相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。An Ensemble Framework for Improving the Prediction of Deleterious Synonymous Mutation
Na Cheng et al.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY (2022)
usDSM: a novel method for deleterious synonymous mutation prediction using undersampling scheme
Xi Tang et al.
BRIEFINGS IN BIOINFORMATICS (2021)
AI-Driver: an ensemble method for identifying driver mutations in personal cancer genomes
Haoxuan Wang et al.
NAR GENOMICS AND BIOINFORMATICS (2020)
iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data
Zhen Chen et al.
BRIEFINGS IN BIOINFORMATICS (2020)
Comparison and integration of computational methods for deleterious synonymous mutation prediction
Na Cheng et al.
BRIEFINGS IN BIOINFORMATICS (2020)
Computational identification of deleterious synonymous variants in human genomes using a feature-based approach
Fang Shi et al.
BMC MEDICAL GENOMICS (2019)
A Single Synonymous Variant (c.354G>A [p.P118P]) in ADAMTS13 Confers Enhanced Specific Activity
Ryan Hunt et al.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES (2019)
CADD: predicting the deleteriousness of variants throughout the human genome
Philipp Rentzsch et al.
NUCLEIC ACIDS RESEARCH (2019)
A single synonymous mutation determines the phosphorylation and stability of the nascent protein
Konstantinos Karakostis et al.
JOURNAL OF MOLECULAR CELL BIOLOGY (2019)
isGPT: An optimized model to identify sub-Golgi protein types using SVM and Random Forest based feature selection
M. Saifur Rahman et al.
ARTIFICIAL INTELLIGENCE IN MEDICINE (2018)
ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides
Leyi Wei et al.
BIOINFORMATICS (2018)
Porcine IGF1 synonymous mutation alter gene expression and protein binding affinity with IGF1R
Yunyun Cheng et al.
INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES (2018)
FATHMM-XF: accurate prediction of pathogenic point mutations via extended features
Mark F. Rogers et al.
BIOINFORMATICS (2018)
regSNPs-splicing: a tool for prioritizing synonymous single-nucleotide substitution
Xinjun Zhang et al.
HUMAN GENETICS (2017)
The Human Gene Mutation Database: towards a comprehensive repository of inherited mutation data for medical research, genetic diagnosis and next-generation sequencing studies
Peter D. Stenson et al.
HUMAN GENETICS (2017)
PhD-SNPg: a webserver and lightweight tool for scoring single nucleotide variants
Emidio Capriotti et al.
NUCLEIC ACIDS RESEARCH (2017)
Annotating pathogenic non-coding variants in genic regions
Sahar Gelfman et al.
NATURE COMMUNICATIONS (2017)
ClinVar: public archive of interpretations of clinically relevant variants
Melissa J. Landrum et al.
NUCLEIC ACIDS RESEARCH (2016)
dbDSM: a manually curated database for deleterious synonymous mutations
Pengbo Wen et al.
BIOINFORMATICS (2016)
An integrative approach to predicting the functional effects of non-coding and coding sequence variation
Hashem A. Shihab et al.
BIOINFORMATICS (2015)
VariSNP, A Benchmark Database for Variations From dbSNP
Gerard C. P. Schaafsma et al.
HUMAN MUTATION (2015)
Predicting effects of noncoding variants with deep learning-based sequence model
Jian Zhou et al.
NATURE METHODS (2015)
DANN: a deep learning approach for annotating the pathogenicity of genetic variants
Daniel Quang et al.
BIOINFORMATICS (2015)
The human splicing code reveals new insights into the genetic determinants of disease
Hui Y. Xiong et al.
SCIENCE (2015)
Functional annotation of noncoding sequence variants
Graham R. S. Ritchie et al.
NATURE METHODS (2014)
Identification of deleterious synonymous variants in human genomes
Orion J. Buske et al.
BIOINFORMATICS (2013)
CD-HIT Suite: a web server for clustering and comparing biological sequences
Ying Huang et al.
BIOINFORMATICS (2010)
Solving the riddle of codon usage preferences: a test for translational selection
M dos Reis et al.
NUCLEIC ACIDS RESEARCH (2004)
Greedy function approximation: A gradient boosting machine
JH Friedman
ANNALS OF STATISTICS (2001)
分享论文
