Works matching IS 14675463 AND DT 2021 AND VI 22 AND IP 6
Results: 209
Detection algorithms and attentive points of safety signal using spontaneous reporting systems as a clinical data source.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab347
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Molecular design in drug discovery: a comprehensive review of deep generative models.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab344
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Prediction of RBP binding sites on circRNAs using an LSTM-based deep sequence learning architecture.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab342
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Improved protein contact prediction using dimensional hybrid residual networks and singularity enhanced loss function.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab341
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Recent omics-based computational methods for COVID-19 drug discovery and repurposing.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab339
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integrated computational pipeline for designing high-affinity nanobodies with expanded genetic codes.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab338
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Comparison of approaches to transcriptomic analysis in multi-sampled tumors.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab337
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DNAgenie: accurate prediction of DNA-type-specific binding residues in protein sequences.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab336
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DLpTCR: an ensemble deep learning framework for predicting immunogenic peptide recognized by T cell receptor.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab335
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De novo generation of dual-target ligands using adversarial training and reinforcement learning.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab333
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Matrix factorization-based data fusion for the prediction of RNA-binding proteins and alternative splicing event associations during epithelial–mesenchymal transition.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab332
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High-dimensional generalized propensity score with application to omics data.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab331
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Functional meta-omics provide critical insights into long- and short-read assemblies.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab330
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approach to gene-based testing accounting for dependence of tests among nearby genes.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab329
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Predicting potential small molecule–miRNA associations based on bounded nuclear norm regularization.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab328
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Learning to SMILES: BAN-based strategies to improve latent representation learning from molecules.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab327
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RefRGim: an intelligent reference panel reconstruction method for genotype imputation with convolutional neural networks.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab326
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DeepDRIM: a deep neural network to reconstruct cell-type-specific gene regulatory network using single-cell RNA-seq data.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab325
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Reviewing and assessing existing meta-analysis models and tools.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab324
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Detecting methylation quantitative trait loci using a methylation random field method.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab323
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Mendelian randomization under the omnigenic architecture.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab322
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Application of artificial intelligence and machine learning for COVID-19 drug discovery and vaccine design.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab320
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Drug repositioning based on the heterogeneous information fusion graph convolutional network.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab319
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FROGS: a powerful tool to analyse the diversity of fungi with special management of internal transcribed spacers.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab318
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Mol2Context-vec: learning molecular representation from context awareness for drug discovery.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab317
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Single-cell Hi-C data analysis: safety in numbers.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab316
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XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab315
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systematic comparison of data- and knowledge-driven approaches to disease subtype discovery.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab314
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genomic data resource for predicting antimicrobial resistance from laboratory-derived antimicrobial susceptibility phenotypes.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab313
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gentle introduction to understanding preclinical data for cancer pharmaco-omic modeling.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab312
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Cell fate conversion prediction by group sparse optimization method utilizing single-cell and bulk OMICs data.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab311
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NeuroPpred-Fuse: an interpretable stacking model for prediction of neuropeptides by fusing sequence information and feature selection methods.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab310
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APPTEST is a novel protocol for the automatic prediction of peptide tertiary structures.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab308
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Data-driven identification of SARS-CoV-2 subpopulations using PhenoGraph and binary-coded genomic data.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab307
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Genome-wide hierarchical mixed model association analysis.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab306
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Identifying complex gene–gene interactions: a mixed kernel omnibus testing approach.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab305
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Supervised application of internal validation measures to benchmark dimensionality reduction methods in scRNA-seq data.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab304
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comparative benchmark of classic DNA motif discovery tools on synthetic data.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab303
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Identification of miRNA–disease associations via multiple information integration with Bayesian ranking.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab302
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novel framework integrating AI model and enzymological experiments promotes identification of SARS-CoV-2 3CL protease inhibitors and activity-based probe.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab301
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Local ancestry inference in heterogeneous populations—Are recent recombination events more relevant?
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab300
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end-to-end heterogeneous graph attention network for Mycobacterium tuberculosis drug-resistance prediction.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab299
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Corrigendum to: Computational design of ultrashort peptide inhibitors of the receptor-binding domain of the SARS-CoV-2 S protein.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab298
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DeepFeature: feature selection in nonimage data using convolutional neural network.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab297
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Distance-guided protein folding based on generalized descent direction.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab296
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Selecting gene features for unsupervised analysis of single-cell gene expression data.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab295
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Drug sensitivity prediction from cell line-based pharmacogenomics data: guidelines for developing machine learning models.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab294
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Machine learning approaches for drug combination therapies.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab293
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Tracing the evolution of aneuploid cancers by multiregional sequencing with CRUST.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab292
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Comparative analysis of molecular fingerprints in prediction of drug combination effects.
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- Briefings in Bioinformatics, 2021, v. 22, n. 6, p. 1, doi. 10.1093/bib/bbab291
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