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。DLAB: deep learning methods for structure-based virtual screening of antibodies
Constantin Schneider et al.
BIOINFORMATICS (2022)
Antibody structure prediction using interpretable deep learning
Jeffrey A. Ruffolo et al.
PATTERNS (2022)
DeepTCR is a deep learning framework for revealing sequence concepts within T-cell repertoires
John-William Sidhom et al.
NATURE COMMUNICATIONS (2021)
Antibody design using LSTM based deep generative model from phage display library for affinity maturation
Koichiro Saka et al.
SCIENTIFIC REPORTS (2021)
Potential neutralizing antibodies discovered for novel corona virus using machine learning
Rishikesh Magar et al.
SCIENTIFIC REPORTS (2021)
Protein design and variant prediction using autoregressive generative models
Jung-Eun Shin et al.
NATURE COMMUNICATIONS (2021)
Optimization of therapeutic antibodies by predicting antigen specificity from antibody sequence via deep learning
Derek M. Mason et al.
NATURE BIOMEDICAL ENGINEERING (2021)
Humanization of antibodies using a machine learning approach on large-scale repertoire data
Claire Marks et al.
BIOINFORMATICS (2021)
Highly accurate protein structure prediction with AlphaFold
John Jumper et al.
NATURE (2021)
Accurate prediction of protein structures and interactions using a three-track neural network
Minkyung Baek et al.
SCIENCE (2021)
Machine Learning Detects Anti-DENV Signatures in Antibody Repertoire Sequences
Alexander Horst et al.
FRONTIERS IN ARTIFICIAL INTELLIGENCE (2021)
Antibody complementarity determining region design using high-capacity machine learning
Ge Liu et al.
BIOINFORMATICS (2020)
Geometric potentials from deep learning improve prediction of CDR H3 loop structures
Jeffrey A. Ruffolo et al.
BIOINFORMATICS (2020)
Antibody repertoire analysis of mouse immunization protocols using microfluidics and molecular genomics
Michael A. Asensio et al.
MABS (2019)
Capturing the differences between humoral immunity in the normal and tumor environments from repertoire-seq of B-cell receptors using supervised machine learning
Hiroki Konishi et al.
BMC BIOINFORMATICS (2019)
Predicting Splicing from Primary Sequence with Deep Learning
Kishore Jaganathan et al.
CELL (2019)
Antibody Specific B-Cell Epitope Predictions: Leveraging Information From Antibody-Antigen Protein Complexes
Martin Closter Jespersen et al.
FRONTIERS IN IMMUNOLOGY (2019)
Parapred: antibody paratope prediction using convolutional and recurrent neural networks
Edgar Liberis et al.
BIOINFORMATICS (2018)
Predicting enhancers with deep convolutional neural networks
Xu Min et al.
BMC BIOINFORMATICS (2017)
Prediction of delayed retention of antibodies in hydrophobic interaction chromatography from sequence using machine learning
Tushar Jain et al.
BIOINFORMATICS (2017)
ggseqlogo: a versatile R package for drawing sequence logos
Omar Wagih
BIOINFORMATICS (2017)
ANARCI: antigen receptor numbering and receptor classification
James Dunbar et al.
BIOINFORMATICS (2016)
Convolutional neural network architectures for predicting DNA-protein binding
Haoyang Zeng et al.
BIOINFORMATICS (2016)
PEDLA: predicting enhancers with a deep learning-based algorithmic framework
Feng Liu et al.
SCIENTIFIC REPORTS (2016)
msa: an R package for multiple sequence alignment
Ulrich Bodenhofer et al.
BIOINFORMATICS (2015)
Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
Babak Alipanahi et al.
NATURE BIOTECHNOLOGY (2015)
Prediction of VH-VL domain orientation for antibody variable domain modeling
Alexander Bujotzek et al.
PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS (2015)
DEEP: a general computational framework for predicting enhancers
Dimitrios Kleftogiannis et al.
NUCLEIC ACIDS RESEARCH (2015)
SAbDab: the structural antibody database
James Dunbar et al.
NUCLEIC ACIDS RESEARCH (2014)
Prediction of site-specific interactions in antibody-antigen complexes: the proABC method and server
Pier Paolo Olimpieri et al.
BIOINFORMATICS (2013)
Search and clustering orders of magnitude faster than BLAST
Robert C. Edgar
BIOINFORMATICS (2010)
IMGT/LIGM-DB, the IMGT (R) comprehensive database of immunoglobulin and T cell receptor nucleotide sequences
Veronique Giudicelli et al.
NUCLEIC ACIDS RESEARCH (2006)
Yet another numbering scheme for immunoglobulin variable domains:: An automatic modeling and analysis tool
A Honegger et al.
JOURNAL OF MOLECULAR BIOLOGY (2001)