Layer-based Composite Reputation Bootstrapping

We propose a novel generic reputation bootstrapping framework for composite services. Multiple reputation-related indicators are considered in a layer-based framework to implicitly reflect the reputation of the component services. The importance of an indicator on the future performance of a component service is learned using a modified Random Forest algorithm. We propose a topology-aware Forest Deep Neural Network (fDNN) to find the correlations between the reputation of a composite service and reputation indicators of component services. The trained fDNN model predicts the reputation of a new composite service with the confidence value. Experimental results with real-world dataset prove the efficiency of the proposed approach.

CT-Based radiomics model for preoperative prediction of hepatic encephalopathy after transjugular intrahepatic portosystemic shunt

Objective: To develop and evaluate a machine learning-based CT radiomics model for the prediction of hepatic encephalopathy (HE) after transjugular intrahepatic portosystemic shunt (TIPS). Methods: A total of 106 patients who underwent TIPS placement were consecutively enrolled in this retrospective study. Region of interests (ROIs) were drawn on unenhanced, arterial phase, and portal venous Phase CT images, and radiomics features were extracted, respectively. A radiomics model was established to predict the occurrence of HE after TIPS by using random forest algorithm and ten-fold cross-validation. Receiver operating characteristic (ROC) curves were performed to validate the capability of the radiomics model and clinical model on the training, test and original datasets, respectively. Results: The radiomics model showed favorable discriminatory ability in the training cohort with an area under the curve (AUC) of 0.899 (95% CI, 0.848 to 0.951), while in the test cohort, it was confirmed with an AUC of 0.887 (95% CI, 0.760 to 1.00). After applying this model to original dataset, it had an AUC of 0.955 (95% CI, 0.896 to 1.00). A clinical model was also built with an AUC of 0.649 (95% CI, 0.530 to 0.767) in the original dataset, and a Delong test demonstrated its relative lower efficiency when compared with the radiomics model (p < 0.05). Conclusion: Machine learning-based CT radiomics model performed better than traditional clinical parameter-based models in the prediction of post-TIPS HE. Advances in knowledge: Radiomics model for the prediction of post-TIPS HE was built based on feature extraction from routine acquired preoperative CT images and feature selection by random forest algorithm, which showed satisfied performance and proved the advantages of machine learning in this field.

Prediction of Renal Damage in Children with Henoch-Schönlein Purpura Based on Machine Learning

Background This article is objected to explore the value of machine learning algorithm in predicting the risk of renal damage in children with Henoch-Schönlein Purpura, and to construct a predictive model of Henoch-Schönlein Purpura Nephritis in children and analyze the related risk factors of Henoch-Schönlein Purpura Nephritis in children. Methods Case data of 288 hospitalized children with Henoch-Schönlein Purpura from November 2018 to October 2021 were collected. The data included 42 indicators such as demographic characteristics, clinical symptoms and laboratory tests, etc. Univariate feature selection was used for feature extraction, and Logistic regression, support vector machine, decision tree and random forest algorithm were used respectively for classification prediction. Last, the performance of four algorithms are compared using accuracy rate and recall rate. Results The accuracy rate, recall rate and AUC of the established random forest model were 0.83, 0.86 and 0.91 respectively, which were higher than 0.74, 0.80 and 0.89 of the Logistic regression model; higher than 0.70, 0.80 and 0.89 of support vector machine model; higher than 0.74, 0.80 and 0.81 of the decision tree model. The top 10 important features provided by random forest model are Persistent purpura≥4weeks, Cr, Clinic time, ALB, WBC, TC, TG, Relapse, TG, Recurrent purpura and EB-DNA. Conclusion The model based on random forest algorithm has better performance in the prediction of children with allergic purpura renal damage, indicated by better classification accuracy, better classification effect and better generalization performance.

Bioinformatics Analysis Identifies Potential Ferroptosis Key Genes in the Pathogenesis of Pulmonary Fibrosis

Objective: Ferroptosis has an important role in developing pulmonary fibrosis. The present project aimed to identify and validate the potential ferroptosis-related genes in pulmonary fibrosis by bioinformatics analyses and experiments.Methods: First, the pulmonary fibrosis tissue sequencing data were obtained from Gene Expression Omnibus (GEO) and FerrDb databases. Bioinformatics methods were used to analyze the differentially expressed genes (DEGs) between the normal control group and the pulmonary fibrosis group and extract ferroptosis-related DEGs. Hub genes were screened by enrichment analysis, protein-protein interaction (PPI) analysis, and random forest algorithm. Finally, mouse pulmonary fibrosis model was made for performing an exercise intervention and the hub genes’ expression was verified through qRT-PCR.Results: 13 up-regulated genes and 7 down-regulated genes were identified as ferroptosis-related DEGs by comparing 103 lung tissues with idiopathic pulmonary fibrosis (IPF) and 103 normal lung tissues. PPI results indicated the interactions among these ferroptosis-related genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway enrichment and Genome-Ontology (GO) enrichment analyses showed that these ferroptosis-related genes involved in the organic anion transport, response to hypoxia, response to decrease oxygen level, HIF-1 signaling pathway, renal cell carcinoma, and arachidonic acid metabolism signaling pathway. The confirmed genes using PPI analysis and random forest algorithm included CAV1, NOS2, GDF15, HNF4A, and CDKN2A. qRT-PCR of the fibrotic lung tissues from the mouse model showed that the mRNA levels of NOS2 and GDF15 were up-regulated, while CAV1 and CDKN2A were down-regulated. Also, treadmill training led to an increased expression of CAV1 and CDKN2A and a decrease in the expression of NOS2 and GDF15.Conclusion: Using bioinformatics analysis, 20 potential genes were identified to be associated with ferroptosis in pulmonary fibrosis. CAV1, NOS2, GDF15, and CDKN2A were demonstrated to be influencing the development of pulmonary fibrosis by regulating ferroptosis. These findings suggested that, as an aerobic exercise treatment, treadmill training reduced ferroptosis in the pulmonary fibrosis tissues, and thus, reduces inflammation in the lungs. Aerobic exercise training initiate concomitantly with induction of pulmonary fibrosis reduces ferroptosis in lung. These results may develop our knowledge about pulmonary fibrosis and may contribute to its treatment.

Random Forest for Genomic Prediction

We give a detailed description of random forest and exemplify its use with data from plant breeding and genomic selection. The motivations for using random forest in genomic-enabled prediction are explained. Then we describe the process of building decision trees, which are a key component for building random forest models. We give (1) the random forest algorithm, (2) the main hyperparameters that need to be tuned, and (3) different splitting rules that are key for implementing random forest models for continuous, binary, categorical, and count response variables. In addition, many examples are provided for training random forest models with different types of response variables with plant breeding data. The random forest algorithm for multivariate outcomes is provided and its most popular splitting rules are also explained. In this case, some examples are provided for illustrating its implementation even with mixed outcomes (continuous, binary, and categorical). Final comments about the pros and cons of random forest are provided.

An Improved Deep Learning Algorithm for Diabetes Prediction

This chapter introduces the novel approach in deep learning for diabetes prediction. The related work described the various ML algorithms in the field of diabetic prediction that has been used for early detection and post examination of the diabetic prediction. It proposed the Jaya-Tree algorithm, which is updated as per the existing random forest algorithm, and it is used to classify the two parameters named as the ‘Jaya' and ‘Apajaya'. The results described that Pima Indian diabetes dataset 2020 (PIS) predicts diabetes and obtained 97% accuracy.