Protein–peptide interaction is crucial for many cellular processes. It difficult to determine the by experiments as peptides are often very flexible in structure. Accurate sequence-based prediction of peptide-binding residues can facilitate study this interaction. In work, we developed two novel methods SVMpep and PepBind identify residues. Recent studies demonstrate that protein–peptide binding closely associated with intrinsic disorder. We thus introduced disorder our feature design ab...

High-efficiency and low-cost bifunctional electrocatalysts for oxygen reduction reaction (ORR) evolution (OER), as well gel electrolytes with high thermal mechanical adaptability are required the development of flexible batteries. Herein, abundant Setaria Viridis (SV) biomass is selected precursor to prepare porous N-doped carbon tubes specific surface area 900 °C calcination product SV (SV-900) shows optimum ORR/OER activities a small EOER -EORR 0.734 V. Meanwhile, new multifunctional...

Abstract Background Lung cancer is one of the most common types cancer, among which lung adenocarcinoma accounts for largest proportion. Currently, accurate staging a prerequisite effective diagnosis and treatment adenocarcinoma. Previous research has used mainly single-modal data, such as gene expression classification prediction. Integrating multi-modal genetic data (gene RNA-seq, methylation copy number variation) from same patient provides possibility using A new machine learning method...

To solve the problem that safety data in process of coal mine production are easy to be maliciously tampered with and deleted, a consortium blockchain security monitoring system is proposed. The includes supervision department, builds favourable centralized decentralized mode, improves PBFT (Practical Byzantine Fault Tolerance) consensus mechanism implement practical production. evaluation shows architecture we proposed more appropriate efficient for Internet Things than traditional...

Deep convolution neural network (DCNN) technology has achieved great success in extracting buildings from aerial images. However, the current mainstream algorithms are not satisfactory feature extraction and classification of homesteads, especially complex rural scenarios. This study proposes a deep convolutional for homestead consisting detail branch, semantic boundary namely Multi-Branch Network (MBNet). Meanwhile, multi-task joint loss function is designed to constrain consistency bounds...

The emergence of image-based systems to improve diagnostic pathology precision, involving the intent label sets or bags instances, greatly hinges on Multiple Instance Learning for Whole Slide Images(WSIs). Contemporary works have shown excellent performance a neural network in MIL settings. Here, we examine graph-based model facilitate end-to-end learning and sample suitable patches using tile-based approach. We propose MIL-GNN employ Variational Auto-encoder with Gaussian mixture discover...

Abstract Purpose It is of great significance to accurately identify the KRAS gene mutation status for patients in tumor prognosis and personalized treatment. Although computer‐aided diagnosis system based on deep learning has gotten all‐round development, its performance still cannot meet current clinical application requirements due inherent limitations small‐scale medical image data set inaccurate lesion feature extraction. Therefore, our aim propose a model T2 MRI colorectal cancer (CRC)...

Traditional Chinese medicine (TCM) prescriptions have made great contributions to the treatment of diseases and health preservation. To alleviate shortage TCM resources improve professionalism automatically generated prescriptions, this paper deeply explores connection between symptoms herbs through deep learning technology, realizes automatic generation prescriptions. Particularly, considers significance referring similar underlying as herbal candidates in prescribing process. Moreover,...

Abstract Aiming at the problems of long time, high cost, invasive sampling damage, and easy emergence drug resistance in lung cancer gene detection, a reliable non-invasive prognostic method is proposed. Under guidance weakly supervised learning, deep metric learning graph clustering methods are used to learn higher-level abstract features CT imaging features. The unlabeled data dynamically updated through k-nearest label update strategy, transformed into weak continue process strong...

The generation of Traditional Chinese Medicine (TCM) prescription is one the most challenging tasks in research intelligent TCM. Current researches usually use transfer learning methods to apply relevant technology text this task simply and roughly. Either they need train a model with large number standardized dataset, or ignore domain knowledge expertise In order solve these problems, we propose hybrid neural network architecture for TCM generation— <italic...

Acute myocardial infarction (AMI) is one of the most serious and dangerous cardiovascular diseases. In recent years, number patients around world has been increasing significantly, among which people under age 45 have become high-risk group for sudden death AMI. AMI occurs quickly does not show obvious symptoms before onset. addition, postonset clinical testing also a complex invasive test, may cause some postoperative complications. Therefore, it necessary to propose noninvasive convenient...

In cricket, the region plays a significant role in ranking teams. The International Cricket Council (ICC) uses an ad-hoc points system to rank cricket teams, which entirely based on number of wins and losses match. ICC ignores strength weaknesses team across region. Even though relative accuracy is high, but they do not provide clearly defined method ranking. We proposed Region-wise Team Rank (RTR) Weighted (RWTR) intuition get more that match from stronger as compared against weaker & vice...

The prediction of lung tumour growth is the key to early treatment cancer. However, lack intuitive and clear judgments about future development often leads patients miss best opportunities. Combining characteristics variational autoencoder recurrent neural networks, this study proposes a via conditional autoencoder. proposed model uses reconstruct images at different times. Meanwhile, units are infer relationship between according chronological order. varies in patients, patients' condition...

Classification of benign and malignant pulmonary nodules is a critical task for developing Computer-Aided Diagnosis (CAD) system lung cancer. However, the intelligent diagnosis technology often limited by equipment space. Therefore, computer-aided model proposed running in Mobile Edge Computing (MEC) environment. The novel nodule classification framework improved Deep Convolutional Generative Adversarial Nets (DCGAN). Firstly, CT images after pre-processing are input into GAN to generate new...