Parallel Decision Tree with Application to Water Quality Data Analysis

Abstract. Decision tree is a popular classification technique in many applications, such as retail target marketing, fraud detection and design of telecommunication service plans. With the information exploration, the existing classification algorithms are not good enough to tackle large data set. In order to deal with the problem, many researchers try to design efficient parallel classification algorithms. Based on the current and powerful parallel programming framework -MapReduce, we propose a parallel ID3 classification algorithm(PID3 for short). We use water quality data monitoring the Changjiang River which contains 17 branches as experimental data. As the data are time series, we process the data to attribute data before using the decision tree. The experimental results demonstrate that the proposed algorithm can scale well and efficiently process large datasets on commodity hardware

Chitosan-functionalized bioplatforms and hydrogels in breast cancer: immunotherapy, phototherapy and clinical perspectives

Breast cancer is the most common and malignant tumor among women. Chitosan (CS)-based nanoparticles have been introduced into breast cancer therapy as a way to increase the targeted delivery of drugs and genes to the tumor site. CS nanostructures suppress tumorigenesis by enhancing both the targeted delivery of cargo (drug and gene) and its accumulation in tumor cells. The tumor cells internalize CS-based nanoparticles through endocytosis. Moreover, chitosan nanocarriers can also induce phototherapy-mediated tumor ablation. Smart and multifunctional types of CS nanoparticles, including pH-, light- and redox-responsive nanoparticles, can be used to improve the potential for breast cancer removal. In addition, the acceleration of immunotherapy by CS nanoparticles has also been achieved, and there is potential to develop CS-nanoparticle hydrogels that can be used to suppress tumorigenesis

Multi_CycGT: A Deep Learning-Based Multimodal Model for Predicting the Membrane Permeability of Cyclic Peptides

Cyclic peptides are gaining attention for their strong binding affinity, low toxicity, and ability to target “undruggable” proteins; however, their therapeutic potential against intracellular targets is constrained by their limited membrane permeability, and researchers need much time and money to test this property in the laboratory. Herein, we propose an innovative multimodal model called Multi_CycGT, which combines a graph convolutional network (GCN) and a transformer to extract one- and two-dimensional features for predicting cyclic peptide permeability. The extensive benchmarking experiments show that our Multi_CycGT model can attain state-of-the-art performance, with an average accuracy of 0.8206 and an area under the curve of 0.8650, and demonstrates satisfactory generalization ability on several external data sets. To the best of our knowledge, it is the first deep learning-based attempt to predict the membrane permeability of cyclic peptides, which is beneficial in accelerating the design of cyclic peptide active drugs in medicinal chemistry and chemical biology applications