, and allows the user to compare the age and the invasion origin of selected TEs.
However, VisualRepbase suffers from a limited number of available genomes due to infrequent updates of the background database. Furthermore, VisualRepbase, ENSEMBL, and the UCSC Genome Browsers do not show relationships between transposable elements and neighboring genes. Here, we describe a new stand-alone software named VisualTE that dynamically displays and analyzes occurrences of TE families within annotated genomes, based on TE similarity and size. VisualTE also exhibits TE relationships with neighboring gene features, as well as inter-and intra-chromosomal comparisons.
Implementation and input data VisualTE is written in the JAVA programming language (JAVA version 1. 7 or later). The downloadable version can be installed and run on any operating system, including Windows, MacOS, and Linux. VisualTE input data are divided into two categories: the annotation file (in Genbank and/or EMBL formats) and the repeat file. For the latter, VisualTE recognizes AB-BLAST  [14] , NCBI-BLAST  [15] , Censor  [16] , Repeat-Masker  [7] , and Repet  [9]  formats. Moreover, a VisualTE format has been defined for the annotation and repeat files (see Additional file 1). A TE neighboring gene is defined as the closest annotated gene located upstream or downstream of a selected TE. VisualTE needs a file named 'gene2go' that can be downloaded from the NCBI website (ftp://ftp. ncbi.
nlm. nih. gov/gene/DATA/) to analyze the Gene Ontology (GO) of these TE neighboring genes. VisualTE contains a TE superfamily information file that was extracted from the Repbase database (version 19. 04)  [5]  and the ISFinder database (January 2014 version)  [6]. Compared to VisualRepbase  [13] , VisualTE allows the input of any annotated sequence in the right format. For GO studies, the 148 generic GO categories ('GenericEBI') and the two first levels ('TreeLevel1' and 'TreeLevel2') of the GO hierarchical tree were extracted from the EBI (www. ebi. ac. uk/QuickGO/GMultiTerm#tab=chooseterms) and Gene Ontology (ftp://ftp. geneontology. org/ pub/go/ontology/go-basic. obo) websites, respectively. All of the information files from the Repbase, ISFinder, EBI, and GO websites will be regularly updated.
The complete Arabidopsis thaliana genome  [17]  in Genbank format and reference Repbase families were downloaded from the NCBI (ftp://ftp. ncbi. nlm. nih. gov/genomes/Arabidopsis_ thaliana/) and RepBase (www. girinst. org/repbase/) websites, respectively, for a case study. TE copies were identified using RepeatMasker  [7]  with default parameters.