## 2 Materials and Methods
### Development of Computational Models for Genomic TTR Prediction  
Hr, WBr, and H/WBr data computed with the GM method were inserted in a clinical database, including the following clinical features: age, sex, left ventricular mass (LV-mass), calculated with Devereux formula [16], divided on body-surface area (LV-mass/BSA), and echocardiographic variables (interventricular septum, IVS, left-ventricle ejection fraction, LVEF, left-atrial anterior-posterior diameter, d_LA, and posterior wall, PW). Data were used as an input of a least absolute shrinkage and selection operator (LASSO) regression model to predict the patient etiology (i.e., ATTRwt versus ATTRm). The LASSO model was implemented with alpha equal to 1, and elastic net (elnet) regularization with the R library glmnet [17]. Variable normalization was performed before the application of the regression model. The entire patient population was split 80-20%. The first dataset was further subdivided into a proportion of 70-30% training-test set, respectively, and the second dataset was used as a validation set. A LASSO with cross-validation (LASSO-CV) was implemented to find the best lambda value that minimizes the mean squared error and then a LASSO was computed to select the variables with non-zero coefficients in correspondence with each previously estimated best lambda found by LASSO-CV. The LASSO regularization method works as a feature selection throughout a progressive shrinkage of variable coefficients. LASSO-CV and the subsequent LASSO were iterated 100 times to verify which variables are selected more frequently and, therefore, are considered more reliable for predicting the outcome of the two different etiologies. This process of 100 iterations permits the assessment of a frequency score by evaluating the number of times that each feature appears in the build models as proof of the robustness of the feature selection operation. The most frequently selected features (recurrence > 30) were used to train a LASSO regression model based on a training dataset (i.e., 70% randomly selected from the 80% of the entire dataset) and tested on the remaining 30% subset. Finally, the model was validated on the validation dataset (20% of the entire database). Furthermore, the most important and recurrent features selected by the LASSO iterated model were inserted to implement the other two models, Random Forest (RF) and Neural Network (NN). The RF model was built with 500 decision trees that aim to solve a regression problem, implemented by the R library randomForest [18]. The importance of each variable inside the model was evaluated through two parameters: the percentage of the Increment of Mean Decrease Accuracy (%IncMSE) and the Increment of Node Purity (IncNodePurity). The %IncMSE represents the percentage of the incidence of each variable in evaluating the accuracy of the model, i.e., it specifies quantitatively how much the accuracy of our model decreases if that variable was left out. The IncNodePurity measures the importance of the variable based on the Gini impurity index used for calculating the divisions in the trees. The Gini index, also known as a Gini impurity, calculates the amount of probability of a specific feature being misclassified when randomly selected. If all of the elements are linked with a single class, then it can be called pure. The Gini index varies between values 0 and 1, where 0 expresses the purity of classification, i.e., all of the elements belong to a specified class or only one class exists there; instead, the value 1 indicates the random distribution of elements across various classes. In addition, NN was developed with the same aim of predicting the patient etiology with the R library NeuralNet [19]. The same entire dataset was divided into training, test, and validation sets, as in the LASSO, with the same proportion. The NN was fed with the eight variables that were most frequently selected by the LASSO regularization method after 100 iterations.