Research graph
References from Computational and artificial intelligence methodologies for big data analytics in structural biology. Local targets link to admitted publications; unresolved targets remain external evidence.
AI-Driven Heart Disease Prediction Using Machine Learning And Deep Learning Techniques
2025 · External reference
Federated learning in smart healthcare: a comprehensive review on privacy, security, and predictive analytics with IoT integration
10.3390/healthcare12242587 · 2024 · External reference
Unresolved reference
2024 · External reference
Breast cancer; discovery of novel diagnostic biomarkers, drug resistance, and therapeutic implications
10.3389/fmolb.2022.783450 · 2022 · External reference
Exploring and learning the universe of protein allostery using artificial intelligence augmented biophysical and computational approaches
10.1021/acs.jcim.2c01634 · 2023 · External reference
Multimodal deep learning approaches for single-cell multi-omics data integration
10.1093/bib/bbad313 · 2023 · External reference
Incorporating machine learning into established bioinformatics frameworks
10.3390/ijms22062903 · 2021 · External reference
Before and after AlphaFold2: an overview of protein structure prediction
10.3389/fbinf.2023.1120370 · 2023 · External reference
Saliency-driven explainable deep learning in medical imaging: bridging visual explainability and statistical quantitative analysis
10.1186/s13040-024-00370-4 · 2024 · External reference
Tracing protein and proteome history with chronologies and networks: folding recapitulates evolution
10.1080/14789450.2021.1992277 · 2021 · External reference
Structural dynamics in the evolution of SARS-CoV-2 spike glycoprotein
10.1038/s41467-023-36745-0 · 2023 · External reference
Deep learning and virtual drug screening
10.4155/fmc-2018-0314 · 2018 · External reference
Integrative approaches in structural biology: a more complete picture from the combination of individual techniques
10.3390/biom9080370 · 2019 · External reference
Understanding epistatic networks in the B1 β-lactamases through coevolutionary statistical modeling and deep mutational scanning
2024 · External reference
Validating large-scale quantum machine learning: efficient simulation of quantum support vector machines using tensor networks
2025 · External reference
AI-driven deep learning techniques in protein structure prediction
10.3390/ijms25158426 · 2024 · External reference
A novel rational PROTACs design and validation via AI-driven drug design approach
10.1021/acsomega.3c10183 · 2024 · External reference
StructGNN: An efficient graph neural network framework for static structural analysis
10.1016/j.compstruc.2024.107385 · 2024 · External reference
Artificial intelligence in cryo-electron microscopy
2022 · External reference
Extracting phylogenetic dimensions of coevolution reveals hidden functional signals
10.1038/s41598-021-04260-1 · 2022 · External reference
Resolving tissue complexity by multimodal spatial omics modeling with MISO
10.1038/s41592-024-02574-2 · 2025 · External reference
Functional dynamics of G protein-coupled receptors reveal new routes for drug discovery
10.1038/s41573-024-01083-3 · 2025 · External reference
Protein–DNA/RNA interactions: Machine intelligence tools and approaches in the era of artificial intelligence and big data
10.1002/pmic.202100197 · 2022 · External reference
10.20944/preprints202404.0708.v1
10.20944/preprints202404.0708.v1 · External reference
Synthesizing systems biology knowledge from omics using genome-scale models
10.1002/pmic.201900282 · 2020 · External reference
In silico strategies to support fragment-to-lead optimization in drug discovery
10.3389/fchem.2020.00093 · 2020 · External reference
Applications of deep-learning in exploiting large-scale and heterogeneous compound data in industrial pharmaceutical research
10.3389/fphar.2019.01303 · 2019 · External reference
Artificial intelligence in cryo-EM protein particle picking: recent advances and remaining challenges
10.1093/bib/bbaf011 · 2025 · External reference
Are deep learning structural models sufficiently accurate for virtual screening? Application of docking algorithms to AlphaFold2 predicted structures
10.1021/acs.jcim.2c01270 · 2023 · External reference
Novel artificial intelligence-based approaches for Ab initio structure determination and atomic model building for cryo-electron microscopy
10.3390/mi14091674 · 2023 · External reference
AI and precision oncology in clinical cancer genomics: from prevention to targeted cancer therapies-an outcomes based patient care
10.1016/j.imu.2022.100965 · 2022 · External reference
The long and the short of it: Unlocking nanopore long-read RNA sequencing data with short-read differential expression analysis tools
10.1093/nargab/lqab028 · 2021 · External reference
Editorial: artificial intelligence and bioinformatics applications for omics and multi-omics studies
10.3389/fgene.2024.1371473 · 2024 · External reference
Integrative multi-omics approaches for identifying and characterizing biological elements in crop traits: current progress and future prospects
10.3390/ijms26041466 · 2025 · External reference
Enhancing agricultural operations: big data analytics using distributed and parallel computing
10.57041/v3jj9f69 · 2023 · External reference
Molecular insights from conformational ensembles via machine learning
10.1016/j.bpj.2019.12.016 · 2020 · External reference
AI-assisted warfarin dose optimisation with CURATE.AI for clinical impact: Retrospective data analysis
10.1002/btm2.10757 · 2025 · External reference
10.1101/2024.12.03.626671
10.1101/2024.12.03.626671 · External reference
High-throughput virtual laboratory for drug discovery using massive datasets
10.1177/10943420211001565 · 2021 · External reference
The cellular landscape by cryo soft X-ray tomography
10.1007/s12551-019-00567-6 · 2019 · External reference
Privacy-preserving federated learning for collaborative medical data mining in multi-institutional settings
10.1038/s41598-025-97565-4 · 2025 · External reference
Interpreting biologically informed neural networks for enhanced proteomic biomarker discovery and pathway analysis
10.1038/s41467-023-41146-4 · 2023 · External reference
Innovations in genomics and big data analytics for personalized medicine and health care: a review
10.3390/ijms23094645 · 2022 · External reference
Deep learning-based segmentation of cryo-electron tomograms
10.3791/64435 · 2022 · External reference
Exploring protein conformational changes using a large-scale biophysical sampling augmented deep learning strategy
10.1002/advs.202400884 · 2024 · External reference
Automated design of multi-target ligands by generative deep learning
10.1038/s41467-024-52060-8 · 2024 · External reference
An accurate and efficient approach to knowledge extraction from scientific publications using structured ontology models, graph neural networks, and large language models
10.3390/ijms252111811 · 2024 · External reference
How successful are AI-discovered drugs in clinical trials? A first analysis and emerging lessons
10.1016/j.drudis.2024.104009 · 2024 · External reference
Struct2GO: protein function prediction based on graph pooling algorithm and AlphaFold2 structure information
2023 · External reference
Artificial intelligence in drug discovery and development: transforming challenges into opportunities
10.1007/s44395-025-00007-3 · 2025 · External reference
Integrative multi-omics and artificial intelligence: a new paradigm for systems biology
10.1177/15578100251392371 · 2025 · External reference
Applications of and issues with machine learning in medicine: bridging the gap with explainable AI
10.5582/bst.2024.01342 · 2024 · External reference
Explainable AI for bioinformatics: methods, tools and applications
10.1093/bib/bbad236 · 2023 · External reference
Structure-based approaches for protein–protein interaction prediction using machine learning and deep learning
10.3390/biom15010141 · 2025 · External reference
Simultaneous dimensionality reduction and integration for single-cell ATAC-seq data using deep learning
10.1038/s42256-022-00443-1 · 2022 · External reference
AlphaFold3: an overview of applications and performance insights
10.3390/ijms26083671 · 2025 · External reference
Deep learning in structural bioinformatics: current applications and future perspectives
10.1093/bib/bbae042 · 2024 · External reference
Hybrid MPI and CUDA parallelization for CFD applications on multi-GPU HPC clusters
10.1155/2020/8862123 · 2020 · External reference
The next revolution in computational simulations: harnessing AI and quantum computing in molecular dynamics
10.1016/j.sbi.2024.102919 · 2024 · External reference
Blind assessment of monomeric AlphaFold2 protein structure models with experimental NMR data
10.1016/j.jmr.2023.107481 · 2023 · External reference
10.1145/3447548.3467311
10.1145/3447548.3467311 · External reference
A hybrid quantum computing pipeline for real world drug discovery
2024 · External reference
Technical and biological biases in bulk transcriptomic data mining for cancer research
10.7150/jca.100922 · 2025 · External reference
Improved model quality assessment using sequence and structural information by enhanced deep neural networks
10.1093/bib/bbac507 · 2023 · External reference
A targeted multi-omic analysis approach measures protein expression and low-abundance transcripts on the single-cell level
10.1016/j.celrep.2020.03.063 · 2020 · External reference
Computational analysis of phosphoproteomics data in multi-omics cancer studies
10.1002/pmic.201900312 · 2021 · External reference
Generative AI in AI-based digital twins for fault diagnosis for predictive maintenance in industry 4.0/5.0
10.3390/app15063166 · 2025 · External reference
Challenges and limitations of biological network analysis
10.3390/biotech11030024 · 2022 · External reference
Fighting COVID-19 with artificial intelligence
10.1007/978-1-0716-1787-8_3 · 2022 · External reference
GoToCloud optimization of cloud computing environment for accelerating cryo-EM structure-based drug design
10.1038/s42003-024-07031-6 · 2024 · External reference
From data to cure: a comprehensive exploration of multi-omics data analysis for targeted therapies
10.1007/s12033-024-01133-6 · 2025 · External reference
Beyond homology transfer: deep learning for automated annotation of proteins
10.1007/s10723-018-9450-6 · 2019 · External reference
Structural modeling of ion channels using AlphaFold2, RoseTTAFold2, and ESMFold
10.1080/19336950.2024.2325032 · 2024 · External reference
AlphaFold, artificial intelligence (AI), and allostery
10.1021/acs.jpcb.2c04346 · 2022 · External reference
Integrating artificial intelligence in drug discovery and early drug development: a transformative approach
10.1186/s40364-025-00758-2 · 2025 · External reference
Integrative multi-omics and systems bioinformatics in translational neuroscience: a data mining perspective
10.1016/j.jpha.2023.06.011 · 2023 · External reference
Topological and statistical analyses of gene regulatory networks reveal unifying yet quantitatively different emergent properties
10.1371/journal.pcbi.1006098 · 2018 · External reference
Quantum computing in the next-generation computational biology landscape: from protein folding to molecular dynamics
10.1007/s12033-023-00765-4 · 2024 · External reference
Integrative analysis of next-generation sequencing for next-generation cancer research toward artificial intelligence
10.3390/cancers13133148 · 2021 · External reference
Single-cell multi-omics and its prospective application in cancer biology
10.1002/pmic.201900271 · 2020 · External reference
Identification of target associations for polypharmacology from analysis of crystallographic ligands of the protein data Bank
10.1021/acs.jcim.9b00821 · 2020 · External reference
PROTACs: current and future potential as a precision medicine strategy to combat cancer
10.1158/1535-7163.mct-23-0747 · 2024 · External reference
Integrating molecular perspectives: strategies for comprehensive multi-omics integrative data analysis and machine learning applications in transcriptomics, proteomics, and metabolomics
10.3390/biology13110848 · 2024 · External reference
Unlocking machine learning model decisions: a comparative analysis of LIME and SHAP for enhanced interpretability
10.52783/jes.1480 · 2024 · External reference
Hybrid methods for combined experimental and computational determination of protein structure
10.1063/5.0026025 · 2020 · External reference
RCSB protein data bank: visualizing groups of experimentally determined PDB structures alongside computed structure models of proteins
10.3389/fbinf.2023.1311287 · 2023 · External reference
Artificial intelligence and big data for pharmacovigilance and patient safety
10.1016/j.glmedi.2024.100139 · 2024 · External reference
The application of artificial intelligence to the bayesian model algorithm for combining genome data
10.54097/ykhccb53 · 2023 · External reference
Mechanisms and technologies in cancer epigenetics
10.3389/fonc.2024.1513654 · 2025 · External reference
Machine learning for collective variable discovery and enhanced sampling in biomolecular simulation
10.1080/00268976.2020.1737742 · 2020 · External reference
Utilizing molecular dynamics simulations, machine learning, cryo-EM, and NMR spectroscopy to predict and validate protein dynamics
10.3390/ijms25179725 · 2024 · External reference
Accounting for modeling errors and inherent structural variability through a hierarchical bayesian model updating approach: an overview
10.3390/s20143874 · 2020 · External reference
Accurate prediction of protein structural flexibility by deep learning integrating intricate atomic structures and cryo-EM density information
2024 · External reference
Modelling protein complexes with crosslinking mass spectrometry and deep learning
10.1038/s41467-024-51771-2 · 2024 · External reference
Emerging applications of artificial intelligence in pathogen genomics
10.3389/fbrio.2024.1326958 · 2024 · External reference
AI's role in revolutionizing personalized medicine by reshaping pharmacogenomics and drug therapy
10.1016/j.ipha.2024.08.005 · 2024 · External reference
Simple, efficient, and scalable structure-aware adapter boosts protein language models
10.1021/acs.jcim.4c00689 · 2024 · External reference
IoT, and AI Integration
2025 · External reference
Optimizing machine learning models for predictive analytics in cloud environments
10.36676/jrps.v13.i5.1530 · 2024 · External reference
AlphaFold-latest: revolutionizing protein structure prediction for comprehensive biomolecular insights and therapeutic advancements
2024 · External reference
Graph machine learning for integrated multi-omics analysis
10.1038/s41416-024-02706-7 · 2024 · External reference
Protein design using structure-prediction networks: AlphaFold and RoseTTAFold as protein structure foundation models
10.1101/cshperspect.a041472 · 2024 · External reference
Recent advances from computer-aided drug design to artificial intelligence drug design
10.1039/d4md00522h · 2024 · External reference
Protein-protein interaction networks as miners of biological discovery
10.1002/pmic.202100190 · 2022 · External reference
Comprehensive encoding of conformational and compositional protein structural ensembles through the mmCIF data structure
10.1107/s2052252524005098 · 2024 · External reference
Coevolution-based prediction of key allosteric residues for protein function regulation
10.7554/elife.81850 · 2023 · External reference
Epidemiological insights into the omicron outbreak via MeltArray-assisted real-time tracking of SARS-CoV-2 variants
10.3390/v15122397 · 2023 · External reference
Transfer learning via multi-scale convolutional neural layers for human–virus protein–protein interaction prediction
2021 · External reference
Deep learning in multimodal fusion for sustainable plant care: a comprehensive review
10.3390/su17125255 · 2025 · External reference
10.20944/preprints202410.1641.v1
10.20944/preprints202410.1641.v1 · External reference
A comprehensive review of the recent advances on predicting drug-target affinity based on deep learning
10.3389/fphar.2024.1375522 · 2024 · External reference
Optimizing clinical workflow using precision medicine and advanced data analytics
10.3390/pr11030939 · 2023 · External reference
Graph masked self-distillation learning for prediction of mutation impact on protein–protein interactions
10.1038/s42003-024-07066-9 · 2024 · External reference
Exploring protein conformational changes using a large-scale biophysical sampling augmented deep learning strategy
10.1002/advs.202400884 · ExternalCitation · doi-reference
AI-assisted warfarin dose optimisation with CURATE.AI for clinical impact: Retrospective data analysis
10.1002/btm2.10757 · ExternalCitation · doi-reference
Single-cell multi-omics and its prospective application in cancer biology
10.1002/pmic.201900271 · ExternalCitation · doi-reference
Synthesizing systems biology knowledge from omics using genome-scale models
10.1002/pmic.201900282 · ExternalCitation · doi-reference
Computational analysis of phosphoproteomics data in multi-omics cancer studies
10.1002/pmic.201900312 · ExternalCitation · doi-reference
Protein-protein interaction networks as miners of biological discovery
10.1002/pmic.202100190 · ExternalCitation · doi-reference
Protein–DNA/RNA interactions: Machine intelligence tools and approaches in the era of artificial intelligence and big data
10.1002/pmic.202100197 · ExternalCitation · doi-reference
Fighting COVID-19 with artificial intelligence
10.1007/978-1-0716-1787-8_3 · ExternalCitation · doi-reference
Beyond homology transfer: deep learning for automated annotation of proteins
10.1007/s10723-018-9450-6 · ExternalCitation · doi-reference
Quantum computing in the next-generation computational biology landscape: from protein folding to molecular dynamics
10.1007/s12033-023-00765-4 · ExternalCitation · doi-reference
From data to cure: a comprehensive exploration of multi-omics data analysis for targeted therapies
10.1007/s12033-024-01133-6 · ExternalCitation · doi-reference
The cellular landscape by cryo soft X-ray tomography
10.1007/s12551-019-00567-6 · ExternalCitation · doi-reference
Artificial intelligence in drug discovery and development: transforming challenges into opportunities
10.1007/s44395-025-00007-3 · ExternalCitation · doi-reference
Molecular insights from conformational ensembles via machine learning
10.1016/j.bpj.2019.12.016 · ExternalCitation · doi-reference
A targeted multi-omic analysis approach measures protein expression and low-abundance transcripts on the single-cell level
10.1016/j.celrep.2020.03.063 · ExternalCitation · doi-reference
StructGNN: An efficient graph neural network framework for static structural analysis
10.1016/j.compstruc.2024.107385 · ExternalCitation · doi-reference
How successful are AI-discovered drugs in clinical trials? A first analysis and emerging lessons
10.1016/j.drudis.2024.104009 · ExternalCitation · doi-reference
Artificial intelligence and big data for pharmacovigilance and patient safety
10.1016/j.glmedi.2024.100139 · ExternalCitation · doi-reference
AI and precision oncology in clinical cancer genomics: from prevention to targeted cancer therapies-an outcomes based patient care
10.1016/j.imu.2022.100965 · ExternalCitation · doi-reference
AI's role in revolutionizing personalized medicine by reshaping pharmacogenomics and drug therapy
10.1016/j.ipha.2024.08.005 · ExternalCitation · doi-reference
Blind assessment of monomeric AlphaFold2 protein structure models with experimental NMR data
10.1016/j.jmr.2023.107481 · ExternalCitation · doi-reference
Integrative multi-omics and systems bioinformatics in translational neuroscience: a data mining perspective
10.1016/j.jpha.2023.06.011 · ExternalCitation · doi-reference
The next revolution in computational simulations: harnessing AI and quantum computing in molecular dynamics
10.1016/j.sbi.2024.102919 · ExternalCitation · doi-reference
Are deep learning structural models sufficiently accurate for virtual screening? Application of docking algorithms to AlphaFold2 predicted structures
10.1021/acs.jcim.2c01270 · ExternalCitation · doi-reference
Exploring and learning the universe of protein allostery using artificial intelligence augmented biophysical and computational approaches
10.1021/acs.jcim.2c01634 · ExternalCitation · doi-reference
Simple, efficient, and scalable structure-aware adapter boosts protein language models
10.1021/acs.jcim.4c00689 · ExternalCitation · doi-reference
Identification of target associations for polypharmacology from analysis of crystallographic ligands of the protein data Bank
10.1021/acs.jcim.9b00821 · ExternalCitation · doi-reference
AlphaFold, artificial intelligence (AI), and allostery
10.1021/acs.jpcb.2c04346 · ExternalCitation · doi-reference
A novel rational PROTACs design and validation via AI-driven drug design approach
10.1021/acsomega.3c10183 · ExternalCitation · doi-reference
Graph machine learning for integrated multi-omics analysis
10.1038/s41416-024-02706-7 · ExternalCitation · doi-reference
Structural dynamics in the evolution of SARS-CoV-2 spike glycoprotein
10.1038/s41467-023-36745-0 · ExternalCitation · doi-reference
Interpreting biologically informed neural networks for enhanced proteomic biomarker discovery and pathway analysis
10.1038/s41467-023-41146-4 · ExternalCitation · doi-reference
Modelling protein complexes with crosslinking mass spectrometry and deep learning
10.1038/s41467-024-51771-2 · ExternalCitation · doi-reference
Automated design of multi-target ligands by generative deep learning
10.1038/s41467-024-52060-8 · ExternalCitation · doi-reference
Functional dynamics of G protein-coupled receptors reveal new routes for drug discovery
10.1038/s41573-024-01083-3 · ExternalCitation · doi-reference
Resolving tissue complexity by multimodal spatial omics modeling with MISO
10.1038/s41592-024-02574-2 · ExternalCitation · doi-reference
Extracting phylogenetic dimensions of coevolution reveals hidden functional signals
10.1038/s41598-021-04260-1 · ExternalCitation · doi-reference
Privacy-preserving federated learning for collaborative medical data mining in multi-institutional settings
10.1038/s41598-025-97565-4 · ExternalCitation · doi-reference
GoToCloud optimization of cloud computing environment for accelerating cryo-EM structure-based drug design
10.1038/s42003-024-07031-6 · ExternalCitation · doi-reference
Graph masked self-distillation learning for prediction of mutation impact on protein–protein interactions
10.1038/s42003-024-07066-9 · ExternalCitation · doi-reference
Simultaneous dimensionality reduction and integration for single-cell ATAC-seq data using deep learning
10.1038/s42256-022-00443-1 · ExternalCitation · doi-reference
Recent advances from computer-aided drug design to artificial intelligence drug design
10.1039/d4md00522h · ExternalCitation · doi-reference
Hybrid methods for combined experimental and computational determination of protein structure
10.1063/5.0026025 · ExternalCitation · doi-reference
Machine learning for collective variable discovery and enhanced sampling in biomolecular simulation
10.1080/00268976.2020.1737742 · ExternalCitation · doi-reference
Tracing protein and proteome history with chronologies and networks: folding recapitulates evolution
10.1080/14789450.2021.1992277 · ExternalCitation · doi-reference
Structural modeling of ion channels using AlphaFold2, RoseTTAFold2, and ESMFold
10.1080/19336950.2024.2325032 · ExternalCitation · doi-reference
Improved model quality assessment using sequence and structural information by enhanced deep neural networks
10.1093/bib/bbac507 · ExternalCitation · doi-reference
Explainable AI for bioinformatics: methods, tools and applications
10.1093/bib/bbad236 · ExternalCitation · doi-reference
Multimodal deep learning approaches for single-cell multi-omics data integration
10.1093/bib/bbad313 · ExternalCitation · doi-reference
Deep learning in structural bioinformatics: current applications and future perspectives
10.1093/bib/bbae042 · ExternalCitation · doi-reference
Artificial intelligence in cryo-EM protein particle picking: recent advances and remaining challenges
10.1093/bib/bbaf011 · ExternalCitation · doi-reference
The long and the short of it: Unlocking nanopore long-read RNA sequencing data with short-read differential expression analysis tools
10.1093/nargab/lqab028 · ExternalCitation · doi-reference
10.1101/2024.12.03.626671
10.1101/2024.12.03.626671 · ExternalCitation · doi-reference
Protein design using structure-prediction networks: AlphaFold and RoseTTAFold as protein structure foundation models
10.1101/cshperspect.a041472 · ExternalCitation · doi-reference
Comprehensive encoding of conformational and compositional protein structural ensembles through the mmCIF data structure
10.1107/s2052252524005098 · ExternalCitation · doi-reference
10.1145/3447548.3467311
10.1145/3447548.3467311 · ExternalCitation · doi-reference
Hybrid MPI and CUDA parallelization for CFD applications on multi-GPU HPC clusters
10.1155/2020/8862123 · ExternalCitation · doi-reference
PROTACs: current and future potential as a precision medicine strategy to combat cancer
10.1158/1535-7163.mct-23-0747 · ExternalCitation · doi-reference
High-throughput virtual laboratory for drug discovery using massive datasets
10.1177/10943420211001565 · ExternalCitation · doi-reference
Integrative multi-omics and artificial intelligence: a new paradigm for systems biology
10.1177/15578100251392371 · ExternalCitation · doi-reference
Saliency-driven explainable deep learning in medical imaging: bridging visual explainability and statistical quantitative analysis
10.1186/s13040-024-00370-4 · ExternalCitation · doi-reference
Integrating artificial intelligence in drug discovery and early drug development: a transformative approach
10.1186/s40364-025-00758-2 · ExternalCitation · doi-reference
Topological and statistical analyses of gene regulatory networks reveal unifying yet quantitatively different emergent properties
10.1371/journal.pcbi.1006098 · ExternalCitation · doi-reference
10.20944/preprints202404.0708.v1
10.20944/preprints202404.0708.v1 · ExternalCitation · doi-reference
10.20944/preprints202410.1641.v1
10.20944/preprints202410.1641.v1 · ExternalCitation · doi-reference
Before and after AlphaFold2: an overview of protein structure prediction
10.3389/fbinf.2023.1120370 · ExternalCitation · doi-reference
RCSB protein data bank: visualizing groups of experimentally determined PDB structures alongside computed structure models of proteins
10.3389/fbinf.2023.1311287 · ExternalCitation · doi-reference
Emerging applications of artificial intelligence in pathogen genomics
10.3389/fbrio.2024.1326958 · ExternalCitation · doi-reference
In silico strategies to support fragment-to-lead optimization in drug discovery
10.3389/fchem.2020.00093 · ExternalCitation · doi-reference
Editorial: artificial intelligence and bioinformatics applications for omics and multi-omics studies
10.3389/fgene.2024.1371473 · ExternalCitation · doi-reference
Breast cancer; discovery of novel diagnostic biomarkers, drug resistance, and therapeutic implications
10.3389/fmolb.2022.783450 · ExternalCitation · doi-reference
Mechanisms and technologies in cancer epigenetics
10.3389/fonc.2024.1513654 · ExternalCitation · doi-reference
Applications of deep-learning in exploiting large-scale and heterogeneous compound data in industrial pharmaceutical research
10.3389/fphar.2019.01303 · ExternalCitation · doi-reference
A comprehensive review of the recent advances on predicting drug-target affinity based on deep learning
10.3389/fphar.2024.1375522 · ExternalCitation · doi-reference
Generative AI in AI-based digital twins for fault diagnosis for predictive maintenance in industry 4.0/5.0
10.3390/app15063166 · ExternalCitation · doi-reference
Integrating molecular perspectives: strategies for comprehensive multi-omics integrative data analysis and machine learning applications in transcriptomics, proteomics, and metabolomics
10.3390/biology13110848 · ExternalCitation · doi-reference
Structure-based approaches for protein–protein interaction prediction using machine learning and deep learning
10.3390/biom15010141 · ExternalCitation · doi-reference
Integrative approaches in structural biology: a more complete picture from the combination of individual techniques
10.3390/biom9080370 · ExternalCitation · doi-reference
Challenges and limitations of biological network analysis
10.3390/biotech11030024 · ExternalCitation · doi-reference
Integrative analysis of next-generation sequencing for next-generation cancer research toward artificial intelligence
10.3390/cancers13133148 · ExternalCitation · doi-reference
Federated learning in smart healthcare: a comprehensive review on privacy, security, and predictive analytics with IoT integration
10.3390/healthcare12242587 · ExternalCitation · doi-reference
Incorporating machine learning into established bioinformatics frameworks
10.3390/ijms22062903 · ExternalCitation · doi-reference
Innovations in genomics and big data analytics for personalized medicine and health care: a review
10.3390/ijms23094645 · ExternalCitation · doi-reference
AI-driven deep learning techniques in protein structure prediction
10.3390/ijms25158426 · ExternalCitation · doi-reference
Utilizing molecular dynamics simulations, machine learning, cryo-EM, and NMR spectroscopy to predict and validate protein dynamics
10.3390/ijms25179725 · ExternalCitation · doi-reference
An accurate and efficient approach to knowledge extraction from scientific publications using structured ontology models, graph neural networks, and large language models
10.3390/ijms252111811 · ExternalCitation · doi-reference
Integrative multi-omics approaches for identifying and characterizing biological elements in crop traits: current progress and future prospects
10.3390/ijms26041466 · ExternalCitation · doi-reference
AlphaFold3: an overview of applications and performance insights
10.3390/ijms26083671 · ExternalCitation · doi-reference
Novel artificial intelligence-based approaches for Ab initio structure determination and atomic model building for cryo-electron microscopy
10.3390/mi14091674 · ExternalCitation · doi-reference
Optimizing clinical workflow using precision medicine and advanced data analytics
10.3390/pr11030939 · ExternalCitation · doi-reference
Accounting for modeling errors and inherent structural variability through a hierarchical bayesian model updating approach: an overview
10.3390/s20143874 · ExternalCitation · doi-reference
Deep learning in multimodal fusion for sustainable plant care: a comprehensive review
10.3390/su17125255 · ExternalCitation · doi-reference
Epidemiological insights into the omicron outbreak via MeltArray-assisted real-time tracking of SARS-CoV-2 variants
10.3390/v15122397 · ExternalCitation · doi-reference
Optimizing machine learning models for predictive analytics in cloud environments
10.36676/jrps.v13.i5.1530 · ExternalCitation · doi-reference
Deep learning-based segmentation of cryo-electron tomograms
10.3791/64435 · ExternalCitation · doi-reference
Deep learning and virtual drug screening
10.4155/fmc-2018-0314 · ExternalCitation · doi-reference
Unlocking machine learning model decisions: a comparative analysis of LIME and SHAP for enhanced interpretability
10.52783/jes.1480 · ExternalCitation · doi-reference
The application of artificial intelligence to the bayesian model algorithm for combining genome data
10.54097/ykhccb53 · ExternalCitation · doi-reference
Applications of and issues with machine learning in medicine: bridging the gap with explainable AI
10.5582/bst.2024.01342 · ExternalCitation · doi-reference
Enhancing agricultural operations: big data analytics using distributed and parallel computing
10.57041/v3jj9f69 · ExternalCitation · doi-reference
Technical and biological biases in bulk transcriptomic data mining for cancer research
10.7150/jca.100922 · ExternalCitation · doi-reference
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