Bioinformatics for Cancer Immunotherapy.
暂无分享,去创建一个
[1] D. Hanahan,et al. The Hallmarks of Cancer , 2000, Cell.
[2] A. Ben-Hur,et al. METHOD Open Access , 2014 .
[3] Thomas Zichner,et al. DELLY: structural variant discovery by integrated paired-end and split-read analysis , 2012, Bioinform..
[4] J. Castle,et al. Exploiting the mutanome for tumor vaccination. , 2012, Cancer research.
[5] J. Castle,et al. HLA typing from RNA-Seq sequence reads , 2012, Genome Medicine.
[6] Juw Won Park,et al. MATS: a Bayesian framework for flexible detection of differential alternative splicing from RNA-Seq data , 2012, Nucleic acids research.
[7] Bernhard Y. Renard,et al. Confidence-based Somatic Mutation Evaluation and Prioritization , 2012, PLoS Comput. Biol..
[8] Ravi Vijaya Satya,et al. Comparison of somatic mutation calling methods in amplicon and whole exome sequence data , 2014, BMC Genomics.
[9] O. Lund,et al. NetMHCIIpan-3.0, a common pan-specific MHC class II prediction method including all three human MHC class II isotypes, HLA-DR, HLA-DP and HLA-DQ , 2013, Immunogenetics.
[10] Robert A Holt,et al. Sequence analysis of T-cell repertoires in health and disease , 2013, Genome Medicine.
[11] A. Sivachenko,et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples , 2013, Nature Biotechnology.
[12] H. Rammensee,et al. The regulatory landscape for actively personalized cancer immunotherapies , 2013, Nature Biotechnology.
[13] J. Sidney,et al. Genomic and bioinformatic profiling of mutational neoepitopes reveals new rules to predict anticancer immunogenicity , 2014, The Journal of experimental medicine.
[14] Mark M Davis,et al. Linking T-cell receptor sequence to functional phenotype at the single-cell level , 2014, Nature Biotechnology.
[15] Ö. Türeci,et al. Mutanome Engineered RNA Immunotherapy: Towards Patient-Centered Tumor Vaccination , 2015, Journal of immunology research.
[16] J. Castle,et al. Mutant MHC class II epitopes drive therapeutic immune responses to cancer , 2015, Nature.
[17] Mingming Jia,et al. COSMIC: exploring the world's knowledge of somatic mutations in human cancer , 2014, Nucleic Acids Res..
[18] David E. Fisher,et al. Precision medicine for cancer with next-generation functional diagnostics , 2015, Nature Reviews Cancer.
[19] Ash A. Alizadeh,et al. Robust enumeration of cell subsets from tissue expression profiles , 2015, Nature Methods.
[20] Michael Schantz Klausen,et al. LYRA, a webserver for lymphocyte receptor structural modeling , 2015, Nucleic Acids Res..
[21] T. Chan,et al. The role of neoantigens in response to immune checkpoint blockade. , 2016, International immunology.
[22] Nicolai J. Birkbak,et al. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade , 2016, Science.
[23] P. Laurent-Puig,et al. Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression , 2016, Genome Biology.
[24] Jun S. Liu,et al. Comprehensive analyses of tumor immunity: implications for cancer immunotherapy , 2016, Genome Biology.
[25] R. Yamada,et al. HLA‐HD: An accurate HLA typing algorithm for next‐generation sequencing data , 2017, Human mutation.
[26] A. Butte,et al. xCell: digitally portraying the tissue cellular heterogeneity landscape , 2017, Genome Biology.
[27] Jennifer G. Abelin,et al. Mass Spectrometry Profiling of HLA‐Associated Peptidomes in Mono‐allelic Cells Enables More Accurate Epitope Prediction , 2017, Immunity.
[28] Z. Szallasi,et al. An Analysis of Natural T Cell Responses to Predicted Tumor Neoepitopes , 2017, Front. Immunol..
[29] Xiaohui Xie,et al. HLA class I binding prediction via convolutional neural networks , 2017, bioRxiv.
[30] J. Utikal,et al. Personalized RNA mutanome vaccines mobilize poly-specific therapeutic immunity against cancer , 2017, Nature.
[31] M. Nielsen,et al. NetMHCpan-4.0: Improved Peptide–MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data , 2017, The Journal of Immunology.
[32] T. Karlsen,et al. Overview of methodologies for T-cell receptor repertoire analysis , 2017, BMC Biotechnology.
[33] Zoltan Szallasi,et al. MuPeXI: prediction of neo-epitopes from tumor sequencing data , 2017, Cancer Immunology, Immunotherapy.
[34] S. Paik,et al. Neopepsee: accurate genome-level prediction of neoantigens by harnessing sequence and amino acid immunogenicity information , 2018, Annals of oncology : official journal of the European Society for Medical Oncology.
[35] Z. Trajanoski,et al. Quantifying tumor-infiltrating immune cells from transcriptomics data , 2018, Cancer Immunology, Immunotherapy.
[36] M. Nielsen,et al. NetTCR: sequence-based prediction of TCR binding to peptide-MHC complexes using convolutional neural networks , 2018, bioRxiv.
[37] W. Curran,et al. T cell receptor sequencing of activated CD8 T cells in the blood identifies tumor-infiltrating clones that expand after PD-1 therapy and radiation in a melanoma patient , 2018, Cancer Immunology, Immunotherapy.
[38] S. M. Toor,et al. Immune checkpoint inhibitors: recent progress and potential biomarkers , 2018, Experimental & Molecular Medicine.
[39] Jan Baumbach,et al. Comprehensive evaluation of cell-type quantification methods for immuno-oncology , 2018 .
[40] N. McGranahan,et al. Differential binding affinity of mutated peptides for MHC class I is a predictor of survival in advanced lung cancer and melanoma , 2017, Annals of oncology : official journal of the European Society for Medical Oncology.
[41] Thomas Colthurst,et al. A universal SNP and small-indel variant caller using deep neural networks , 2018, Nature Biotechnology.
[42] Yabin Jin,et al. TCR repertoire profiling of tumors, adjacent normal tissues, and peripheral blood predicts survival in nasopharyngeal carcinoma , 2018, Cancer Immunology, Immunotherapy.
[43] Joachim Weischenfeldt,et al. SvABA: genome-wide detection of structural variants and indels by local assembly , 2018, Genome research.
[44] Yeonseok Chung,et al. Future prospects of immune checkpoint blockade in cancer: from response prediction to overcoming resistance , 2018, Experimental & Molecular Medicine.
[45] Ö. Türeci,et al. Personalized vaccines for cancer immunotherapy , 2018, Science.
[46] Aurélien de Reyniès,et al. Quantitative Analyses of the Tumor Microenvironment Composition and Orientation in the Era of Precision Medicine , 2018, Front. Oncol..
[47] Yabin Jin,et al. Circulating CD8+ T-cell repertoires reveal the biological characteristics of tumors and clinical responses to chemotherapy in breast cancer patients , 2018, Cancer Immunology, Immunotherapy.
[48] Christopher T. Saunders,et al. Strelka2: fast and accurate calling of germline and somatic variants , 2018, Nature Methods.
[49] Yuxin Cui,et al. DeepSeqPan, a novel deep convolutional neural network model for pan-specific class I HLA-peptide binding affinity prediction , 2018, Scientific Reports.
[50] Yu Wang,et al. Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data , 2017, Genome Medicine.
[51] Vanessa Isabell Jurtz,et al. Computational Methods for Identification of T Cell Neoepitopes in Tumors. , 2019, Methods in molecular biology.
[52] Jan Baumbach,et al. Comprehensive evaluation of transcriptome-based cell-type quantification methods for immuno-oncology , 2019, Bioinform..