Novel machine learning model for predicting cancer drugs' susceptibilities and discovering novel treatments.

Background And Objective: Timely treatment is crucial for cancer patients, so it's important to administer the appropriate treatment as soon as possible. Because individuals can respond differently to a given drug due to their unique genomic profiles, we aim to use their genomic information to predict how various drugs will affect them and determine the best course of treatment.

Methods: We present Kernelized Residual Stacking (KRS), a new multi-task learning approach, and use it to predict the responses to anti-cancer drugs based on genomic data.

Automatic data augmentation for medical image segmentation using Adaptive Sequence-length based Deep Reinforcement Learning.

Although existing deep reinforcement learning-based approaches have achieved some success in image augmentation tasks, their effectiveness and adequacy for data augmentation in intelligent medical image analysis are still unsatisfactory. Therefore, we propose a novel Adaptive Sequence-length based Deep Reinforcement Learning (ASDRL) model for Automatic Data Augmentation (AutoAug) in intelligent medical image analysis. The improvements of ASDRL-AutoAug are two-fold: (i) To remedy the problem of some augmented images being invalid, we construct a more accurate reward function based on different variations of the augmentation trajectories.

An Integrated Multi-Channel Deep Neural Network for Mesial Temporal Lobe Epilepsy Identification Using Multi-Modal Medical Data.

Epilepsy is a chronic brain disease with recurrent seizures. Mesial temporal lobe epilepsy (MTLE) is the most common pathological cause of epilepsy. With the development of computer-aided diagnosis technology, there are many auxiliary diagnostic approaches based on deep learning algorithms.

A retrospective cohort study evaluating the improvement of medical records management based on whole-process control.

Background: Whole-process management is a novel approach widely applied in industry and commerce; however, it is not widely used in the management of medical records in hospitals.

Objective: The purpose of this study is to investigate the application of whole-process control in the administration of a hospital's medical records department to achieve refined management of medical records.

Methods: Whole-process control is a management measure that begins with process conception and implementation and includes control over all processes.

Recommendation model based on generative adversarial network and social reconstruction.

Social relations can effectively alleviate the data sparsity problem in recommendation, but how to make effective use of social relations is a difficulty. However, the existing social recommendation models have two deficiencies. First, these models assume that social relations are applicable to various interaction scenarios, which does not match the reality.

[A research on epilepsy source localization from scalp electroencephalograph based on patient-specific head model and multi-dipole model].

Accurate source localization of the epileptogenic zone (EZ) is the primary condition of surgical removal of EZ. The traditional localization results based on three-dimensional ball model or standard head model may cause errors. This study intended to localize the EZ by using the patient-specific head model and multi-dipole algorithms using spikes during sleep.

Dynamic Correlation Adjacency-Matrix-Based Graph Neural Networks for Traffic Flow Prediction.

Modeling complex spatial and temporal dependencies in multivariate time series data is crucial for traffic forecasting. Graph convolutional networks have proved to be effective in predicting multivariate time series. Although a predefined graph structure can help the model converge to good results quickly, it also limits the further improvement of the model due to its stationary state.

A Systematic Evaluation of Supervised Machine Learning Algorithms for Cell Phenotype Classification Using Single-Cell RNA Sequencing Data.

The new technology of single-cell RNA sequencing (scRNA-seq) can yield valuable insights into gene expression and give critical information about the cellular compositions of complex tissues. In recent years, vast numbers of scRNA-seq datasets have been generated and made publicly available, and this has enabled researchers to train supervised machine learning models for predicting or classifying various cell-level phenotypes. This has led to the development of many new methods for analyzing scRNA-seq data.

Stability and Stabilization for Fuzzy Systems With Time Delay by Applying Polynomial Membership Function and Iteration Algorithm.

Recently, a switching method is applied to deal with the membership function-dependent Lyapunov-Krasovskii functional (LKF) for fuzzy systems with time delay; however, the Lyapunov matrices are only linear dependent on the grades of membership which leads to linear switching (Wang and Lam, 2019). In this article, the linear dependence on the grades of membership is extended to homogenous polynomially membership function dependent (HPMFD) and the linear switching is extended to polynomial matrix switching, based on which the obtained result contains the previous one as a special case. Furthermore, in order to fully use the introduced variables without speial structure, an iteration algorithm is designed to construct the switching controller and the initial condition of the algorithm is also discussed.

Comparison of the effect of fluticasone combined with salmeterol and fluticasone alone in the treatment of pediatric asthma.

Introduction: A systematic review and meta-analysis was performed to investigate the effect of fluticasone + salmeterol and fluticasone alone in the treatment of pediatric asthma.

Evidence Acquisition: Studies meeting specific selection criteria were selected from online databases, including Pubmed, Embase, and the Cochrane Library. The quality of randomized controlled trials was assessed using the Cochrane Library.

Modified Zhibai Dihuang pill, a traditional Chinese medicine formula, on steroid withdrawal in systemic lupus erythematosus: A systematic review and meta-analysis.

Background: Zhibai Dihuang pill (ZBDH), a Chinese herbal formula, has been widely used as an adjunctive therapy to help reduce the patient's steroid dose and maintain low disease activity in systemic lupus erythematosus (SLE).

Objective: This systematic review evaluates the therapeutic effect of modified ZBDH in reducing steroid use in patients with SLE.

Search Strategy: A systematic literature search was carried out using seven databases, including PubMed, Embase, Cochrane Central Register of Controlled Trials, Chinese Biomedical Literature Database, Chinese National Knowledge Infrastructure, Chinese VIP Information and Wanfang Database, from their inception to June 1st, 2019.

Probabilistic Graph Convolutional Network via Topology-Constrained Latent Space Model.

Although many graph convolutional neural networks (GCNNs) have achieved superior performances in semisupervised node classification, they are designed from either the spatial or spectral perspective, yet without a general theoretical basis. Besides, most of the existing GCNNs methods tend to ignore the ubiquitous noises in the network topology and node content and are thus unable to model these uncertainties. These drawbacks certainly reduce their effectiveness in integrating network topology and node content.

PPAI: a web server for predicting protein-aptamer interactions.

Background: The interactions between proteins and aptamers are prevalent in organisms and play an important role in various life activities. Thanks to the rapid accumulation of protein-aptamer interaction data, it is necessary and feasible to construct an accurate and effective computational model to predict aptamers binding to certain interested proteins and protein-aptamer interactions, which is beneficial for understanding mechanisms of protein-aptamer interactions and improving aptamer-based therapies.

Results: In this study, a novel web server named PPAI is developed to predict aptamers and protein-aptamer interactions with key sequence features of proteins/aptamers and a machine learning framework integrated adaboost and random forest.

Potential biomarkers of acute myocardial infarction based on weighted gene co-expression network analysis.

Background: Acute myocardial infarction (AMI) is the common cause of mortality in developed countries. The feasibility of whole-genome gene expression analysis to identify outcome-related genes and dysregulated pathways remains unknown. Molecular marker such as BNP, CRP and other serum inflammatory markers have got the notice at this point.

Improved L0 Gradient Minimization with L1 Fidelity for Image Smoothing.

Edge-preserving image smoothing is one of the fundamental tasks in the field of computer graphics and computer vision. Recently, L0 gradient minimization (LGM) has been proposed for this purpose. In contrast to the total variation (TV) model which employs the L1 norm of the image gradient, the LGM model adopts the L0 norm and yields much better results for the piecewise constant image.

[Effect of intravenous drip infusion of cyclophosphamide with high-dose Astragalus injection in treating lupus nephritis].

Objective: To observe the effect of high-dose Astragalus injection and cyclophosphamide (CTX) on infection, urine protein and immune function of the patients with lupus nephritis.

Methods: Forty-three patients diagnosed as systemic lupus erythematosus (SLE) complicated by kidney damage and qi-deficiency syndrome were randomly divided into trial group (n=23) and control group (n=20). Patients in both groups were treated for 3 months.