ME-ACP: Multi-view neural networks with ensemble model for identification of anticancer peptides. 2022

Guanwen Feng, and Hang Yao, and Chaoneng Li, and Ruyi Liu, and Rungen Huang, and Xiaopeng Fan, and Ruiquan Ge, and Qiguang Miao
Xi'an Key Laboratory of Big Data and Intelligent Vision, School of Computer Science and Technology, Xidian University, Xi'an, China.

Cancer remains one of the most threatening diseases, which kills millions of lives every year. As a promising perspective for cancer treatments, anticancer peptides (ACPs) overcome a lot of disadvantages of traditional treatments. However, it is time-consuming and expensive to identify ACPs through conventional experiments. Hence, it is urgent and necessary to develop highly effective approaches to accurately identify ACPs in large amounts of protein sequences. In this work, we proposed a novel and effective method named ME-ACP which employed multi-view neural networks with ensemble model to identify ACPs. Firstly, we employed residue level and peptide level features preliminarily with ensemble models based on lightGBMs. Then, the outputs of lightGBM classifiers were fed into a hybrid deep neural network (HDNN) to identify ACPs. The experiments on independent test datasets demonstrated that ME-ACP achieved competitive performance on common evaluation metrics.

UI MeSH Term Description Entries
D009369 Neoplasms New abnormal growth of tissue. Malignant neoplasms show a greater degree of anaplasia and have the properties of invasion and metastasis, compared to benign neoplasms. Benign Neoplasm,Cancer,Malignant Neoplasm,Tumor,Tumors,Benign Neoplasms,Malignancy,Malignant Neoplasms,Neoplasia,Neoplasm,Neoplasms, Benign,Cancers,Malignancies,Neoplasias,Neoplasm, Benign,Neoplasm, Malignant,Neoplasms, Malignant
D010455 Peptides Members of the class of compounds composed of AMINO ACIDS joined together by peptide bonds between adjacent amino acids into linear, branched or cyclical structures. OLIGOPEPTIDES are composed of approximately 2-12 amino acids. Polypeptides are composed of approximately 13 or more amino acids. PROTEINS are considered to be larger versions of peptides that can form into complex structures such as ENZYMES and RECEPTORS. Peptide,Polypeptide,Polypeptides
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000595 Amino Acid Sequence The order of amino acids as they occur in a polypeptide chain. This is referred to as the primary structure of proteins. It is of fundamental importance in determining PROTEIN CONFORMATION. Protein Structure, Primary,Amino Acid Sequences,Sequence, Amino Acid,Sequences, Amino Acid,Primary Protein Structure,Primary Protein Structures,Protein Structures, Primary,Structure, Primary Protein,Structures, Primary Protein
D000970 Antineoplastic Agents Substances that inhibit or prevent the proliferation of NEOPLASMS. Anticancer Agent,Antineoplastic,Antineoplastic Agent,Antineoplastic Drug,Antitumor Agent,Antitumor Drug,Cancer Chemotherapy Agent,Cancer Chemotherapy Drug,Anticancer Agents,Antineoplastic Drugs,Antineoplastics,Antitumor Agents,Antitumor Drugs,Cancer Chemotherapy Agents,Cancer Chemotherapy Drugs,Chemotherapeutic Anticancer Agents,Chemotherapeutic Anticancer Drug,Agent, Anticancer,Agent, Antineoplastic,Agent, Antitumor,Agent, Cancer Chemotherapy,Agents, Anticancer,Agents, Antineoplastic,Agents, Antitumor,Agents, Cancer Chemotherapy,Agents, Chemotherapeutic Anticancer,Chemotherapy Agent, Cancer,Chemotherapy Agents, Cancer,Chemotherapy Drug, Cancer,Chemotherapy Drugs, Cancer,Drug, Antineoplastic,Drug, Antitumor,Drug, Cancer Chemotherapy,Drug, Chemotherapeutic Anticancer,Drugs, Antineoplastic,Drugs, Antitumor,Drugs, Cancer Chemotherapy
D016571 Neural Networks, Computer A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming. Computational Neural Networks,Connectionist Models,Models, Neural Network,Neural Network Models,Neural Networks (Computer),Perceptrons,Computational Neural Network,Computer Neural Network,Computer Neural Networks,Connectionist Model,Model, Connectionist,Model, Neural Network,Models, Connectionist,Network Model, Neural,Network Models, Neural,Network, Computational Neural,Network, Computer Neural,Network, Neural (Computer),Networks, Computational Neural,Networks, Computer Neural,Networks, Neural (Computer),Neural Network (Computer),Neural Network Model,Neural Network, Computational,Neural Network, Computer,Neural Networks, Computational,Perceptron

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