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

Hierarchical graph attention network for miRNA-disease association prediction

(2022) Zhengwei Li et al.   MOLECULAR THERAPY

MGF6mARice: prediction of DNA N6-methyladenine sites in rice by exploiting molecular graph feature and residual block

(2022) Mengya Liu et al.   BRIEFINGS IN BIOINFORMATICS

MDA-GCNFTG: identifying miRNA-disease associations based on graph convolutional networks via graph sampling through the feature and topology graph

(2021) Yanyi Chu et al.   BRIEFINGS IN BIOINFORMATICS

SGNNMD: signed graph neural network for predicting deregulation types of miRNA-disease associations

(2021) Guangzhan Zhang et al.   BRIEFINGS IN BIOINFORMATICS

A deep learning method for repurposing antiviral drugs against new viruses via multi-view nonnegative matrix factorization and its application to SARS-CoV-2

(2021) Xiaorui Su et al.   BRIEFINGS IN BIOINFORMATICS

Predicting miRNA–disease associations based on graph random propagation network and attention network

(2021) Tangbo Zhong et al.   BRIEFINGS IN BIOINFORMATICS

dbMCS: A Database for Exploring the Mutation Markers of Anti-Cancer Drug Sensitivity

(2021) Yueyue Shen et al.   IEEE Journal of Biomedical and Health Informatics

The mutational constraint spectrum quantified from variation in 141,456 humans

(2020) Konrad J. Karczewski et al.   NATURE

PredCID: prediction of driver frameshift indels in human cancer

(2020) Zhenyu Yue et al.   BRIEFINGS IN BIOINFORMATICS

Predicting drug–disease associations through layer attention graph convolutional network

(2020) Zhouxin Yu et al.   BRIEFINGS IN BIOINFORMATICS

A new COVID-19 Patients Detection Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier

(2020) Warda M. Shaban et al.   KNOWLEDGE-BASED SYSTEMS

LipoFNT: Lipoylation Sites Identification with Flexible Neural Tree

(2019) Wenzheng Bao et al.   COMPLEXITY

Pathogenicity and functional impact of non-frameshifting insertion/deletion variation in the human genome

(2019) Kymberleigh A. Pagel et al.   PLoS Computational Biology

Graph embedding on biomedical networks: methods, applications and evaluations

(2019) Xiang Yue et al.   BIOINFORMATICS

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

(2018) Zhenyu Yue et al.   BRIEFINGS IN BIOINFORMATICS

A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications

(2018) HongYun Cai et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

The Proposal to Lower P Value Thresholds to .005

(2018) John P. A. Ioannidis   JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION

Learning k for kNN Classification

(2017) Shichao Zhang et al.   ACM Transactions on Intelligent Systems and Technology

The international Genome sample resource (IGSR): A worldwide collection of genome variation incorporating the 1000 Genomes Project data

(2016) Laura Clarke et al.   NUCLEIC ACIDS RESEARCH

DDIG-in: detecting disease-causing genetic variations due to frameshifting indels and nonsense mutations employing sequence and structural properties at nucleotide and protein levels

(2015) Lukas Folkman et al.   BIOINFORMATICS

Assessing the Pathogenicity of Insertion and Deletion Variants with the Variant Effect Scoring Tool (VEST-Indel)

(2015) Christopher Douville et al.   HUMAN MUTATION

ClinVar: public archive of interpretations of clinically relevant variants

(2015) Melissa J. Landrum et al.   NUCLEIC ACIDS RESEARCH

A general framework for estimating the relative pathogenicity of human genetic variants

(2014) Martin Kircher et al.   NATURE GENETICS

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

(2013) S. B. Montgomery et al.   GENOME RESEARCH

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

(2013) Jing Hu et al.   PLoS One

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

(2009) H. Carter et al.   CANCER RESEARCH

On the sampling distribution of resubstitution and leave-one-out error estimators for linear classifiers

(2009) Amin Zollanvari et al.   PATTERN RECOGNITION