PredinID: Predicting Pathogenic Inframe Indels in Human Through Graph Convolution Neural Network With Graph Sampling Technique-Reference-Cited by-同舟云学术

PredinID: Predicting Pathogenic Inframe Indels in Human Through Graph Convolution Neural Network With Graph Sampling Technique

Published:2023-09-01 Issue:5 Volume:20 Page:3226-3233
ISSN:1545-5963
Container-title:IEEE/ACM Transactions on Computational Biology and Bioinformatics
language:
Short-container-title:IEEE/ACM Trans. Comput. Biol. and Bioinf.

Author:

Yue Zhenyu¹^ORCID,Xiang Ying¹^ORCID,Chen Guojun¹^ORCID,Wang Xiaosong¹^ORCID,Li Ke²^ORCID,Zhang Youhua¹^ORCID

Affiliation:

1. School of Information and Computer, Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, Anhui, China

2. School of Information and Computer, Anhui Agricultural University, Hefei, Anhui, China

Funder

National Natural Science Foundation of China

Open-end Fund of Information Materials and Intelligent Sensing Laboratory of Anhui Province

Natural Science Young Foundation of Anhui

Natural Science Young Foundation of Anhui Agricultural University

Introduction and Stabilization of Talent Project of Anhui Agricultural University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Genetics,Biotechnology

Link

http://xplorestaging.ieee.org/ielx7/8857/10274759/10098914.pdf?arnumber=10098914

Reference43 articles.

1. The origin, evolution, and functional impact of short insertion–deletion variants identified in 179 human genomes

2. SIFT Indel: Predictions for the Functional Effects of Amino Acid Insertions/Deletions in Proteins

3. dbCID: a manually curated resource for exploring the driver indels in human cancer

4. Cancer-Specific High-Throughput Annotation of Somatic Mutations: Computational Prediction of Driver Missense Mutations

5. ClinVar: public archive of interpretations of clinically relevant variants