AIMC Topic: Deep Learning

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Deep learning of gene relationships from single cell time-course expression data.

Briefings in bioinformatics Sep 2, 2021
Time-course gene-expression data have been widely used to infer regulatory and signaling relationships between genes. Most of the widely used methods for such analysis were developed for bulk expression data. Single cell RNA-Seq (scRNA-Seq) data offe...
Single-Cell Analysis RNA-Seq Computational Biology Models, Genetic Gene Regulatory Networks Epistasis, Genetic Mice Animals Gene Expression Profiling Deep Learning Time Factors Humans Algorithms Cells, Cultured
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SAResNet: self-attention residual network for predicting DNA-protein binding.

Briefings in bioinformatics Sep 2, 2021
Knowledge of the specificity of DNA-protein binding is crucial for understanding the mechanisms of gene expression, regulation and gene therapy. In recent years, deep-learning-based methods for predicting DNA-protein binding from sequence data have a...
Humans Protein Binding DNA-Binding Proteins Computational Biology Neural Networks, Computer DNA Binding Sites Algorithms Internet Reproducibility of Results Deep Learning
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Explainability in transformer models for functional genomics.

Briefings in bioinformatics Sep 2, 2021
The effectiveness of deep learning methods can be largely attributed to the automated extraction of relevant features from raw data. In the field of functional genomics, this generally concerns the automatic selection of relevant nucleotide motifs fr...
DNA, Bacterial Transcription Initiation Site Promoter Regions, Genetic Transcription Factors Binding Sites Escherichia coli Genome, Bacterial Base Sequence Deep Learning Genomics
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DeepDTAF: a deep learning method to predict protein-ligand binding affinity.

Briefings in bioinformatics Sep 2, 2021
Biomolecular recognition between ligand and protein plays an essential role in drug discovery and development. However, it is extremely time and resource consuming to determine the protein-ligand binding affinity by experiments. At present, many comp...
Deep Learning Protein Binding Proteins Models, Molecular Amino Acid Sequence Ligands Data Accuracy Hydrogen Bonding Software Reproducibility of Results Drug Discovery Binding Sites Protein Conformation, alpha-Helical
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DeepDRK: a deep learning framework for drug repurposing through kernel-based multi-omics integration.

Briefings in bioinformatics Sep 2, 2021
Recent pharmacogenomic studies that generate sequencing data coupled with pharmacological characteristics for patient-derived cancer cell lines led to large amounts of multi-omics data for precision cancer medicine. Among various obstacles hindering ...
Transcriptome Deep Learning Pharmacogenetics Cell Line, Tumor Computational Biology Precision Medicine Drug Repositioning Prognosis Datasets as Topic Neoplasms Humans Antineoplastic Agents Software
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NPI-GNN: Predicting ncRNA-protein interactions with deep graph neural networks.

Briefings in bioinformatics Sep 2, 2021
Noncoding RNAs (ncRNAs) play crucial roles in many biological processes. Experimental methods for identifying ncRNA-protein interactions (NPIs) are always costly and time-consuming. Many computational approaches have been developed as alternative way...
Datasets as Topic RNA, Untranslated Protein Binding Proteins Internet Benchmarking Software Sensitivity and Specificity Deep Learning Humans
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A spatial-temporal gated attention module for molecular property prediction based on molecular geometry.

Briefings in bioinformatics Sep 2, 2021
MOTIVATION: Geometry-based properties and characteristics of drug molecules play an important role in drug development for virtual screening in computational chemistry. The 3D characteristics of molecules largely determine the properties of the drug ...
Models, Molecular Molecular Structure Pharmaceutical Preparations Ligands Deep Learning Protein Binding Computational Biology Proteins Molecular Conformation Humans Neural Networks, Computer Software Algorithms Reproducibility of Results Drug Discovery
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Deep-ABPpred: identifying antibacterial peptides in protein sequences using bidirectional LSTM with word2vec.

Briefings in bioinformatics Sep 2, 2021
The overuse of antibiotics has led to emergence of antimicrobial resistance, and as a result, antibacterial peptides (ABPs) are receiving significant attention as an alternative. Identification of effective ABPs in lab from natural sources is a cost-...
Deep Learning Computational Biology Amino Acid Sequence Gram-Negative Bacteria Anti-Bacterial Agents Drug Resistance, Bacterial Peptides Gram-Positive Bacteria Viral Tail Proteins Streptococcus Phages
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Machine learning on ligand-residue interaction profiles to significantly improve binding affinity prediction.

Briefings in bioinformatics Sep 2, 2021
Structure-based virtual screenings (SBVSs) play an important role in drug discovery projects. However, it is still a challenge to accurately predict the binding affinity of an arbitrary molecule binds to a drug target and prioritize top ligands from ...
Protein Binding Algorithms Drug Evaluation, Preclinical Databases, Protein Molecular Docking Simulation Protein Kinases Humans Receptors, G-Protein-Coupled Drug Discovery Ligands Molecular Structure Deep Learning Crystallization
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iDHS-Deep: an integrated tool for predicting DNase I hypersensitive sites by deep neural network.

Briefings in bioinformatics Sep 2, 2021
DNase I hypersensitive site (DHS) refers to the hypersensitive region of chromatin for the DNase I enzyme. It is an important part of the noncoding region and contains a variety of regulatory elements, such as promoter, enhancer, and transcription fa...
Transcription Factors DNA Internet Oryza Humans Software Deep Learning Protein Binding Enhancer Elements, Genetic Arabidopsis Chromatin Deoxyribonuclease I Genetic Loci Datasets as Topic Promoter Regions, Genetic Transcription, Genetic
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