AIMC Topic: Antigens, Neoplasm

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Prediction of biological activity of compounds containing a 1,3,5-triazinyl sulfonamide scaffold by artificial neural networks using simple molecular descriptors.

Bioorganic chemistry Dec 19, 2020
Simple molecular descriptors of extensive series of 1,3,5-triazinyl sulfonamide derivatives, based on the structure of sulfonamides and their physicochemical properties, were designed and calculated. These descriptors were successfully applied as inp...
Carbonic Anhydrase II Carbonic Anhydrase IX Carbonic Anhydrase Inhibitors Neural Networks, Computer Drug Design Antigens, Neoplasm Sulfonamides Triazines Humans
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Predicting cryptic ligand binding sites based on normal modes guided conformational sampling.

Proteins Dec 5, 2020
To greatly expand the druggable genome, fast and accurate predictions of cryptic sites for small molecules binding in target proteins are in high demand. In this study, we have developed a fast and simple conformational sampling scheme guided by norm...
Regression Analysis Area Under Curve Antigens, CD Molecular Dynamics Simulation Hepacivirus Coronavirus 3C Proteases Computational Biology Coronavirus Papain-Like Proteases Antigens, Neoplasm Humans Karyopherins Interleukin-2 Protein Tyrosine Phosphatase, Non-Receptor Type 1 Machine Learning Models, Statistical Exportin 1 Protein Algorithms Ligands Reproducibility of Results Elasticity SARS-CoV-2 Protein Conformation ROC Curve Receptors, Cytoplasmic and Nuclear Binding Sites
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IConMHC: a deep learning convolutional neural network model to predict peptide and MHC-I binding affinity.

Immunogenetics Jun 24, 2020
Tumor-specific neoantigens are mutated self-peptides presented by tumor cell major histocompatibility complex (MHC) molecules and are necessary to elicit host's anti-cancer cytotoxic T cell responses. It could be specifically recognized by neoantigen...
Peptides Deep Learning Protein Binding Antigens, Neoplasm Databases, Protein Humans Neural Networks, Computer Computer Simulation Reproducibility of Results Alleles Histocompatibility Antigens Class I Amino Acid Sequence
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The emerging roles of artificial intelligence in cancer drug development and precision therapy.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie May 20, 2020
Artificial intelligence (AI) has strong logical reasoning ability and independent learning ability, which can simulate the thinking process of the human brain. AI technologies such as machine learning can profoundly optimize the existing mode of anti...
Antigens, Neoplasm Clinical Decision-Making Signal Transduction Radiotherapy, Computer-Assisted Animals Humans Drug Development Neoplasms Drug Resistance, Neoplasm Artificial Intelligence Antineoplastic Agents Molecular Targeted Therapy Precision Medicine Immunotherapy
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Assessment of associations between clinical and immune microenvironmental factors and tumor mutation burden in resected nonsmall cell lung cancer by applying machine learning to whole-slide images.

Cancer medicine May 12, 2020
BACKGROUND: It is unclear whether clinical factors and immune microenvironment (IME) factors are associated with tumor mutation burden (TMB) in patients with nonsmall cell lung cancer (NSCLC).
Carcinoembryonic Antigen Smokers Exome Sequencing Keratin-19 Ex-Smokers Lymphocytes, Tumor-Infiltrating Regression Analysis Mutation Forkhead Transcription Factors Non-Smokers Adult Tumor Microenvironment Male Middle Aged Lung Neoplasms B7-H1 Antigen Antigens, Neoplasm Adenocarcinoma Carcinoma, Non-Small-Cell Lung Machine Learning Female Carcinoma, Squamous Cell Aged Aged, 80 and over Humans
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iTTCA-Hybrid: Improved and robust identification of tumor T cell antigens by utilizing hybrid feature representation.

Analytical biochemistry Apr 22, 2020
In spite of the repertoire of existing cancer therapies, the ongoing recurrence and new cases of cancer poses a challenging health concern that prompts for novel and effective treatment. Cancer immunotherapy represents a promising venue for treatment...
Immunotherapy Antigens, Neoplasm Histocompatibility Antigens Class I Humans Machine Learning T-Lymphocytes Neoplasms
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pVACtools: A Computational Toolkit to Identify and Visualize Cancer Neoantigens.

Cancer immunology research Jan 6, 2020
Identification of neoantigens is a critical step in predicting response to checkpoint blockade therapy and design of personalized cancer vaccines. This is a cross-disciplinary challenge, involving genomics, proteomics, immunology, and computational a...
Artificial Intelligence Software Algorithms Neoplasms Humans Computational Biology Data Mining Neural Networks, Computer Immunotherapy Cancer Vaccines Antigens, Neoplasm Mutation
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High-Throughput Prediction of MHC Class I and II Neoantigens with MHCnuggets.

Cancer immunology research Dec 23, 2019
Computational prediction of binding between neoantigen peptides and major histocompatibility complex (MHC) proteins can be used to predict patient response to cancer immunotherapy. Current neoantigen predictors focus on estimation of MHC binding aff...
Data Mining Algorithms Mutation, Missense Neoplasms Histocompatibility Antigens Class II Antigens, Neoplasm Protein Binding Humans CD8-Positive T-Lymphocytes Computational Biology Neural Networks, Computer Predictive Value of Tests Cancer Vaccines Artificial Intelligence Histocompatibility Antigens Class I Software
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DeepHLApan: A Deep Learning Approach for Neoantigen Prediction Considering Both HLA-Peptide Binding and Immunogenicity.

Frontiers in immunology Nov 1, 2019
Neoantigens play important roles in cancer immunotherapy. Current methods used for neoantigen prediction focus on the binding between human leukocyte antigens (HLAs) and peptides, which is insufficient for high-confidence neoantigen prediction. In th...
Deep Learning Humans Antigens, Neoplasm Neoplasms CD8-Positive T-Lymphocytes Epitopes, T-Lymphocyte Mutation Peptides HLA Antigens
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Machine-Learning Prediction of Tumor Antigen Immunogenicity in the Selection of Therapeutic Epitopes.

Cancer immunology research Sep 12, 2019
Current tumor neoantigen calling algorithms primarily rely on epitope/major histocompatibility complex (MHC) binding affinity predictions to rank and select for potential epitope targets. These algorithms do not predict for epitope immunogenicity usi...
Mice, Inbred C57BL Peptide Fragments Histocompatibility Antigens Class I Histocompatibility Antigens Class II T-Lymphocytes, Cytotoxic Humans Computational Biology Animals Machine Learning Cancer Vaccines Male Algorithms Antigens, Neoplasm Neoplasms Female Mice, Inbred BALB C Epitopes, T-Lymphocyte Mice
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