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Performance comparison with various methods for miRNA-phenotype association prediction.

NIAID Data Ecosystem2026-03-12 收录

数据链接：

https://figshare.com/articles/dataset/Performance_comparison_with_various_methods_for_miRNA-phenotype_association_prediction_/14726914 数据链接链接失效反馈

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资源简介：

Performance comparison with various methods for miRNA-phenotype association prediction.

应用场景：

创建时间：

2021-06-03

计算机视觉

算法评估

Comparisons of the annotation accuracy rates for each category of the PASCAL VOC 2012 dataset based on different algorithms after 100 training iterations.

NIAID Data Ecosystem60

MOESM1 of Inferring potential small moleculeâ miRNA association based on triple layer heterogeneous network

小分子-miRNA关联预测

生物信息学

Additional file 1: Table S1. We applied TLHNSMMA to prioritize all the candidate SMâ miRNA pairs from highly reliable SM2miR V1.0 database as training samples. This prediction result is released for f

DataCite Commons2024-02-20 更新50

Evaluation results from eight algorithms predicting the proportions of students in grades 9–12 reporting any same-sex sexual contacts in 2017.

青少年性健康预测

算法评估

Evaluation results from eight algorithms predicting the proportions of students in grades 9–12 reporting any same-sex sexual contacts in 2017.

Figshare2024-06-27 更新40

Comparisons of the annotation accuracy rates for each category of the PASCAL VOC 2012 dataset based on different algorithms after 100 training iterations.

计算机视觉基准

算法评估

Comparisons of the annotation accuracy rates for each category of the PASCAL VOC 2012 dataset based on different algorithms after 100 training iterations.

Figshare2020-06-01 更新50

2d synthetic dataset from Clustering: how much bias do we need?.

算法评估

偏差分析

Scientific investigations in medicine and beyond, increasingly require observations to be described by more features than can be simultaneously visualized. Simply reducing the dimensionality by projec

The Royal Society Figshare2020-10-15 更新40

Performance comparison with various methods for miRNA-phenotype association prediction.

资源简介：

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