dataesr/scientific-paragraphs-categorization huggingface dataset:
No written instruction was given to them since they were directed by an operator sitting next to them. The Kinect camera was positioned in front of the monitor, under the screen (See Figure  7 ). Subjects's face distance from the Kinect was kept around 65cm, which is the optimal distance suggested by the FaceShift software. A mirror was provided to help users during the task. The operator, using the laptop, assisted the subject during the calibration and provided guidance during the task. The operator was able to monitor the behaviour and the facial expression of the subject by looking the Kinect color camera preview on his screen. Before starting the capture, subjects were assisted by the operator in configuring the FaceShift software. During the configuration, the FaceShift control window was shown on the screen of the subjects.
Starting from a neutral expression, after a short countdown, subjects had 15 seconds to reproduce the facial expression and go back to a neutral expression. The mirror was helping the subjects to fix the desired expression on their own face before applying it to the virtual character. Professional animators do the same: they explore different stances in front of a small mirror before authoring a facial pose using their traditional manual animation rig. The six reference videos, displaying the six Ekman's emotions  [1] , belong to the DaFEx database of dynamic facial expressions  [27]  (Actor 1, high intensity). The videos show a professional female actress. High intensity can be considered as mild. For each subject the experiment lasted between 30 and 40 minutes: 8 to 10 minutes were needed for FaceShift configuration, the remaining time for the recordings.
Introduction Genome-wide association studies (GWAS) have generated huge datasets in the past 8 years in order to find association between genetic polymorphisms and phenotypes. Individual risk prediction based on those discoveries was promising. Nevertheless, genetic architecture of complex diseases, such as type II diabetes, is still largely misunderstood  (Vassy et al. , 2014). Indeed, gene-environment and gene-gene interactions must be considered to better understand etiology of such phenotypes. In other words, various joint effects of genetic variations, namely epistasis, are likely to partly determine the disease state  (Mackay and Moore, 2014). While common genome-wide association analysis checks for potential SNP-disease associations in a one-SNP-at-a-time fashion, looking for all potential epistatic interactions in such datasets will quickly result in combinatorial overload.
This is why classical GWAS often left behind the daunting task of epistasis detection. Several strategies came up to overcome the epistasis intricacy. After a first section dealing with epistasis generalities, we will present in this review the main categories of methods dedicated to epistasis detection. These methods are classified as follows. First, some exhaustive approaches for searching significant genetic marker combinations will be introduced. As some of these, like Multi-Dimensional Reduction (MDR), are not manageable at a genome-wide scale, we will next turn our attention to filtering strategies which aim at reducing the size of the dataset, thereby decreasing the size of the search space. A final section will deal with machine learning and data mining techniques. This review does not intend to provide an exhaustive list of all software programs designed to find epistatic interactions, but rather to give an overview of the main categories of strategies put forward in the last 5 years.
Though the overall complexity is linear with the number of individuals in the studied population, it becomes exponential when the interaction order increases. In 2-way interactions, this complexity corresponds to quadratic complexity. The number of combinations to be tested within a dataset containing 1 million SNPs is tremendous: 5 × 10 11 pairwise interactions, 1. 7 × 10 17 3-way interactions, 4. 2 × 10 22 4-way interactions, 8. 3 × 10 27 5way interactions, and so on  (Ritchie, 2015). Hence, an exhaustive search of epistatic interactions of order 3 or more would lead to a computational burden too prohibitive. Finally, the third challenge is the interpretation of the analytical results. To interpret statistical results biologically is not straightforward, for statistical interaction does not automatically entails interaction at the biological or mechanistic level  (Cordell, 2002).
Parametric Regression Methods Traditionally, the most common framework for exploring GWAS data is parametric regression models. A parametric algorithm has a fixed number of parameters that has to be estimated from the data, and relies on strong assumptions about the probability distribution generating the data. This class of algorithms makes accurate predictions when those assumptions are sufficiently close to reality, but performs badly when proved incorrect. Logistic regression (see Box 1) has been widely used as a parametric method for exhaustive search of interactions in association analysis. For example, software PLINK  (Purcell et al. , 2007)  has implemented logistic regression models to detect epistasis. But, in high dimensional data, parameter estimation is a costly and non-accurate procedure that introduces large standard errors because sample sizes are too small compared to genome-wide data size.
As a consequence, many false positives are generated when dealing with such data. To overcome this problem, p-values are usually corrected with Bonferroni multiple-test correction (see Box 2). This correction being overly conservative, only interactions with very strong effects will be detected and many other interactions will be missed. Hence, the logistic regression strategy has been widely portrayed as unsuitable for handling genome-wide datasets  (Cordell, 2009; Moore and Williams, 2009; Steen, 2012). Highly related to standard regression methods, penalized regression techniques, such as the LASSO (least absolute shrinkage and selection operator) or SCAD (smoothly clipped absolute deviation) gained some popularity to detect SNP-SNP interactions. However, those techniques are restricted to two-way interactions and are still BOX 2 | Bonferroni correction.
Bitwise Representation of Data and Likelihood Ratio-based Testing We will introduce the Boolean operation-based testing and screening (BOOST) software program to exemplify this section. Designed to be fast, BOOST runs an exhaustive analysis of all potential pairwise SNP-SNP interactions  (Wan et al. , 2010). The main feature of BOOST is to build contingency tables and use them to calculate log-likelihood ratios for evaluating interaction effects. For two SNPs, a contingency table is a 3 × 3 matrix displaying the frequency distribution of all nine possible genotypes (Figure  1B ). However, computing all potential contingency tables at a genome-wide scale is a timeconsuming process. In fact, there are as many contingency tables as there are pairwise interactions to test (see Section Challenges in Epistasis Detection).
In order to boost the procedure in terms of time and space efficiency, GWAS data is first transformed in a binary way. In usual data representation, each row symbolizes a SNP and each column symbolizes a subject (Figure  3A ). In binary representation, each SNP is depicted by three rows, each of them describing the genotype status (i. e. , 0, 1, or 2), and two columns depict cases and controls subjects respectively (Figure  3B ). Each table cell contains a bit string where each bit represents one subject and its genotype: 1 if it corresponds to the genotype status encoded by the current row, 0 otherwise. Even if the binary matrix seems three times larger than the usual one, its space usage is smaller because one bit is an eighth of a byte, and bytes are the usual units (i. e. , non binary) used for storing information.
That representation also sticks closer to machinelanguage, which means that building a contingency table from it only involves fast bitwise (i. e. , Boolean) operations. Once contingency tables are constructed, the program is ready to test for pairwise interactions. The way to detect epistasis complies with Fisher's epistasis definition (see Section Biological Epistasis and Statistical Epistasis) since authors look for a difference between the independent effect model (i. e. , marginal effects) and the model which includes both marginal and interaction effects. In other words, for each SNP pair, BOOST tests for a departure from the linear additive model. Under the assumption of equivalence between a logistic regression model and its corresponding log-linear model  (Agresti, 2002) , this departure is expressed in terms of log-likelihoods.
GWIS is also designed to be fast (e. g. , faster than BOOST) and to scale up to datasets containing millions of SNPs. The BOOST and GWIS strategies are designed to run exhaustive genome-wide fast scans of epistatic interactions. However, they are restricted to the detection of interacting SNP pairs, which is a substantial limitation. All epistatic models assuming interaction with order greater than two will be missed by these two methods. In the next section, we present a technique that overcomes this problem and exhaustively looks for higher order epistasis.
A Full Combinatorial Approach Multifactor Dimensionality Reduction (MDR) is now a reference in the epistasis detection field. No parameters are estimated (i. e. , nonparametric) and no assumptions are made on the genetic model (i. e. , model-free) under this supervised classification approach. This strategy could detect interactions even when independent main effects are inexistent  (Ritchie et al. , 2001 (Ritchie et al. , , 2003;; Hahn et al. , 2003). It is not constrained to identification of pairwise interactions but also searches for higher order interactions  (Moore et al. , 2006). First, MDR partitions the dataset for cross-validation. By default, nine tenths of the dataset (training set) is used to build the model and the remaining tenth (testing set) is used to evaluate this model.
The proportion of illclassified affected and unaffected individuals is evaluated on the testing set (one tenth of the data). Finally, the prediction error of each model is estimated over the 10 iterations (Figure  4D ). The top best models over the 10-fold cross-validation are retained. As the main feature of MDR is to reduce the data dimension, it can easily be combined with other classification methods  (Moore and Andrews, 2015). This flexibility is also a good point to emphasize because since 2006, many extensions of MDR have been proposed so that it is applicable to quantitative traits  (Gui et al. , 2013). Besides, other variants of the MDR algorithm have been proposed that rely on parallel implementations to boost MDR computing time performance  (Bush et al. , 2006) , to handle missing data  (Namkung et al.
Filtering Based on Data Mining Techniques We will illustrate this category with the ReliefF method. ReliefF approach consists in learning informative features from the dataset without any a priori knowledge (Robnik-Šikonja and  Kononenko, 2003). The algorithm computes a proximity measure between individuals on the basis of genome-wide genetic similarity. The goal is to evaluate the quality of genetic variants according to how well their values distinguish individuals near to each other. The algorithm is quite simple (Figure  5 ). For each individual (noted I), the procedure determines the nearest individuals (i. e. , neighbors) sharing the same phenotype (set noted S for same), and also the nearest individuals that show up the opposite phenotype (set noted O for opposite). If I and S show different values for a marker, then this variant discriminates individuals having the same phenotype, thus decreasing its importance.
On the contrary, if I and O show different values for a marker, this variant discriminates individuals having different phenotypes, thereby its importance is increased. These steps are then repeated over a predefined number of individuals. Moore and coworkers showed in 2007 that ReliefF algorithm is scalable  (Moore and White, 2007). The popularity of ReliefF gave rise to several variations  (Kononenko, 1994 ) that we will quickly present below. RReliefF (Regressional ReliefF) was designed to study quantitative traits like eQTL epistasis  (Huang et al. , 2013). When applied to a genome-wide dataset, noisy genetic markers may be attributed too much weight, hence inflating their importance estimates. To alleviate this problem, TuRF (Tuned ReliefF) proposed to eliminate from the SNPs set considered for epistasis detection, SNPs with no or very low importance.
, 2009b). SURF generally takes into consideration much more neighbors than ReliefF, labeling 25-50% of all individuals as neighbors. So when applied to a GWAS dataset, SURF has higher power of detection than ReliefF, albeit this may become a cumbersome procedure. A latest variation, SURF *  (Greene et al. , 2010) , also considers information of farthest individuals to build importance scores. In terms of detection power of epistatic interactions, the performance of TuRF and ReliefF has been compared in  Moore and White (2007). ECRF has also been compared to ReliefF in  McKinney et al (2007). Finally, SURF has been compared to both ReliefF and TuRF in  Greene et al. (2009b). However, ECRF and SURF have not been compared to each other, as well as ECRF and TuRF. ECRF and TuRF show improved performance over ReliefF, whereas SURF and SURF* show improved performance over both ReliefF and TuRF.
Filtering Based on Data-integration Techniques Another research area advocates the use of knowledge from external databases, in order to select SNP groups that are relevant to the phenotype of interest  (Grady et al. , 2011). Even if this approach is hindered by a lack of epistasis understanding in complex organisms, it avoids the black box effect of data mining techniques that may hamper the interpretation of underlying biological mechanisms. One way to do that is to query information in online public protein-protein interaction databases like IntAct  (Kerrien et al. , 2012) , BioGRID  (Chatr-Aryamontri et al. , 2015) , STRING  (Franceschini et al. , 2013)  or ChEMBL  (Willighagen et al. , 2013). It is then possible to narrow all SNPs down to a reduced list of markers located in genes that encode for proteins involved in relevant interactions.
When markers are mapped to an interacting gene pair, tests are exhaustively conducted on interactions between each SNP of the first gene against each SNP of the second gene. Unfortunately, one would probably fail at discovering new biological models by selecting SNPs in such a direct way. A more promising strategy is to come up with a score for each SNP  (Ritchie, 2015) , based on assessed relative importance of the proteins encoded by the genomic region encompassing the SNP. Novel findings are within reach by running a prioritization scheme rather than a strict removal  (Pattin and Moore, 2008). Resorting to pathways is also interesting. For instance, this approach has already been applied with information drawn from pathways involved in lipid synthesis  (Ma et al. , 2015) , by including evidence from public databases like KEGG Pathway  (Kanehisa et al.
This would require a tool designed to simulate pathways and protein-protein interaction networks, and then simulate GWAS data where several SNPs are involved in these networks. Such a tool does not exist yet. Therefore, for this kind of filter based on comprehensive knowledge, we cannot properly and objectively assess its scientific relevance. One software program worth mentioning is Biofilter. It gathers information from 13 databases  (Pendergrass et al. , 2013a) , which contain experimental evidence of interaction, pathway or ontological similarity relationships. On the basis of biological plausibility, Biofilter models interactions that will be tested irrespective of the marginal effects. So it creates polygenic models, thanks to gene-disease and gene-gene connection knowledge  (Pendergrass et al.
, 2013b). The statistical and computational challenges are also addressed since not all combinations of interactions are examined. Statistical relevance is based on the statement that the more two genes are involved in a relationship, the more likely they are to share an important biological link  (Bush et al. , 2009). Although data-integration techniques yield meaningful and biologically relevant results, exploiting external information sources like pathways or protein-protein interaction networks is controversial. Online databases are incomplete and so is our understanding of biological pathways. Thus, making use of them to build filters would in most cases results in a flawed analysis. Moore and Hill recently recommended  (Moore and Hill, 2015)  to combine both the biased approach (from a biologist point of view) based on expert knowledge, and computational approaches solely driven by GWAS data (neither immune to bias from a statistician point of view).
Non-exhaustive Searches Enhanced by Artificial Intelligence Machine learning and combinatorial optimization represent alternatives to parametric statistical methods for detecting combinations of variants that are associated with a phenotype. Machine learning methods build non-parametric models to compile information further used for epistatic detection. Combinatorial optimization techniques consider a search space of solutions (i. e. , combinations of potentially interacting SNPs) and browse through this space to find the more relevant combinations. Heuristics are commonly used in these algorithms, especially when dealing with genome-wide datasets in search of higher order genetic interactions. Identification of classification variables and interactions between them which allows outcome prediction is a well-known hurdle addressed by the machine learning and data mining fields of artificial intelligence  (Cordell, 2009).
In such non-parametric models, precautions must be taken to avoid overfitting (see Box 3). It has to be noted that if the model complexity of the underlying genetic mechanisms is too high compared to the sample size, using non-parametric methods BOX 3 | Overfitting. The aim of machine learning is to explain a system by learning a model with a training dataset. But dataset's particularities result in an overly tuned model adjusted for very specific features  (Leinweber, 2007). In other words, overfitting happens when the training stage gives too much importance to the noise within data. Overfitting is detected when a simpler and more accurate model exists. However, identifying what to ignore in the overfitting model is a non-trivial task. Overfitting typically arises when model complexity is too high compared to the size of the training data.
, 2005)  and was applied to GWAS several times in the last 5 years with epiForest  (Jiang et al. , 2009) , random Jungle  (Schwarz et al. , 2010)  and SNPInterforest  (Yoshida and Koike, 2011). A classification tree is grown using the following steps  (Jiang et al. , 2009). First, a bootstrap sampling is performed from the GWAS dataset comprised of N individuals and M SNPs. It consists in randomly selecting, with replacement, N individuals from the N original individuals. Individuals not drawn are called out-of-the-bag (OOB) individuals. So a new dataset and an OOB set are created for each grown tree. Then a random feature selection is applied to construct each node of the tree. To do so, instead of considering all variables from the initial GWAS dataset, a random subset of variables is picked out without replacement.
A recursive data splitting procedure is next executed, such that a parent node results in two child nodes given a rule that leads to a better discrimination of the current set of individuals (from the parent node) with regards to the disease status. This discrimination score is measured as a goodness of split or a decrease in impurity i. The tree is then grown up to its largest extent. These previous steps are repeated until a forest is built (Figure  6 ). For each node, a so-called variable importance is assessed to evaluate its contribution to the trait either individually or via multi-way interactions with other predictor markers. In other words, variable importance represents weight approximating the causal effect of a predictor variable. There are several ways to measure variable importance  (Schwarz et al.
However, even if random forests reveal associations potentially pointing at interactions, they cannot make a distinction between a scenario of interacting SNPs and a scenario of several independent SNPs additively contributing to the phenotype. As a result, random forests are lacking clear interpretation. More recently, another tree assembling software program was developed: GWGGI  (Wei and Lu, 2014). It differs from the previous methods in two points. First, it uses a tree-growing algorithm which is more computationally efficient  (Lu et al. , 2012) : the standard variable selection procedure is replaced with a forward algorithm. The principle of a forward algorithm is to take into account previously selected variables. The novel variable identified is the one, when added to the previous set of variables, allowing for the most accurate prediction.
When applied to genetic data, variables are typically SNPs and phenotypic values. Bayesian networks offer an appealing and intuitive way to capture relationships existing between genetic markers and disease status. The structure learning of a Bayesian network amounts to a model selection problem. Because this learning is an NP-hard problem  (Chickering et al. , 2004) , specific techniques have to be used to reduce the computational burden. A famous Bayesian network-based software program called BEAM (Bayesian Epistasis Association Mapping)  (Zhang and Liu, 2007)  is often used as a Bayesian-based reference for performance comparisons. BEAM relies on a Markov Chain Monte Carlo (MCMC) algorithm to test iteratively each marker, conditional on the current status of other markers. For each marker, the algorithm outputs its posterior probability of association with disease.
Markers are then distributed into three groups: group 0 for markers unlinked with the phenotype, group 1 for SNPs that contribute independently to the phenotype (additive model) and group 2 for SNPs that influence the disease risk given particular allele combinations (epistasis model). After that partitioning phase, a B-statistic is used to further filter detected SNP groups. When the BEAM method was originally published, the B-statistic was a new alternative to the usual χ 2 test of association between a phenotype and a set of SNPs. A detailed explanation of its computation would require a much deeper presentation of BEAM, which is not the aim of this section. The interested reader is referred to  Zhang and Liu (2007)  for a comprehensive explanation of how to build a B-statistic. Although the B-statistic enables to get rid of expensive permutation tests, MCMC iterations make this method inadequate when handling datasets containing more than 500,000 genetic markers, which is now commonplace in GWAS studies.
The network is iteratively constructed and at each iteration, the current network structure goodness is assessed with a score function. The goal is to maximize this score. The score function is made of two terms that indicate how well the current structure fits the dataon the basis of a maximum likelihood ratio-and how complex the Bayesian network is. In  Han et al. (2012) , it has been shown through multiple simulations that the EpiBN software program seems to outperform BEAM in interactions detection power. A different but not less appealing Bayesian strategy is the Markov blanket-based method. It allows discovery of SNPs in the local pathway of the phenotype, also referred to as "local causal SNPs"  (Alekseyenko et al. , 2011). In the context of GWAS, this strategy is used to avoid the time-consuming training processes like tree-growing of random forests or structure learning of a full Bayesian network.