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Development of in silico methodologies to predict the toxicity of novel proteins in the context of food and feed risk assessment - Annexes

Name: Development of in silico methodologies to predict the toxicity of novel proteins in the context of food and feed risk assessment - Annexes
Creator: University of Milan; INNOVATUNE, Italy
Published: 2024-10-14T00:00:00+08:00

Zenodo2024-10-14 更新2026-04-07 收录

蛋白质预测

食品风险评估

数据链接：

https://zenodo.org/doi/10.5281/zenodo.13904499 数据链接链接失效反馈

官方服务：

资源简介：

EU; en; xlsx; NIF@efsa.europa.eu

应用场景：

提供机构：

University of Milan; INNOVATUNE, Italy

创建时间：

2024-10-14

蛋白质预测

原生生物

BRAKER1 predicted proteins of Naegleria fowleri ATCC 30894

DataCite Commons2025-06-01 更新80

sj-txt-4-bbi-10.1177_11779322211045878 – Supplemental material for ProTG4: A Web Server to Approximate the Sequence of a Generic Protein From an in Silico Library of Translatable G-Quadruplex (TG4)-Mapped Peptides

蛋白质预测

G-四链体分析

Supplemental material, sj-txt-4-bbi-10.1177_11779322211045878 for ProTG4: A Web Server to Approximate the Sequence of a Generic Protein From an in Silico Library of Translatable G-Quadruplex (TG4)-Map

DataCite Commons2024-08-28 更新70

Data_Sheet_1_A Novel Model for Identifying Essential Proteins Based on Key Target Convergence Sets.ZIP

蛋白质预测

蛋白质相互作用网络

In recent years, many computational models have been designed to detect essential proteins based on protein-protein interaction (PPI) networks. However, due to the incompleteness of PPI networks, the

NIAID Data Ecosystem40

Coding potential and putative proteins of “Rosavirus B” and “Rosavirus C” compared to Rosavirus A .

病毒比较基因组学

蛋白质预测

Coding potential and putative proteins of “Rosavirus B” and “Rosavirus C” compared to Rosavirus A.

NIAID Data Ecosystem70

Table S1 - A Unified Multitask Architecture for Predicting Local Protein Properties

蛋白质预测

多任务评估

The table lists, for each prediction task, the per-residue per-class performance, i.e. (precision, recall, F1, total number of provided positive amino acids, true positive) averaged per cross-validati

NIAID Data Ecosystem60

Development of in silico methodologies to predict the toxicity of novel proteins in the context of food and feed risk assessment - Annexes

资源简介：

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