標(biāo)題: Titlebook: Bioinformatics for Cancer Immunotherapy; Methods and Protocol Sebastian Boegel Book 2020 Springer Science+Business Media, LLC, part of Spri [打印本頁(yè)] 作者: Lampoon 時(shí)間: 2025-3-21 16:34
書目名稱Bioinformatics for Cancer Immunotherapy影響因子(影響力)
書目名稱Bioinformatics for Cancer Immunotherapy影響因子(影響力)學(xué)科排名
書目名稱Bioinformatics for Cancer Immunotherapy網(wǎng)絡(luò)公開度
書目名稱Bioinformatics for Cancer Immunotherapy網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Bioinformatics for Cancer Immunotherapy被引頻次
書目名稱Bioinformatics for Cancer Immunotherapy被引頻次學(xué)科排名
書目名稱Bioinformatics for Cancer Immunotherapy年度引用
書目名稱Bioinformatics for Cancer Immunotherapy年度引用學(xué)科排名
書目名稱Bioinformatics for Cancer Immunotherapy讀者反饋
書目名稱Bioinformatics for Cancer Immunotherapy讀者反饋學(xué)科排名
作者: GROWL 時(shí)間: 2025-3-21 22:43 作者: Ballad 時(shí)間: 2025-3-22 01:57
https://doi.org/10.1007/978-3-642-93460-5ted MHC-I peptides. We have previously shown that this targeted search strategy improved peptide identifications for both mouse and human MHC ligands by greater than two-fold and is superior to traditional “no enzyme” search of reference proteomes (Murphy et al. J Res Proteome 16:1806–1816, 2017).作者: Defraud 時(shí)間: 2025-3-22 05:08 作者: 匍匐前進(jìn) 時(shí)間: 2025-3-22 11:56
The SysteMHC Atlas: a Computational Pipeline, a Website, and a Data Repository for Immunopeptidomic作者: patriarch 時(shí)間: 2025-3-22 16:55 作者: 顛簸地移動(dòng) 時(shí)間: 2025-3-22 21:04 作者: 網(wǎng)絡(luò)添麻煩 時(shí)間: 2025-3-23 01:02
Bioinformatics for Cancer Immunotherapy,f mutations and the subsequent prediction of potential epitopes, as well as methods for associated biomarker research, such as high-throughput sequencing of T-cell receptors (TCRs), followed by data analysis and the bioinformatics quantification of immune cell infiltration in cancer samples.作者: Custodian 時(shí)間: 2025-3-23 01:37 作者: 懸掛 時(shí)間: 2025-3-23 06:08 作者: 單挑 時(shí)間: 2025-3-23 13:00 作者: mastoid-bone 時(shí)間: 2025-3-23 17:49 作者: FLINT 時(shí)間: 2025-3-23 22:01 作者: TAIN 時(shí)間: 2025-3-23 23:57
https://doi.org/10.1007/978-3-642-93460-5sing public data from two cancer cell lines, here we show how TIminer, a framework to perform immunogenomics analyses, can be easily used to assemble and run customized pipelines to predict cancer neoantigens from multisample NGS data.作者: engrave 時(shí)間: 2025-3-24 03:42 作者: debunk 時(shí)間: 2025-3-24 09:32
SomaticSeq: An Ensemble and Machine Learning Method to Detect Somatic Mutations, just the coding regions), the number of sequencing errors easily outnumbers the number of real somatic mutations by orders of magnitudes. Here, we describe SomaticSeq, which incorporates multiple somatic mutation detection algorithms and then uses machine learning to vastly improve the accuracy of the somatic mutation call sets.作者: 線 時(shí)間: 2025-3-24 13:45 作者: 巫婆 時(shí)間: 2025-3-24 16:01 作者: 知識(shí) 時(shí)間: 2025-3-24 21:27
Identification of Epitope-Specific T Cells in T-Cell Receptor Repertoires,itate the study of T-cell epitope specificity, we developed a prediction tool, TCRex, that can identify epitope-specific T-cell receptors (TCRs) directly from TCR repertoire data and perform epitope-specificity enrichment analyses. This chapter details the use of the TCRex web tool.作者: VEIL 時(shí)間: 2025-3-25 00:51
The Clinical Potential of NMR Imaging,ighly accurate HLA typing solution, arcasHLA. We provide a detailed outline for practitioners using our protocol to perform HLA typing and demonstrate the applicability of arcasHLA in several clinical samples from tumors.作者: 使?jié)M足 時(shí)間: 2025-3-25 06:21 作者: 遠(yuǎn)足 時(shí)間: 2025-3-25 08:50 作者: objection 時(shí)間: 2025-3-25 15:18 作者: 轉(zhuǎn)換 時(shí)間: 2025-3-25 16:54 作者: 安心地散步 時(shí)間: 2025-3-25 20:33
Rapid High-Resolution Typing of Class I HLA Genes by Nanopore Sequencing, nanopore sequencing. The method features a novel algorithm for candidate allele selection, followed by error correction through consensus building. Here, we describe the protocol of using Athlon packaged in a VirtualBox image for the above application.作者: LURE 時(shí)間: 2025-3-26 02:29 作者: Exuberance 時(shí)間: 2025-3-26 04:28
Computational Deconvolution of Tumor-Infiltrating Immune Components with Bulk Tumor Gene Expression immune cell types in the tumor microenvironment: B cell, CD4+ T cell, CD8+ T cell, neutrophil, macrophage, and dendritic cell. We further introduce its associated webserver for convenient, user-friendly analysis of tumor immune infiltrates across multiple cancer types.作者: 我沒(méi)有強(qiáng)迫 時(shí)間: 2025-3-26 10:47
An Individualized Approach for Somatic Variant Discovery,Here, we describe an individualized approach for somatic variant discovery through the step-by-step usage of Personalized Reference Editor for Somatic Mutation discovery in cancer genomics (PRESM), a personalized reference editor for somatic mutation discovery in cancer genomes.作者: hauteur 時(shí)間: 2025-3-26 13:39 作者: G-spot 時(shí)間: 2025-3-26 17:12
HLApers: HLA Typing and Quantification of Expression with Personalized Index,eline which adapts widely used tools for analysis of standard RNA-seq data to infer HLA genotypes and estimate expression. By generating reliable expression estimates for each HLA allele that an individual carries, HLApers allows a better understanding of the relationship between HLA alleles and phenotypes manifested by an individual.作者: 過(guò)分 時(shí)間: 2025-3-26 22:05
Cell-Type Enrichment Analysis of Bulk Transcriptomes Using xCell,es to enrichment scores of 64 immune and stroma cell types across samples. Here, we described the method, discuss correct usage, and demonstrate an analysis of a cohort of peripheral blood mononuclear cells (PBMC).作者: Arctic 時(shí)間: 2025-3-27 03:23
Book 2020entification and immune cell analysis from high-throughput sequencing data for cancer immunotherapy. The chapters in this book cover topics that discuss the two emerging concepts in recognition of tumor cells using endogenous T cells: cancer vaccines against neo-antigens presented on HLA class I and作者: 不吉祥的女人 時(shí)間: 2025-3-27 05:16 作者: CT-angiography 時(shí)間: 2025-3-27 11:13
Kernspin-Tomographie in der Medizines from individual mutation callers using supervised machine learning. SMuRF has improved prediction accuracy for both somatic point mutations (single nucleotide variants; SNVs) and small insertions/deletions (indels) in cancer genomes and exomes. Here, we describe the method and provide a tutorial on the installation and application of SMuRF.作者: magnanimity 時(shí)間: 2025-3-27 15:20 作者: 印第安人 時(shí)間: 2025-3-27 20:32
,Nasopharynx und Gesichtssch?del,es to enrichment scores of 64 immune and stroma cell types across samples. Here, we described the method, discuss correct usage, and demonstrate an analysis of a cohort of peripheral blood mononuclear cells (PBMC).作者: AGONY 時(shí)間: 2025-3-27 22:36 作者: 諂媚于人 時(shí)間: 2025-3-28 04:35 作者: 袋鼠 時(shí)間: 2025-3-28 10:07
An Individualized Approach for Somatic Variant Discovery, and inferring likelihoods from statistical models. False positives, however, are common among various tools as mismatches with the universal reference can also occur due to germline variants. Previous applications of personalized reference construction are not amenable with cancer genome analysis. 作者: 投票 時(shí)間: 2025-3-28 11:33
Ensemble-Based Somatic Mutation Calling in Cancer Genomes,ogeneity in the tumors. Indeed, recent independent benchmark studies have revealed low concordance between different somatic mutation callers. Here, we describe .omatic .tation calling method using a .andom .orest (SMuRF), a portable ensemble method that combines the predictions and auxiliary featur作者: 放氣 時(shí)間: 2025-3-28 18:24
SomaticSeq: An Ensemble and Machine Learning Method to Detect Somatic Mutations,sue and its matched normal (commonly blood or adjacent normal tissue) for side-by-side comparison. However, when interrogating entire genomes (or even just the coding regions), the number of sequencing errors easily outnumbers the number of real somatic mutations by orders of magnitudes. Here, we de作者: 對(duì)待 時(shí)間: 2025-3-28 19:58
HLA Typing from RNA Sequencing and Applications to Cancer,t of cancer and treatment therapies, the HLA locus plays a critical role in tumor recognition and tolerance mechanisms. In silico HLA class I and class II typing, as well as expression quantification from next-generation RNA sequencing, can therefore have great potential clinical applications. Howev作者: Arctic 時(shí)間: 2025-3-29 02:23 作者: Agnosia 時(shí)間: 2025-3-29 06:16 作者: 陰郁 時(shí)間: 2025-3-29 08:36
High-Throughput MHC I Ligand Prediction Using MHCflurry,r integration into high-throughput bioinformatic pipelines. Users can download models fit to publicly available data or train predictors on their own affinity measurements or mass spec datasets. This chapter gives a tutorial on essential MHCflurry functionality, including generating predictions, tra作者: 心神不寧 時(shí)間: 2025-3-29 11:48 作者: Budget 時(shí)間: 2025-3-29 17:10 作者: ineptitude 時(shí)間: 2025-3-29 19:45 作者: DRILL 時(shí)間: 2025-3-30 01:41
Identification of Epitope-Specific T Cells in T-Cell Receptor Repertoires,epitope-specific T cells has been instrumental in our understanding of cancer immunology and the development of personalized immunotherapies. To facilitate the study of T-cell epitope specificity, we developed a prediction tool, TCRex, that can identify epitope-specific T-cell receptors (TCRs) direc作者: 光明正大 時(shí)間: 2025-3-30 04:05
Immunedeconv: An R Package for Unified Access to Computational Methods for Estimating Immune Cell Fesults of several methods. Given numerous dependencies and differences in input and output format of the various computational methods, comparative analyses can become quite complex. This motivated us to develop ., an R package providing uniform and user-friendly access to seven state-of-the-art com作者: 商談 時(shí)間: 2025-3-30 10:14
EPIC: A Tool to Estimate the Proportions of Different Cell Types from Bulk Gene Expression Data,ained from bulk samples containing a mixture of cell types. Knowledge of the proportions of these cell types is crucial as they are key determinants of the disease evolution and response to treatment. Moreover, heterogeneity in cell type proportions across samples is an important confounding factor 作者: LAVA 時(shí)間: 2025-3-30 14:10 作者: Accommodation 時(shí)間: 2025-3-30 18:09 作者: CHECK 時(shí)間: 2025-3-31 00:39 作者: Hallowed 時(shí)間: 2025-3-31 04:15 作者: upstart 時(shí)間: 2025-3-31 07:02
Bioinformatics for Cancer Immunotherapy978-1-0716-0327-7Series ISSN 1064-3745 Series E-ISSN 1940-6029 作者: FLAG 時(shí)間: 2025-3-31 12:58
https://doi.org/10.1007/978-3-642-93460-5es. It is a Dockerized end-to-end pipeline that takes as input raw tumor/normal sequencing data. It is currently used in three clinical trials (NCT02721043, NCT03223103, and NCT03359239). In this chapter, we describe how to install and use OpenVax, as well as how to interpret the generated results.作者: 親屬 時(shí)間: 2025-3-31 14:27 作者: 不近人情 時(shí)間: 2025-3-31 20:43 作者: SMART 時(shí)間: 2025-4-1 00:49
Kernspin-Tomographie in der Medizinogeneity in the tumors. Indeed, recent independent benchmark studies have revealed low concordance between different somatic mutation callers. Here, we describe .omatic .tation calling method using a .andom .orest (SMuRF), a portable ensemble method that combines the predictions and auxiliary featur作者: Radiation 時(shí)間: 2025-4-1 02:50
The Clinical Potential of NMR Imaging,sue and its matched normal (commonly blood or adjacent normal tissue) for side-by-side comparison. However, when interrogating entire genomes (or even just the coding regions), the number of sequencing errors easily outnumbers the number of real somatic mutations by orders of magnitudes. Here, we de作者: brachial-plexus 時(shí)間: 2025-4-1 07:03
The Clinical Potential of NMR Imaging,t of cancer and treatment therapies, the HLA locus plays a critical role in tumor recognition and tolerance mechanisms. In silico HLA class I and class II typing, as well as expression quantification from next-generation RNA sequencing, can therefore have great potential clinical applications. Howev作者: BLINK 時(shí)間: 2025-4-1 13:27
The Clinical Potential of NMR Imaging,le-molecule sequencing and ultralong reads. The technology is ideal for the typing of human leukocyte antigen (HLA) genes for transplantation and cancer immunotherapy. However, such applications have been hindered by the high error rate of nanopore sequencing reads. We developed the workflow and bio作者: 假裝是我 時(shí)間: 2025-4-1 16:16 作者: 植物學(xué) 時(shí)間: 2025-4-1 21:31
https://doi.org/10.1007/978-3-642-69100-3r integration into high-throughput bioinformatic pipelines. Users can download models fit to publicly available data or train predictors on their own affinity measurements or mass spec datasets. This chapter gives a tutorial on essential MHCflurry functionality, including generating predictions, tra作者: 欺騙世家 時(shí)間: 2025-4-2 02:39
https://doi.org/10.1007/978-3-642-93460-5neration sequencing (NGS) data is possible but requires the assembly of complex, multistep computational pipelines and extensive data preprocessing. Using public data from two cancer cell lines, here we show how TIminer, a framework to perform immunogenomics analyses, can be easily used to assemble 作者: Alcove 時(shí)間: 2025-4-2 06:15
https://doi.org/10.1007/978-3-642-93460-5es. It is a Dockerized end-to-end pipeline that takes as input raw tumor/normal sequencing data. It is currently used in three clinical trials (NCT02721043, NCT03223103, and NCT03359239). In this chapter, we describe how to install and use OpenVax, as well as how to interpret the generated results.作者: ARY 時(shí)間: 2025-4-2 08:07
