dataesr/scientific-paragraphs-categorization huggingface dataset:
7 mm mislocalization across the brain. In contrast to our simulation approach, which was designed to explore the effects of different parameters on localization error,  Mah et al. used  all data available in a large database to quantify localization error. Their approach was to calculate a vector showing the displacement of the identified center of gravity from each voxel lesioned in more than four subjects. Thus, the magnitude of mislocalization they identified represents the best case scenario using standard lesion mapping approaches. Making their model more similar to ours (that a critical region was damaged if 20% of its voxels were affected, and further assigning a deficit probability of 90%) increased the mean displacement slightly. As we have shown, if the number of subjects with a lesion and a deficit is smaller, the mean displacement is likely to be larger and the probability of finding a significant association where one exists is reduced.
We develop a novel approach for incorporating expert rules into Bayesian networks for classification of Mycobacterium tuberculosis complex (MTBC) clades. The proposed knowledge-based Bayesian network (KBBN) treats sets of expert rules as prior distributions on the classes. Unlike prior knowledge-based support vector machine approaches which require rules expressed as polyhedral sets, KBBN directly incorporates the rules without any modification. KBBN uses data to refine rule-based classifiers when the rule set is incomplete or ambiguous. We develop a predictive KBBN model for 69 MTBC clades found in the SITVIT international collection. We validate the approach using two testbeds that model knowledge of the MTBC obtained from two different experts and large DNA fingerprint databases to predict MTBC genetic clades and sublineages.
Large databases of spoligotypes have been collected. Each spoligotype for a strain is determined by the presence or absence of 43 specific spacers in the DR region, producing a 43-bit number. Each spacer separates two direct repeats. These strains have been assigned sublineage labels using mixed expert-based and bioinformatics approaches derived from visual rules applied to spoligotypes as shown in Figure  1. These visual rules are based on the identification of characteristic deletions of one or more adjacent spacers in spoligotypes. Certain inferred mutations (deletions of blocks of adjacent spacers) in progenitor strains are considered to be lineage defining  [5]. These deletions are conserved in all descendent strains since studies have shown that the mechanism of mutation observed in the direct repeat (DR) region involves loss of spacers, and spacers are rarely gained  [6].
Additionally, spoligotyping is based on polymorphisms in a single locus, the DR region, and therefore has the potential for convergent evolution. Relying on specific subsequences within the spoligotypes for the study of genetic diversity is hence error prone. This paper presents a hierarchical probabilistic graphical model, the knowledge-based Bayesian network (KBBN), that encodes rules of thumb and large training databases to classify data into given classes. Expert knowledge is modeled in the top level of variables in the BN representing the rules. The middle level variables represent the class and the lower level represents various features of interest. KBBN uses the strategy of not directly modeling the dependency of the rules on the features. This greatly reduces the model parameter space which helps reduce the amount of data required for training while capturing the knowledge in the rules.
Thus we do extensive experiments on SITVIT as well as an additional testbed: CDC. The CDC test set consists of data and rules from the United States Centers for Disease Control and Prevention. This paper is organized as follows. We first examine prior Bayesian networks for MTBC classification and then introduce the KBBN approach to incorporate rules. We then examine the rules of thumb and data associated with MTBC clades. We present results for the KBBN-SITVIT model and assess its accuracy. Finally, computational studies examine how KBBN can improve accuracy over Bayesian networks on the two KBBN testbeds (SITVIT and CDC). The results show that KBBN is quite resilient to incomplete, inaccurate, or ambiguous rules and can obtain better performance than BN using less data. Previously in the MTBC domain, other approaches to incorporate advice in the form of rules have been shown to improve discriminative learning models of MTBC major lineages and other problems  [9].
Knowledge-Based Bayesian Network. The knowledgebased Bayesian network (KBBN) is a hierarchical probabilistic graphical model which encodes the knowledge obtained from expert-defined rules derived from true observations with large databases to solve classification problems. KBBN incorporates the rules of thumb as high level variables or class priors in the Bayesian network and therefore combines the information obtained from rules of thumb with the information provided by a BN model specifically designed for the domain. The method is designed and tested on widely used simple BNs such as naïve Bayes and polytrees with polynomial time learning and inference algorithms. The KBBN for the tuberculosis domain uses CBN as its base and is used for both CDC and SITVIT testbeds. KBBN, represented in Figure  2 𝑃 (𝐶, 𝑆 Ω , 𝑅 Ψ ) = ∏ 𝑗∈Ω ( ∑ 𝐻 𝑗∈{0,1} 𝑃 (𝑆 𝑗 | 𝐻 𝑗 ) 𝑃 (𝐻 𝑗 | 𝐶)) × 𝑃 (𝐶 | 𝑅 Ψ ) 𝑃 (𝑅 Ψ ) , (2) where the random variable  represents the sublineage class, the random variable  Ω = {  |  ∈ Ω} with Ω = {1,.
CDC-Sublineage and CDC Rules. The second dataset, CDC-Sublineage, examines 1286 MTBC isolates genotyped by spoligotyping and labeled with 8 sublineages. Dr. Lauren Cowan of the CDC was interviewed to obtain 8 rules of thumb. The data is a subset of 31,482 MTBC isolates genotyped by spoligotyping and 12-locus mycobacterial interspersed repetitive units (MIRU) typing with known lineages from a set collected by the CDC as part of routine TB surveillance in the United States from 2004 to 2009. Since only spoligotypes are used in the rules, the data for training were restricted to spoligotypes with labeled sublineages. There are 8 rules expressed as numeric formulas based on the 43 spacers in the spoligotypes. For example, the rule for the Indo-Oceanic sublineage Manila (or EAI2-Manila in SpolDB4 rules) is If absence of spacers 3, 20, 21, 29-32, 34, sum(spacers33-36) > 0 and presence of spacers 2, 4, 19, 22, then Indo-Oceanic Manila.
There is one rule per sublineage. This dataset was preprocessed by adding an array of 8 bits, one bit per rule. The value of a bit was set to 1 if the rule was fired and zero otherwise. Note that the sublineage sizes are unequal. Overall, the minimum sublineage size was 39, the maximum sublineage size was 356, and the median was 138 records. The rules were ambiguous and no precedence was imposed. In some cases no rules fire for a record. A maximum of 2 rules is fired for each record. The mode of the number of rules fired per record was also 2. If multiple rules fire for a record and the sublineages determined conflict or if no rules fire, the record is considered to be misclassified. Details of the CDC rules can be found in Supplement 2.
Model Validation. The next set of experiments evaluates the effectiveness of the KBBN approach with respect to other techniques and the effectiveness of incorporating rules. All experiments were done on both the SITVIT and CDC datasets to ensure that the results are not an artifact of a single dataset. For each dataset, first we used 10-fold stratified cross validation. Each training set was divided into 10 parts with 9 parts available as the training data for creation of models and 1 part held out as an independent test set. For all experiments the same test sets were employed, but the training dataset or the set of rules used may be varied. The accuracy of the results was measured using the -measure on the testing data (harmonic mean of precision and recall) averaged over the classes. To facilitate a fair comparison, the data were constructed so that there are at least 10 records per class.
In the SITVIT domain, this required removing clades that do not commonly infect human beings (e. g. , PINI1 and PINI2). We refer to this subset of the SITVIT-Train dataset as SITVIT-CV. SITVIT-CV had 45 classes and 2593 records. The minimum sublineage size was 11, and the maximum sublineage size was 390 with a mode of 21 records.
Comparison with Other Techniques. We designed several sets of experiments on the two datasets SITVIT-CV and CDC-Sublineage to determine the following: if incorporating rules improved the performance of the Bayesian network over the performance of the BN or rules alone. The results were gathered for KBBN, BN, and the rules used alone. In addition, linear and nonlinear SVM results were provided as a baseline for comparison. The SVM implementation in WEKA (http://www. cs. waikato. ac. nz/ml/weka/) was used. The SVM kernels and parameters were selected using a grid search of 9fold cross validated accuracy of the training set. The degreethree polynomial kernel and radial basis function kernels were found to work best. All SVM data was normalized  before training. Also, we are interested in the nature of the misclassification because it tells us about the potential inaccuracies in the definition of the lineages.
Effectiveness of Rules in Bayesian Network. Next, we designed several sets of experiments to determine the following: how the quality and quantity of rules and data affected the performance of KBBN. The basic underlying experimental design was the same for experiments across the two testbeds. Our hypothesis was that KBBN can learn the concept faster with less data by adding rules. We wanted to show that rules can improve learning especially where you have less data. For each dataset, first we used 10-fold stratified cross validation. Next each training set was divided into 9 parts providing models using 1/9, 2/9,. or 9/9 of the training set and tested on the corresponding test set. The test sets were kept the same for different training set sizes. We measured the amount of -value for different training set sizes with or without the rules and compared the result with the case of using no data at all (i.
Quality of Rules KBBN can provide us with information about the quality of each rule. We studied posterior probabilities of rules given the class to provide insight into the utility and accuracy of each rule. The ( | ) is of great interest because it tells us how good rule  is for a given class. The posterior probability of the rules given the classes for the CDC-Sublineage data is presented in Table  5. The table includes a row for "No rule" indicating the probability of no rule getting fired. When no rule is fired a regular BN is used instead of KBBN. Note that the probabilities within columns may sum to more than 1 since rules are not mutually exclusive. For CDC, the rule for LAM exactly corresponds to the class LAM on this data, since (Rule = LAM | Class = LAM) = 1 and all other probabilities in the LAM row or column are 0.
The rules for S and X correctly fire for their respective classes, but they also fire incorrectly for other lineages as indicated by the other entries in the S and X rows. The rules for Haarlem and Manila correctly predict their corresponding sublineages, but the fact that "No Rule" occurs 29. 6% and 24. 3% of the time, respectively, indicates that these rules fail to cover all members of their class. For the India class, the India rule is quite accurate, but the rules can be ambiguous as indicated by the multiple entries in the India column. Most Vietnams are not covered by any rules and for those that are covered the rules may be ambiguous. We provide the posterior probability distribution of each rule given the sublineage for the SITVIT-CV dataset as a heat map in Figure  5.
Discussion of Alternative Knowledge-Based Approaches The KBBN has a great appeal over alternative knowledgebased approaches such as knowledge-based SVM (KBSVM) and knowledge-based neural networks (KBANN)  [8, 9, 18]. The first advantage is that no special encodings of the rules are required. In KBANN, the rules are mapped into a neural network by converting the data to numeric form and designing appropriate nodes, links, and weights in the neural network. KBSVM requires each rule to be encoded as a polyhedral rule, such as   ∈     ≤  ℎ  = 1. In KBSVM, the process of converting rules to probabilities can greatly increase the number of rules. For example, for the task of predicting major lineages of MTBC, the 13 original logical rules published in a prior study  [8]  were mapped into 29 polyhedral rules.
The added rules help capture the precedence of the original rules which made them mutually exclusive. There is no easy way to capture rule precedence in KBSVM or KBANN. KBSVM must add rules of the form "     ℎ. " In KBBN, the data can be numeric or symbolic and each rule may be any arbitrary function of the observations to the classes. As reported in the preliminary study  [9] , KBBN works effectively on rules with and without precedence. A second advantage of KBBN is the computational complexity of training. For the polytree type KBBN studied here, both training and inference can be done very efficiently in polynomial time. The MAP estimation of KBBN parameters has a closed form solution enabling globally optimal solutions to be found by simple counting algorithms. The only additional computation required over BN is representation of the probabilities of the classes given the rules.
Unlike KBSVM and KBANN, no special purpose software is required for mapping and training KBBN beyond knowing which rules are fired for which example. KBSVM requires the solution of challenging nonconvex programs with many constraints and variables introduced for each rule over the original SVM. Similarly, KBANN also requires the solution of a nonconvex program of considerably greater complexity than the original ANN network due to the addition of weights and nodes to capture the rules. Special purpose software is needed to create the structure of the KBANN network, but any neural network training algorithm can be used to train it. Additional benefits of KBBN over KBSVM and KBANN include that KBBN can be easily used for multiclass problems, it provides estimates of the posterior probabilities of the classes, and the resulting classification function is more transparent and explainable.
Conclusions We have developed an effective classifier to predict SITVIT MTBC clades with high accuracy. The result is a publicly available web-based tool for SITVIT clade classification to support TB control and research efforts available for use at TB-INSIGHT (http://tbinsight. cs. rpi. edu/run tb lineage. html) and later on SITVIT2. We established that the clade estimates are robust by performing two out-of-sample testing experiments. Furthermore, the results on the two testbeds show that KBBN is a highly accurate classifier that can outperform methods based on rules or Bayesian networks trained on data alone and that meets or beats the performance of nonlinear and linear SVM models. KBBN proved to be robust to ambiguity, incompleteness, and inaccuracy of the rule set. The results here are limited to simple commonly used BN that are polytrees using MAP estimation, but future work is needed to examine the KBBN approach on more general BN models and algorithms.
SITVIT and CDC MTBC testbeds. To validate the approach we also used a dataset of isolates collected by the CDC for cross validation studies. The following two sections describe the datasets in detail. Table1summarizes them. 3. 1. SITVIT Testbed. SITVIT-Train and SITVIT-Test are based on the SITVIT, a MTBC genotyping markers database provided by the Institut Pasteur de la Guadeloupe, and on the SpolDB4 rules that are published in Brudey et al. , 2006 [3], plus one rule recently developed for the URAL1 clade. KBBN was trained on the SITVIT-Train dataset of 2714 records, each corresponding to a spoligotype and clade. There were 69 classes, the minimum sublineage size was 1, and the maximum sublineage size was 390. To test this model while keeping all classes we used SITVIT-Test, a large dataset based on SITVIT with 7949 records, each record corresponding to a spoligotype and clade.
This dataset contained the same 69 classes as SITVIT-Train with different class distributions and again with the minimum class size of 1. SITVIT-Train and SITVIT-Test do not overlap so the total SITVIT dataset consists of 10633 distinct spoligotypes. To enable 10-fold cross validation (CV) with at least one spoligotype per class, the SITVIT-CV dataset was created which consists of the SITVIT-Train data restricted to the 45 classes with at least 11 spoligotypes each.
This dataset contained the same 69 classes as SITVIT-Train with different class distributions and again with the minimum class size of 1. SITVIT-Train and SITVIT-Test do not overlap so the total SITVIT dataset consists of 10633 distinct spoligotypes. To enable 10-fold cross validation (CV) with at least one spoligotype per class, the SITVIT-CV dataset was created which consists of the SITVIT-Train data restricted to the 45 classes with at least 11 spoligotypes each. Testbed Dataset Size Number of classes Max class size Min class size Number of rules Train 2714 69 390 1 69 SITVIT Test 7949 69 1107 1 69 CV 2593 45 390 11 45 CDC CV 1286 8 356 39 8 multimarker database for tuberculosis molecular epidemiol- ogy [13]. Different datasets were created for training, testing, and cross validation.
Good rules only have red on the diagonal. A rule fires for multiple classes if it has multiple red entries BioMed Research International (e. g. T) are not very effective in indicating the associated class as opposed to Beijing which is an effective rule. AFRI Beijing Bovis CAS EAI H LAM MANU T X 1. 0 Rules 0. 0 Figure 5: The heat map represents the posterior probability of each rule given the sublineage for the SITVIT dataset. A strong association of a rule in predicting a sublineage is shown with a red square while a blue square represents no relation. Here H includes URAL-1 and URAL-2 and LAM includes Turkey and Cameroon sublineages. in a row. The rule set is ambiguous for a class if there are multiple red entries within a given class column. Notice that the rules that are fired for many classes with high probability.
Second, we presented a space-state modelling approach that explicitly accounts for sampling error when quantifying population synchrony. Third, we exemplify our approach with datasets for which sampling variance (i) has been previously estimated, and (ii) has to be jointly estimated with population synchrony. Finally, we compared our results to those of a standard approach neglecting sampling variance. We showed that ignoring sampling variance can mask a synchrony pattern whatever its true value and that the common practice of averaging few replicates of population size estimates poorly performed at decreasing the bias of the classical estimator of the synchrony strength.
e. , using r proc ). We present a state-space model for quantifying population synchrony that explicitly accounts for sampling variance and illustrate our approach using (i) a published dataset of fish (Alestes baremoze) abundance  [24]  for which sampling variance has been estimated in a prior study, and (ii) an original dataset of feral cat (Felis silvestris catus) abundance for which sampling variance has to be estimated. Then we compare our results to those obtained using a standard approach ignoring sampling variance. We deliberately used systems composed of few populations unlikely to be strongly connected by dispersal and simple linear models to account for their dynamics. Indeed, our aim is to focus on the consequences of sampling error on the estimation of population synchrony not to deal with all the factors known to be potentially relevant, such as non-linearity, dispersal, demographic stochasticity, heterogeneity among populations, biotic interactions, colour of the environmental noise and distance between sites.
2. 1. Estimation of spatial synchrony using an independent estimate of sampling variance. In this example, we used the abundance data analysed by Tedesco et al. [24]  to explore intra-and inter-specific synchrony patterns in population dynamics of four West African fishes caused by a Moran effect. The only topic we considered here is the way the sampling error is accounted for, i. e. , we were not interested in improving other aspects of the analysis performed by Tedesco et al. [24]. We used 24-year time series  of abundance estimates of Alestes baremoze collected at three sites (inter-site distances: 176-367 km) in two different catchment basins in Cote d'Ivoire (West Africa). On a given site i within a given year j, several gill-net fishing trials were conducted at a few month intervals.
Following Tedesco et al. [24] , we used a stochastic Gompertz model to describe the deterministic inter-annual dynamics of the fish populations. Since the magnitudes of the temporal variations of fish abundance are similar among the study sites (see Fig. 1 ), we considered the following state process to quantify the strength of synchrony among fish populations: X j ~X1,j{1. X i,j{1. where X j ~½X 1j ,:::,X ij ,. ,X N I j 0 is the true mean (log) number of catch per unit effort at site i in year j; b 0 zb 1 X i,j{1 is the first order Gompertz model where b 1 is a coefficient of density-dependence and b 0 is an intercept. Here r r ICC is an estimator of the strength of the average synchrony ( r r pop ) among fish populations (see Materiel and Methods section 1. 3). Both the fish dataset and the R-program used to fit the state-space model are available as supplementary file (File S4) 2.
2. Joint estimation of spatial synchrony and sampling variance. Here, we aimed at illustrating (i) the use of the Generalised Intra-Class Correlation instead of Intra-Class Correlation to estimate average population synchrony, and (ii) the joint estimation of spatial synchrony and sampling variance using count data. Thus, for the sake of simplicity, we considered the same density-dependence structure as in the previous example. We used an original dataset of feral cat abundance collected at four sites (Port-aux-Francais, Port-Jeanne-d'Arc, Port-Couvreux, Ratmanoff; inter-site distances: 20-60 km) of the Grande Terre Island of the Kerguelen Archipelago (sub-Antarctic) from 1996 to 2007  [36, 37]. The sampling protocol used is based on the collection of replicated count data (see File S2) within field sessions of 7-10 days all year round.
As for the fish dataset, the difference in the average abundance of cats between the counting sessions (') within each time j at site i was considered to be part of the sampling process: NC ij'k *N egBin(l ij'k , h) log (l j )~D (2) j (D (1) j x j zc j )zDist j ð6Þ with the total numbers of observations and field sessions at time j given by N K, : j :~P i ' N K,ij' and N L, : j ~P i N L,ij , respectively. In eqn 6, l j is a N K, : j :-element vector of l ij'k ; Dist j is a N K, : j :-element vector containing the logarithmic distance covered by the observer during a given count (log (Dist ij'k )) to account for the likely increase in the number of cats available for detection with the distance covered; D (2)   j and D (1)   j are two N K, : j :|N L, : j and N L, : j |N I matrices of 0 s and 1 s that translate the N L, : j (log) mean population size at time j into N K, : j : (log) mean population size at time j and the N I (log) mean population size at time j into N L, : j (log) mean population size at time j, respectively; and c j is a random vector with c ij' *N (0,s 2 c ) a random term that accounts for the difference in the (log) mean abundance of cats between the field sessions (') within each time j at site i.