Poincaré maps for analyzing complex hierarchies in single-cell data
10.1038/s41467-020-16822-4 · doi-reference
10.1101/2020.10.09.334243
10.1101/2020.10.09.334243 · doi-reference
10.1101/2021.02.17.431338
10.1101/2021.02.17.431338 · doi-reference
Characteristics of Sentence Length in Running Text
10.1093/llc/8.1.20 · doi-reference
American Gut: an Open Platform for Citizen Science Microbiome Research
10.1128/msystems.00031-18 · doi-reference
10.1128/aem.03006-05
10.1128/aem.03006-05 · doi-reference
10.1038/nature17995
10.1038/nature17995 · doi-reference
10.1093/bioinformatics/btu739
10.1093/bioinformatics/btu739 · doi-reference
10.1016/j.cell.2016.04.059
10.1016/j.cell.2016.04.059 · doi-reference
10.1016/j.cell.2014.10.002
10.1016/j.cell.2014.10.002 · doi-reference
Inferring rna sequence preferences for poorly studied rna-binding proteins based on co-evolution
10.1186/s12859-018-2091-8 · doi-reference
Affinity regression predicts the recognition code of nucleic acid–binding proteins
10.1038/nbt.3343 · doi-reference
10.1016/j.cell.2014.08.009
10.1016/j.cell.2014.08.009 · doi-reference
10.1101/2020.06.26.174417
10.1101/2020.06.26.174417 · doi-reference
10.1101/2021.02.12.430858
10.1101/2021.02.12.430858 · doi-reference
Unsupervised protein embeddings outperform hand-crafted sequence and structure features at predicting molecular function
10.1101/2020.04.07.028373 · doi-reference
10.1093/bioinformatics/btx431
10.1093/bioinformatics/btx431 · doi-reference
Netsurfp-2.0: Improved prediction of protein structural features by integrated deep learning
10.1002/prot.25674 · doi-reference
SDN2GO: An integrated deep learning model for protein function prediction
10.3389/fbioe.2020.00391 · doi-reference
10.1093/bioinformatics/btaa013
10.1093/bioinformatics/btaa013 · doi-reference
10.1101/2021.04.27.441365
10.1101/2021.04.27.441365 · doi-reference
BERT4Bitter: a bidirectional encoder representations from transformers (BERT)-based model for improving the prediction of bitter peptides
10.1093/bioinformatics/btab133 · doi-reference
DNABERT: pre-trained Bidirectional Encoder Representations from Transformers model for DNA-language in genome
10.1093/bioinformatics/btab083 · doi-reference
10.1101/2020.07.12.199554
10.1101/2020.07.12.199554 · doi-reference
10.1021/jm300687e
10.1021/jm300687e · doi-reference
Sequence-to-function deep learning frameworks for engineered riboregulators
10.1038/s41467-020-18676-2 · doi-reference
Modeling aspects of the language of life through transfer-learning protein sequences
10.1186/s12859-019-3220-8 · doi-reference
10.1093/bioinformatics/btaa433
10.1093/bioinformatics/btaa433 · doi-reference
BindSpace decodes transcription factor binding signals by large-scale sequence embedding
10.1038/s41592-019-0511-y · doi-reference
USMPep: universal sequence models for major histocompatibility complex binding affinity prediction
10.1186/s12859-020-03631-1 · doi-reference
UDSMProt: universal deep sequence models for protein classification
10.1093/bioinformatics/btaa003 · doi-reference
10.1038/s41592-019-0598-1
10.1038/s41592-019-0598-1 · doi-reference
10.1126/science.abd7331
10.1126/science.abd7331 · doi-reference
ProbeRating: a recommender system to infer binding profiles for nucleic acid-binding proteins
10.1093/bioinformatics/btaa580 · doi-reference
10.1016/j.ab.2019.02.017
10.1016/j.ab.2019.02.017 · doi-reference
10.1093/bioinformatics/btv256
10.1093/bioinformatics/btv256 · doi-reference
CircSLNN: Identifying RBP-Binding Sites on circRNAs via Sequence Labeling Neural Networks
10.3389/fgene.2019.01184 · doi-reference
10.1093/bioinformatics/btx234
10.1093/bioinformatics/btx234 · doi-reference
Prediction of enhancer-promoter interactions via natural language processing
10.1186/s12864-018-4459-6 · doi-reference
DNA sequences performs as natural language processing by exploiting deep learning algorithm for the identification of N4-methylcytosine
10.1038/s41598-020-80430-x · doi-reference
