Evidence map›Paper›PMID 37258913›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2023

TSNAD and TSNAdb: The Useful Toolkit for Clinical Application of Tumor-Specific Neoantigens.

Jingcheng Wu, Zhan Zhou

Abstract read
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Jingcheng WuInnovative Institute for Artificial Intelligence in Medicine and Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China.
Zhan ZhouInnovative Institute for Artificial Intelligence in Medicine and Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang, China. zhanzhou@zju.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tumor-specific neoantigens play important roles in tumor immunotherapy. How to predict neoantigens accurately and efficiently has attracted much attention. TSNAD is the first one-stop neoantigen prediction tool from next-generation sequencing data, and TSNAdb provides both predicted and validated neoantigens based on pan-cancer immunogenomics analyses. In this chapter, we describe the usage of TSNAD and TSNAdb for the clinical application of neoantigens. The latest version of TSNAD is available at https://pgx.zju.edu.cn/tsnad , and the latest version of TSNAdb is available at https://pgx.zju.edu.cn/tsnadb .

Indexed as

Antigens, NeoplasmNeoplasmsHigh-Throughput Nucleotide SequencingHumansImmunotherapyAntigens, NeoplasmBioinformaticsDatabaseNeoantigenPredictionTumor immunotherapy

Identifiers

PMID37258913

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.