Works matching IS 14712105 AND DT 2018 AND VI 19 AND IP 19
Results: 16
miRBaseConverter: an R/Bioconductor package for converting and retrieving miRNA name, accession, sequence and family information in different versions of miRBase.
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- BMC Bioinformatics, 2018, v. 19, n. 19, p. 179, doi. 10.1186/s12859-018-2531-5
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A latent allocation model for the analysis of microbial composition and disease.
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- BMC Bioinformatics, 2018, v. 19, n. 19, p. 171, doi. 10.1186/s12859-018-2530-6
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Computational prediction of plasma protein binding of cyclic peptides from small molecule experimental data using sparse modeling techniques.
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- BMC Bioinformatics, 2018, v. 19, n. 19, p. 157, doi. 10.1186/s12859-018-2529-z
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Classifying early and late mild cognitive impairment stages of Alzheimer's disease by fusing default mode networks extracted with multiple seeds.
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- BMC Bioinformatics, 2018, v. 19, n. 19, p. 147, doi. 10.1186/s12859-018-2528-0
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PDRLGB: precise DNA-binding residue prediction using a light gradient boosting machine.
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- BMC Bioinformatics, 2018, v. 19, n. 19, p. 135, doi. 10.1186/s12859-018-2527-1
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Constructing a database for the relations between CNV and human genetic diseases via systematic text mining.
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- BMC Bioinformatics, 2018, v. 19, n. 19, p. 125, doi. 10.1186/s12859-018-2526-2
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Predicting protein-protein interactions using high-quality non-interacting pairs.
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- BMC Bioinformatics, 2018, v. 19, n. 19, p. 105, doi. 10.1186/s12859-018-2525-3
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A benchmark study of sequence alignment methods for protein clustering.
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- BMC Bioinformatics, 2018, v. 19, n. 19, p. 95, doi. 10.1186/s12859-018-2524-4
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Convolutional neural network based on SMILES representation of compounds for detecting chemical motif.
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- BMC Bioinformatics, 2018, v. 19, n. 19, p. 83, doi. 10.1186/s12859-018-2523-5
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DWNN-RLS: regularized least squares method for predicting circRNA-disease associations.
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- BMC Bioinformatics, 2018, v. 19, n. 19, p. 73, doi. 10.1186/s12859-018-2522-6
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Estimating heterogeneous treatment effect by balancing heterogeneity and fitness.
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- BMC Bioinformatics, 2018, v. 19, n. 19, p. 61, doi. 10.1186/s12859-018-2521-7
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Predicting adverse drug reactions of combined medication from heterogeneous pharmacologic databases.
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- BMC Bioinformatics, 2018, v. 19, n. 19, p. 49, doi. 10.1186/s12859-018-2520-8
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Single-cell gene expression analysis reveals β-cell dysfunction and deficit mechanisms in type 2 diabetes.
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- BMC Bioinformatics, 2018, v. 19, n. 19, p. 37, doi. 10.1186/s12859-018-2519-1
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Identification of pre-microRNAs by characterizing their sequence order evolution information and secondary structure graphs.
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- BMC Bioinformatics, 2018, v. 19, n. 19, p. 25, doi. 10.1186/s12859-018-2518-2
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In silico design of MHC class I high binding affinity peptides through motifs activation map.
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- BMC Bioinformatics, 2018, v. 19, n. 19, p. 13, doi. 10.1186/s12859-018-2517-3
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- Article
DeepM6ASeq: prediction and characterization of m6A-containing sequences using deep learning.
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- BMC Bioinformatics, 2018, v. 19, n. 19, p. 1, doi. 10.1186/s12859-018-2516-4
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- Article