dataesr/scientific-paragraphs-categorization huggingface dataset:
Games are developed specifically for a specific disease group or disease symptoms. Thus, it is aimed to provide rehabilitation by increasing active participation in therapy with higher motivation. One of the systems developed for this purpose is; Becure system, formerly known as Fizyosoft. Becure System has software that can work integrated with the Wii Balance Board. Studies in the literature show that Becure games give positive results in different disease groups  [21] [22] [23] [24] [25]. However, there is no study conducted with serious game with pes planus. Copy and paste relevant sections from the manuscript (include quotes in quotation marks "like this" to indicate direct quotes from your manuscript), or elaborate on this item by providing additional information not in the ms, or brie y explain why the item is not applicable/relevant for your study Today, the rapid development of technology and patients' search for new treatments have made it necessary to add these technologies to the rehabilitation process.
Pedobarographic analysis The pedobarographic analysis will be done in two steps, statically and dynamically, with the Sensor Medica Maxi brand device (Sensormedica, Guidonia Montecelio, Rome, Italy). Static analysis data will be recorded while standing upright. Dynamic analysis data will be taken while walking on the platform six times. Data such as ankle valgus angle, medial arch, lateral arch, medial heel, lateral heel surface area (cm²), and load (%) will be recorded. Plantar pressure analyses will be determined through the FreeStep software, which is compatible with the device (Figure  2 )  [26].
The advent of next-generation sequencing (NGS) technologies has revolutionized the field of bioinformatics and genomics, particularly in the area of onco-somatic genetics. NGS has provided a wealth of information about the genetic changes that underlie cancer and has considerably improved our ability to diagnose and treat cancer. However, the large amount of data generated by NGS makes it difficult to interpret the variants. To address this, machine learning algorithms such as Extreme Gradient Boosting (XGBoost) have become increasingly important tools in the analysis of NGS data. In this paper, we present a machine learning tool that uses XGBoost to predict the pathogenicity of a mutation in the myeloid panel. We optimized the performance of XGBoost using metaheuristic algorithms and compared our predictions with the decisions of biologists and other prediction tools.
The myeloid panel is a critical component in the diagnosis and treatment of myeloid neoplasms, and the sequencing of this panel allows for the identification of specific genetic mutations, enabling more accurate diagnoses and tailored treatment plans. We used datasets collected from our myeloid panel NGS analysis to train the XGBoost algorithm. It represents a data collection of 15,977 mutations variants composed of a collection of 13,221 Single Nucleotide Variants (SNVs), 73 Multiple Nucleoid Variants (MNVs), and 2683 insertion deletions (INDELs). The optimal XGBoost hyperparameters were found with Differential Evolution (DE), with an accuracy of 99. 35%, precision of 98. 70%, specificity of 98. 71%, and sensitivity of 1.
In this paper, we present a machine learning tool that utilizes XGBoost for predicting the pathogenicity of mutations in the myeloid panel. We aim to optimize the performance of XGBoost by employing metaheuristic algorithms to determine the most suitable architecture and incorporate new data into the learning process. To achieve this, we tuned several hyperparameters, including the number of boosting iterations, learning rate, maximum tree depth, minimum reduction in the loss function, fraction of columns used in each split, minimum number of samples in a leaf node, and fraction of the training set used for each boosting iteration. As the performance of XGBoost is influenced by these hyperparameters, optimizing their selection is crucial. Hence, we employ metaheuristic algorithms, including Differential Evolution (DE), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Simulated Annealing (SA), to search the hyperparameter space and identify the combination that maximizes performance.
With this method, a more accurate and comprehensive picture of the genetic mutations present in a patient's cancer is achieved, which can inform treatment decisions and help monitor disease progression and treatment response over time. Our dataset is composed of a cohort of 15,977 Single Nucleotide Variants (SNVs), Single Nucleotide Polymorphisms (SNPs), and insertion deletion variants (INDELs) from our 24 genes in the panel, ASXL1, CALR, CBL, CEBPA, CXCR4, DNMT3A, EZH2, FLT3, GATA2, IDH1, IDH2, JAK2, KIT, KRAS, MPL, NPM1, NRAS, PTEN, RUNX1, SF3B1, SRSF2, TET2, TP53, and WT1 note that only the target regions are sequenced), which represents 1050 samples from 2019 to 2022. For all the clinical samples, the genomic DNA extraction method was performed with the EZ1 XL (Qiagen, Hilden, Germany).
We performed sequencing with Ion Torrent S5XL (Thermo Fisher, Waltham, MA, USA) with a sensitivity of 5% and minimum coverage of 300X. Then, sequencing data were analyzed through 2 pipelines. The first pipeline was developed by ThermoFisher on the IonTorrent Suite + Ion Reporter. IonTorrent Suite generates FASTQ data and ensures BAM (binary alignment mapping) alignment with the hg19 reference genome by using the TMAP (Torrent Mapping Alignment Program). The Ion Reporter makes variant caller and variant annotations. The second pipeline was developed in our laboratory and runs open-source software such as BWA-MEM for alignment, SAMtools for mpileup, VarScan2 as the variant caller, and VEP Ensemble for annotations. All data are stored in our local MySQL database. Biologists manually validated variants as pathogenic if the following statements were true : We verify the presence of these pathogenic variants in our second-in-house pipeline to validate them.
Programming Language The programming language used to develop the machine learning model was R version 4. 2. 2 with XGBoost version 1. 6. 0. 1 and caret version 6. 0-93 Libraries for Genetic Algorithms (GA version 3. 2. 2), Particle Swarm Optimization (pso version 1. 0. 4), Differential Evolution (DEoptim version 2. 2-6), and Generalized Simulated Annealing (GenSA version 1. 17).
Data Selection The myeloid dataset contains clinical information of 15,977 mutation variants (Figure  1 ) composed of a collection of 13,221 Single Nucleotide Variant (SNVs), 73 Multiple Nucleotide Variant (MNVs), and 2683 insertion deletions (INDELs). All variants were labeled by the biologist as benign or pathogenic in the function of the pathology of the patient. A total of 14,957 variants were labeled as benign (class 0), and 1020 were labeled as pathogenic (class 1). Our dataset is imbalanced. We have a disproportionate representation of the pathogenic. To overcome this problem, there are many appropriate methods to remedy it. We chose to use the oversampling approach for the creation of synthetic data to increase the numbers of the minority class. To perform this operation, we used the package ROSE (random oversampling examples version 0.
0-4). In general, oversampling can be a useful technique when working with imbalanced datasets, as it can help to balance the class distribution and improve the performance of machine learning models  [8, 9]. After applying the oversampling method, our training set was composed of 10,339 occurrences with class 1 (pathogenic) and 10,470 occurrences with class 0 (benign). However, oversampling can also introduce bias and reduce the overall representativeness of the training data if not carried out carefully. For example, simply replicating the minority class observations can lead to overfitting and poor generalization to unseen data. It is always a good idea to evaluate the performance of a model on a hold-out test set to ensure that it generalizes well to unseen data. This will give a more accurate assessment of the model's performance on real-world data.
For that, we split our dataset into 2 parts, training (70% of the dataset) and testing (the remaining 30% (unseen)). In addition, to avoid overfitting and perform further evaluation and validation, we used 10 cross-validations, repeated 3 times. This ensures that the model generalizes well to new data. Moreover, the results of these evaluations can help to refine the model and improve its performance. Cross-validation is an important step in the evaluation of a machine learning model. The purpose of cross-validation is to assess how well a model will generalize to an independent data set, in other words, how well the model will perform on unseen data. By using cross-validation, we can avoid overfitting and obtain a better estimate of the model's performance  [10] [11] [12] [13]. In the machine learning model evaluation, we split the data into two parts: a training set and a test set.
The model is trained on the training set and then evaluated on the test set. The issue with this approach is that the test set might be small or not representative of the whole dataset, which can lead to poor generalization. Crossvalidation solves this issue by dividing the data into multiple folds and then iteratively training the model on different combinations of folds and evaluating it on the remaining fold. This allows us to obtain a better estimate of the model's performance, since we are using multiple different training and test sets. It also provides a measure of variability in the model's performance, which is useful for selecting the best model from a set of candidate models.
Data Encoding (Feature Construction) The laboratory has generated these data using the Ion Torrent sequencer's pipeline, which is available in the form of a. tsv file containing tabulated separated values. Each line in the file represents a mutation present in the myeloid panel, and the biologist assigned a label of 1 to indicate pathogenic and 0 to indicate benign/uncertain significance (identified by the columns isMut). The input to our XGBoost model consists of 14 features, and the output has two possible values (Table  1  and Equation (  1 )). Data processing is crucial for creating a clean, informative, and well-structured dataset that can help XGBoost to learn patterns and make accurate predictions. It helps to ensure that the data is of good quality and free of errors, inconsistencies, or missing values.
Additionally, we can use this plot to identify potential interactions between predictors and the outcome variable. There seems to be a considerable positive correlation between the amino acid mutated chemical value (aamutChemicalVal), the exon, the Coverage, and the amino acid reference chemical value (aarefChemical) with the output variable isMut (Figure  3 ). If we want to visualize the distribution of a variable or a set of variables in our dataset to get a sense of the distribution of each variable and how it varies depending on the value of the isMut column (Figure  4 ), we need to plot the density. In the density plot, each individual density plot shows the distribution of values for one of the variables in our dataset. The x-axis shows the values that the variable can take, and the y-axis shows the estimated probability density for each value.
Principal Component Analysis (PCA) PCA is a useful tool for dimension reduction and can help identify patterns and relationships among variables in our dataset. It can be a useful way to reduce the dimensionality of our data and identify the most important factors that explain the variation in our dataset  [19] [20] [21] [22]. This can help simplify our analysis and make it easier to identify patterns and relationships that are not immediately apparent in the raw data. In a 2D plot and 3D plot resulting from PCA, we can visually inspect if there is clustering by looking for groups or clusters of data points that are close together. If there are distinct groups of data points that are separated from each other on the plot, this suggests that there may be clustering in the data. As we can observe in Figures 5 , the isMut variables (0 and 1), represented by the colors green and red, are mixed and not separated into distinct groups, so it is unlikely that we have clustering.
Defining the Fitness Function In our specific case, we want to maximize the function accuracy. Our objective function is measuring the performance of XGBoost in terms of accuracy; then, we can represent it mathematically using the following notation: Let note D = {(x 1 , y 1 0,. , (x n , y n )} be the training dataset, where each x i is a feature vector and each y i is the corresponding class label. Let f (x i ) be the XGBoost model with parameters θ that maps a feature vector x to a predicted class label, and let y i = f (x i ) be the predicted class label for the ith example. Then, the accuracy of the XGBoost model with parameters θ can be defined as follows: Accuracy(Θ) = ( 1 n ) × ∑ i = 1 n × [y i = y i ] (3) where [y i = y i ] is an indicator function that equals 1 if y i = y i and 0 otherwise.
The notation [y i = y i ] is sometimes written as a Kronecker delta function δ(y i , y i ), which is defined as δ(y i , y i ) = 1 if y i = y i 0 otherwise (4) If we reduce Equation (3) to our binary classification task, then the accuracy of the XGBoost model with parameters θ can be defined as the fraction of correctly classified examples in the training dataset, which can be written as Accuracy(θ) = TP + TN TP + TN + FP + FN ( 5 ) where TP is the number of true positives (i. e. , the number of examples that are correctly classified as positive), TN is the number of true negatives (i. e. , the number of examples that are correctly classified as negative), FP is the number of false positives (i. e. , the number of examples that are incorrectly classified as positive), and FN is the number of false negatives (i.
e. , the number of examples that are incorrectly classified as negative). These values can be computed using the confusion matrix, which is a table that summarizes the number of true and false positives and negatives for a classifier   Actual Positive Actual Negative Predicted Positive TP FP Predicted Negative FN TN   (6) Using the confusion matrix (Equation (  6 )), we can write        TP = ∑ i 1 n [y i = 1 and y i = 1] TN = ∑ i 1 n [y i = 0 and y i = 0] FP = ∑ i 1 n [y i = 1 and y i = 0] FN = ∑ i 1 n [y i = 0 and y i = 1]        ( 7 ) where n is the number of examples in the training dataset. Once we compute the values of TP, TN, FP, and FN, we can plug them into the accuracy formula to obtain the accuracy (Equation (  5 )) of the XGBoost model with parameters θ.
5. Repeat steps 2 to 4 until a stopping criterion is met (the maximum number of iterations is reached). The accuracy function that we want to maximize using PSO was defined by the Equation (3), where D is our training dataset, where each x i is a feature vector, and each y i is a binary class label (0 or 1). We can also express the accuracy as a function of the confusion matrix C, which is a 2 × 2 matrix that contains the number of true positives, true negatives, false positives, and false negatives: Accuracy(θ) = Γ(θ) = (C[1, 1] + C[2, 2]) (C[1, 1] + C[1, 2] + C[2, 1] + C[2, 2]) (11) The goal of the PSO algorithm is to find the set of XGBoost parameters θ that maximize this accuracy function. We fixed the parameters learning rate to 0. 5; the parameters phi1 and phi2, known as the cognitive and social parameters, were fixed to 2.
Applying SA to XGBoost In the context of tuning the parameters of an XGBoost model, Simulated Annealing is used to explore the space of possible parameter values and find the optimal combination that maximizes the accuracy of the model on a given dataset D. The objective function, in this case, would be the accuracy of the XGBoost model defined by the Equation (  5 ), and the parameters to be optimized would be eta, nrounds, max_depth, gamma, colsample_bytree, min_child_weight, and subsample. The Simulated Annealing algorithm would explore different combinations of parameter values by iteratively adjusting them and accepting or rejecting the new solution based on the acceptance probability. To apply SA to the task of tuning XGBoost parameters, we can use a similar approach to the one we described for DE, GA, and PSO.
Model Performance We evaluated the predictive models on validation sets and we compared the results of validation to obtain the best XGBoost architecture for identifying pathogenicity in myeloid panel genes. Finally, we tested the best model based on a test dataset on a new NGS run comprising 6 different patients (total of 89 mutations) to obtain general predictions on onco-somatic mutation variants. To assess the performance of each model, accuracy, recall (sensitivity), precision, sensibility, kappa, error rate, and Matthew Correlation Coefficient (MCC) were calculated to measure the performance of classification models obtained with Differential Evolution, Particle Swarm Optimization, Genetic Algorithm, and Simulated Annealing. These parameters are defined as follows: • Accuracy = TP+TN TP+TN+FP+FN.
• Error Rate = ∑ n i=1 [y i = y i ] n where n is the total of predictions, y i is the actual target label for the ith observation, and y i is the predicted label for the ith observation. Even if we oversampled our dataset during training, it is important to calculate the Matthew Correlation coefficient (MCC). When we oversample our dataset, we are essentially creating synthetic samples in the minority class to balance the distribution of the classes. However, this does not change the performance of the model on the test set, which is evaluated on the original, unmodified distribution of the classes. Therefore, it is still important to calculate the MCC on the test set to assess the true performance of the model on the original, imbalanced distribution. MCC is a measure of the quality of binary (two-class) classifications, which takes into account true and false positives and negatives.
Performance on the Training Set Summary results with different methods with a 10 cross-validation repeated 3 times. Each algorithm was evaluated 30 times. The training set represents 70% of the dataset, and the remaining 30% is the test set. The seven important hyperparameters to control XGBoost behavior that we tuned are the following: • nrounds: The number of boosting iterations to perform. A boosting iteration trains a new weak learner to improve the overall performance of the model. • eta: The learning rate or step size for shrinking the contribution of each tree in the model. A smaller eta value will result in more rounds of boosting required to achieve the same level of accuracy, but each boosting iteration will have a smaller impact on the final model. • max_depth: The maximum depth of each tree in the model.
Feature Importance The feature importance plot provides information about the contribution of each feature to the prediction of the target variable (the class of the mutation variant). A high score indicates that the feature has a strong impact on the prediction, while a low score indicates that the feature has a weaker impact. The method used to calculate feature importance for XGBoost models is the "weight" method, which simply counts the number of times each feature is used to split the data across all trees in the model. The importance scores are then normalized to sum to one. The method is included in the package Caret. The contribution of each feature to the model's prediction is based on the split gain. The split gain is a measure of how much each feature splits the data and leads to a reduction in impurity or increase in accuracy.
They offer significant advantages over grid search and random search in three main areas: global optimization, handling complex objective functions, and automated stopping criteria. By using population-based or swarm-based search strategies, metaheuristic algorithms are designed to find global optimal solutions, whereas grid search and random search can get stuck in local optima. Additionally, they can handle complex and nondifferentiable objective functions, which can be difficult for grid search and random search. Finally, metaheuristic algorithms often have automated stopping criteria, such as when a satisfactory solution is found or when a maximum number of iterations is reached, which can save time compared with a grid search or random search that require manual stopping. One advantage of XGBoost is its ability to handle large datasets with high-dimensional features, which is common in bioinformatics applications such as genomic data analysis  [46].
However, it should be noted that further testing is required to evaluate their performance on mutations outside the myeloid panel. Overall, our results suggest that metaheuristic algorithms are valuable tools for optimizing machine learning models in bioinformatics applications, especially when dealing with high-dimensional and complex datasets. Moreover, the social implications of our study are noteworthy. The accurate prediction of mutation pathogenicity contributes to a deeper understanding of genetic diseases and their underlying mechanisms. This knowledge can facilitate the development of targeted therapies and interventions, leading to better patient outcomes and improved healthcare strategies. Additionally, by incorporating metaheuristic algorithms in bioinformatics re-search, we promote the adoption of advanced computational techniques that enhance the field's efficiency and effectiveness.
Acknowledgments: We would like to thank the team of technicians ( Veronique Brice ,  Annick Durand ,  Cindy Fernandez-Muniz ,  Christine GIUSTI ,  Elsa Niel ,  Christine Schiano ,  Nicolas Sprynski ,  Christelle Taillandier , and  Corinne Terlier ) for their involvement and the work they do daily in the  Onco-Biology laboratory. Without them, this project would not have been accomplished.
We used these findings to compile vast datasets of pathogenic and non-pathogenic variant structures and developed a machine-learning-based neural network (AMIVA-F) to predict the impact of single point mutations. AMIVA-F outperformed most commonly used predictors both in disease related as well as neutral variants, approaching ~80% accuracy. Taken together, our study documents additional FLNC variants, their biophysical and structural properties, and their link to the disease phenotype. Furthermore, we developed a state-of-the-art web-based server AMIVA-F that can be used for accurate predictions regarding the effect of single point mutations in human filamin C, with broad implications for basic and clinical research.
Patient clinical data, demographics and family histories We report data on eight patients, six of whom carry a pathogenic FLNC variant, and two who carry a neutral variant. Overall, the cohort included both men (2 of 8) and women (6 of 8), all except one of European origin (one woman in this cohort is of Afro-Caribbean origin). In this cohort, all patients who bore pathogenic variants exhibited features of various cardiomyopathies (see below for details). No clinical skeletal myopathy was noticed in these patients. Below we describe each patient, their demographic information, and provide summaries of associated clinical data and family histories. The patient information is organized by the location of their point mutation, from N to C-terminus of FlnC. Patient #1 is a female proband, who received a diagnosis of dilated cardiomyopathy (DCM) at 23 years of age.