ACP-DL: A Deep Learning Long Short-Term Memory Model to Predict Anticancer Peptides Using High-Efficiency Feature Representation

Abstract

Cancer is a well-known killer of human beings, which has led to countless deaths and misery. Anticancer peptides open a promising perspective for cancer treatment, and they have various attractive advantages. Conventional wet experiments are expensive and inefficient for finding and identifying novel anticancer peptides. There is an urgent need to develop a novel computational method to predict novel anticancer peptides. In this study, we propose a deep learning long short-term memory (LSTM) neural network model, ACP-DL, to effectively predict novel anticancer peptides. More specifically, to fully exploit peptide sequence information, we developed an efficient feature representation approach by integrating binary profile feature and k-mer sparse matrix of the reduced amino acid alphabet. Then we implemented a deep LSTM model to automatically learn how to identify anticancer peptides and nonanticancer peptides. To our knowledge, this is the first time that the deep LSTM model has been applied to predict anticancer peptides. It was demonstrated by cross-validation experiments that the proposed ACP-DL remarkably outperformed other comparison methods with high accuracy and satisfied specificity on benchmark datasets. In addition, we also contributed two new anticancer peptides benchmark datasets, ACP740 and ACP240, in this work. The source code and datasets are available at https://github.com/haichengyi/ACP-DL.

RPI-SE: a stacking ensemble learning framework for ncRNA-protein interactions prediction using sequence information

“…Deep learning shows excellent ability with large-scale data support in many fields, however, ncRPIs data sets generally don't have large scales, thus it's not very suitable or urgent need for deep learning methods. Previous research confirmed that in ncRPIs prediction task, tree-based model and SVM model can work well, and sequences contain enough information for predicting ncRPIs [25,26]. Traditional machine learning techniques have the potential to be explored for accuracy and interpretability in small sample learning tasks, especially ncRNA-protein interactions prediction task.…”

Construction of a genetic method to forecast the population health indicators based on neural network models

“…UDC 004.93 DOI: 10.15587/1729-4061.2020.197319 models suggested in known literary sources [1][2][3][4][5][6][7][8][9][10][11][12][13][14], is associated with certain limitations and requirements for target functions. When applying such methods, it is impossible to increase the accuracy of the forecast when parameters change, for example, in forecasting the dependence of indicators of public health on pollutant emissions in the air.…”

“…x rainfall is the rainfall quantity, x docs is the indicator that characterizes the impact of the number of doctors, x beds is the indicator characterizing the impact of total beds at stationary clinics. Article [10] proposes a classic regression analysis to derive a mathematical dependence of health indicators on pollutant emissions. It is shown that the classical method of stochastic forecasting of the morbidity explores interrelationships between indicators of morbidity and factors that predetermine it, when the dependence between them is not strictly functional and distorted by the influence of foreign factors.…”

“…Having analyzed works [10][11][12][13], we established that the set task can be resolved by the effective use of ANN because models based on artificial neural networks provide the possibility of processing multidimensional data of various types (thereby implementing the function of many variables), high adaptability to external changes, provide the possibility of synthesis of models with high approximating and generalizing properties. Therefore, it is necessary to develop a method for constructing neural network models based on empirical data, which would make it possible to synthesize the models of dependence of health indicators on the volumes of pollutant emissions.…”

GNMFLMI: Graph Regularized Nonnegative Matrix Factorization for Predicting LncRNA-MiRNA Interactions

“…In recent years, More and more studies have shown that both lncRNA and miRNA play critical roles in various biological processes and human complex diseases [19,20]. It has been systematically studied that the lncRNA-miRNA interactions exert regulation role in some human complex diseases [21][22][23]. In many diseased cells, lncRNA is discovered to have a certain quantitative relationship with some miRNAs, this quantitative relationship is closely associated with the occurrence and development of diseases [24].…”