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125 results for “Structure learning”
Beware of the generic machine learning-based scoring functions in structure-based virtual screening
<p>Data sets and the rescoing scores utilized in the paper "Beware of the generic machine learning-based scoring functions in structure-based virtual screening" (DOI: 10.1093/bib/bbaa070)</p>
Experimental-data-driven deep learning strategy for Structural Health Monitoring of a plate in acoustic fields
Open the record for dataset details and reuse information.
Supporting dataset for "27Al NMR chemical shifts in zeolite MFI via machine learning acceleration of structure sampling and shift prediction"
<p>This dataset includes includes training databases of CHA, MOR and MFI zeolites, trained kernel ridge regression (KRR) models, and the initial structures utilized in the study. A more detailed description of the dataset can be found in the README file.</p> <p>Note, all MD simulations were performed using SiAlOH1 ML potential from the work of Erlebach et al. (Erlebach et al., Nat Commun 15, 4215 (2024)), available at: https://doi.org/10.5281/zenodo.10361794.</p>
Data for "Machine Learning Scoring Functions for Drug Discovery from Experimental and Computer-generated Protein-Ligand Structures: Towards Per-target Scoring Functions"
<p>Data used in "<em>Machine Learning Scoring Functions for Drug Discovery from Experimental and Computer-generated Protein-Ligand Structures: Towards Per-target Scoring Functions</em>"<br> by F. Pellicani, D. Dal Ben, A. Perali, S. Pilati</p> <p>If you use these data or the python script for your research or other activities, please cite the corresponding journal article.</p> <p> </p> <p>====================</p> <p>Uncompressing the zipped file <em>DataSFUnicam.zip</em> provies the following files and folders:</p> <p><br> <strong>DataSFUnicam/</strong></p> <p> </p> <p> ExperimentalDataPDBFiles/<br> <em>This folder contains 2408 .pdb files of experimental complex structures. The files are named with a univocal code corresponding to the protein-ligand complex.</em></p> <p> </p> <p> ExperimentalDataXLSXFile.xlsx<br> <em>This Excel file reports the experimental protein-ligand chemical information. In the sheet named “Foglio1”, the first column contains the univocal code of the protein-ligand complex, the second column contains the experimentally measured pK_d.</em></p> <p> </p> <p> SyntheticDataPDBFiles/<br> <em>This folder contains the .pdb files of the synthetic complex structures. The .pdb files are grouped in 17 folders according to just as many target proteins. The folders are named after the corresponding protein. Each folder contains the .pdb files for the best position of each protein-ligand pair according to the MOE docking score. The files are named with a univocal code.</em></p> <p> </p> <p> SyntheticDataXLSXFiles/<br> <em> The folder contains 17 Excel files with the chemical information of the synthetic protein-ligand complexes. The files are named after the corresponding target protein. In the sheet named “Foglio1” of each .xlsx file, the first column contains a univocal code of the protein-ligand complex in each conformation, the second column contains an auxiliary numerical code corresponding to the protein-ligand pair, the third column contains the experimentally measured pK_i, and the fourth column contains the docking score provided by the MOE software.</em></p> <p>====================</p> <p>USER GUIDE FOR THE PYTHON SCRIPT</p> <p>Download and uncompress the zipped file "<em>SFUnicam.zip</em>" with a command like "<em>unzip SFUnicam.zip</em>". </p> <p>The following file structure is created:</p> <p><em>SFUnicam/</em></p> <p> <em>ComplexToBePredictedFolder/4ey5_30.pdb <br> MaxAssMatrix.npy<br> my_model<br> devStndSynt.npy<br> mediaSynt.npy<br> UnicamSF13prot.py<br> README.txt</em><br> <br> The subfolder "<em>ComplexToBePredictedFolder/</em>" contains the example PDB file "<em>4ey5_30.pdb</em>".</p> <p>-) To execute the script "<em>UnicamSF13prot.py</em>", Python 3 should be installed with the following libraries and sublibraries:<br> <em>Keras:<br> Regularizers<br> Sequential (keras.models)<br> Conv1D, Dense, MaxPooling1D, GlobalMaxPooling1D, GlobalAveragePooling1D, AveragePooling1D (keras.layers)<br> Adam (keras.optimizers)<br> Numpy</em><br> <em>Tensorflow</em></p> <p>Operation:<br> -) Copy the .pdb file related to the protein-ligand complex whose affinity is to be predicted in the subfolder “<em>ComplexToBePredictedFolder/</em>”.<br> -) Make sure the following files are in the same folder where the python script is:<br> <em>MaxAssMatrix.npy<br> mediaSynt.npy<br> devStndSynt.npy<br> my_model</em><br> -) Run the code using Python 3 with a command like "<em>python3.x UnicamSF13prot.py</em>".<br> -) Enter the name of the protein-ligand PDB file whose affinity is to be predicted (excluding the extension ".pdb").<br> -) Read the predicted affinity from screen.<br> </p> <p> </p>
Feasibility and Utility of Artificial Intelligence (AI) / Machine Learning (ML) - Driven Advanced Intraoperative Visualization and Identification of Critical Anatomic Structures and Procedural Phases
ClinicalTrials.gov study NCT05775133. IPD Sharing: NO. Countries: 1. Publications: 0.
The Effect of Structured Feedback on Learning, Clinical Confidence and Anxiety
ClinicalTrials.gov study NCT06890819. IPD Sharing: NO. Countries: 1. Publications: 0.
The Impact of a Structured Physical Activity Program on Bone Strength and Psycho-Motor Learning of Young Children
ClinicalTrials.gov study NCT01407458. IPD Sharing: Not stated. Countries: 1. Publications: 0.
To Learn How Bone Structure and Bone Mass Change After Long-term PPI Use
ClinicalTrials.gov study NCT01306799. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Practice Structure on Motor Learning in Post-Stroke Patients
ClinicalTrials.gov study NCT00604747. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Spatial constraints and cell surface molecule depletion structure a randomly connected learning circuit
GEO Series GSE276028. Drosophila melanogaster. 9 samples. Type: Expression profiling by high throughput sequencing.
Fluo-Cast-Bright: A Deep Learning Pipeline for the Non-Invasive Prediction of Chromatin Structure and Developmental Potential in Live Oocytes
GEO Series GSE266845. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
Data from the manuscript: Machine learning reveals structural characteristics of stereochemistry-specific interdigitation of synthetic monomycoloyl glycerol analogs
<h2>Four membrane configurations:</h2> <p>1. single = single bilayer (512 MMG molecules and 25600 water molecule) <br>2. db_large = large double bilayer (1024 MMG molecules and 51200 water molecules)<br>3. id_small = interdigitated double bilayer (256 MMG molecules and 12800 water molecules)<br>4. db_small = small double bilayer (256 MMG molecules and 12800 water molecules)</p> <p>Each membrane configuration has 2 different MMG analogs:</p> <p><br>1. MMG1 = MMG-1, 1:1 racemic mixture of stereoisomers with (2R,3S)/(2S,3R) configurations<br>2. MMG6 = MMG-6, 1:1 racemic mixture of stereoisomers with (2R,3R)/(2S,3S) configurations</p> <h2>File names:</h2> <p><br>Each trajectory folder contains starting structures, input structure files, trajectory, energy file and system topology file.<br>Additionally, the .mdp files and topologies are located in their own folders.</p> <p>- start.gro = starting structure before energy minimization**<br>- eq.gro = input structure file for production<br>- eq.cpt = equilibration checkpoint file<br>- run.gro = final frame structure file<br>- run.cpt = production checkpoint file<br>- run.tpr = production tpr file <br>- run.xtc = production trajectory file, final 500 ns<br>- run.edr = production energy file, final 500 ns</p> <p>the <em>db_large</em> system is built with equilibrated <em>db_small</em> system, hence the starting structure is before the equilibration.</p> <h2>MDP-files:</h2> <p><br>Molecular dynamics parameter files can be found in their own folder (05_mdps/). Files for energy minimization,<br>equilibration and production run are provided.</p> <p>- em1_charmm36.mdp = 1st energy minimization (for all systems)<br>- em_charmm36.mdp = 2nd energy minimization (for all systems)<br>- eq1_noposres_charmm36.mdp = equilibration without position restraints (for single)<br>- eq1_posresin_charmm36.mdp = equilibration with O1 atoms constrained in Z-direction (for db_large, id_small, db_small)<br>- production_charmm36.mdp = production run (for all systems)</p> <h2>Topologies and position restraint files:</h2> <p><br>System topologies for each MMG analog can be found in their own folders (06_top/MMG1/, 06_top/MMG6/). <br>Molecular topologies, forcefield parameters and position restraint files can be found from their respective folders (06_top/MMG1/topol/, 06_top/MMG6/topol/).</p> <h3>MMG-1:</h3> <p><br>- db_large-MMG1.top = MMG-1 large double-bilayer system<br>- db_small-MMG1.top = MMG-1 small systems: interdigitated and non-interdigitated double bilayers <br>- single-MMG1.top = MMG-1 single bilayer system<br>- forcefield.itp = force field parameters<br>- TIP3_CHARMM36.itp = water model<br>- MMG1_2R3S_outer.itp = outer leaflet 2R3S MMG-1 <br>- MMG1_2S3R_outer.itp = outer leaflet 2S3R MMG-1 <br>- MMG1_2R3S_inner.itp = inner leaflet 2R3S MMG-1<br>- MMG1_2S3R_inner.itp = inner leaflet 2S3R MMG-1<br>- posres_MMG1_tail_z.itp = position restraints on MMG tails in z-direction<br>- posres_MMG1_O1_z.itp = position restraints on MMG headgroup O1 atom in z-direction</p> <h3>MMG-6:</h3> <p><br>- db_large-MMG6.top = MMG-6 large double-bilayer system<br>- db_small-MMG6.top = MMG-6 small systems: interdigitated and non-interdigitated double bilayers<br>- single-MMG6.top = MMG-6 single bilayer system<br>- forcefield.itp = force field parameters<br>- TIP3_CHARMM36.itp = water model<br>- MMG6_2S3S_outer.itp = outer leaflet 2S3S MMG-6 <br>- MMG6_2R3R_outer.itp = outer leaflet 2R3R MMG-6<br>- MMG6_2S3S_inner.itp = inner leaflet 2S3S MMG-6<br>- MMG6_2R3R_inner.itp = inner leaflet 2R3R MMG-6 <br>- posres_MMG6_tail_z_2R3R.itp = position restraints on tails in z-direction <br>- posres_MMG6_tail_z_2S3S.itp = position restraints on tails in z-direction<br>- posres_MMG6_O1_z.itp = position restraints on MMG headgroup O1 atom in z-direction</p>
C2SafeRust: Translating C to Safe Rust via incorporating In-context Learning and Program Structures into LLM
<p>This repository contains the code and dataset for the paper <strong>C2SafeRust: Translating C to Safe Rust via Incorporating In-Context Learning and Program Structures into LLM</strong> in FSE 2025.</p> <p><strong>C2SafeRust</strong> is an advanced tool designed to automatically translate C code into safe, readable, and maintainable Rust code. Leveraging in-context learning (ICL), C2SafeRust generates idiomatic Rust that adheres to Rust's safety guarantees. The tool employs detailed program structures, such as Control Flow Graphs (CFG) and Data Dependency Graphs (DDG), along with intermediate variable values obtained through program instrumentation. By integrating these elements with a Large Language Model (LLM), C2SafeRust ensures high syntactic and semantic correctness in the translation process, while maintaining the readability and maintainability of the resulting Rust code.</p>
Test-C2SafeRust: Translating C to Safe Rust via incorporating In-context Learning and Program Structures into LLM
<p>This is a tool for translating C to safe Rust via incorporating In-context Learning and Program Structures into LLM.</p>
C2SafeRust: Translating C to Safe Rust via incorporating In-context Learning and Program Structures into LLM
<p>This repository contains the code and dataset for the paper <strong>C2SafeRust: Translating C to Safe Rust via Incorporating In-Context Learning and Program Structures into LLM</strong> in FSE 2025.</p> <p><strong>C2SafeRust</strong> is an advanced tool designed to automatically translate C code into safe, readable, and maintainable Rust code. Leveraging in-context learning (ICL), C2SafeRust generates idiomatic Rust that adheres to Rust's safety guarantees. The tool employs detailed program structures, such as Control Flow Graphs (CFG) and Data Dependency Graphs (DDG), along with intermediate variable values obtained through program instrumentation. By integrating these elements with a Large Language Model (LLM), C2SafeRust ensures high syntactic and semantic correctness in the translation process, while maintaining the readability and maintainability of the resulting Rust code.</p>
C2SafeRust: Translating C to Safe Rust via incorporating In-context Learning and Program Structures into LLM
<p>This repository contains the code and dataset for the paper <strong>C2SafeRust: Translating C to Safe Rust via Incorporating In-Context Learning and Program Structures into LLM</strong> in FSE 2025.</p> <p><strong>C2SafeRust</strong> is an advanced tool designed to automatically translate C code into safe, readable, and maintainable Rust code. Leveraging in-context learning (ICL), C2SafeRust generates idiomatic Rust that adheres to Rust's safety guarantees. The tool employs detailed program structures, such as Control Flow Graphs (CFG) and Data Dependency Graphs (DDG), along with intermediate variable values obtained through program instrumentation. By integrating these elements with a Large Language Model (LLM), C2SafeRust ensures high syntactic and semantic correctness in the translation process, while maintaining the readability and maintainability of the resulting Rust code.</p>
FIGURE 1 in Learning from Linnaeus: towards developing the foundation for a general structure concept for morphology*
FIGURE 1. Aristotelian definitions: The definition of the 'parent' kind is inherited by all of its 'first child generation' kinds and forms the Genus part of their definitions. These represent properties that an instance of a 'child' kind necessarily has to possess. On the other hand, the essential property of a 'child' kind represents the distinguishing property that is in combination with the Genus part sufficient for the recognition of an instance of that kind, since all and only individuals that are instances of this kind do possess this property. This forms the Differentia part of the kind's definition. Genus and Differentia together represent the essence of the kind and at the same time its definition.
MUSCLE-ML: Multimodal Integration of Muscle Strength, Structure by Machine Learning for Precision Rehabilitation After ACL Injury
ClinicalTrials.gov study NCT07284771. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Identification of flap structure specific endonuclease 1 as a factor involved in LTM formation of aversive learning
GEO Series GSE11520. Rattus norvegicus. 8 samples. Type: Expression profiling by array.
Spatial constraints and cell surface molecule depletion structure a randomly connected learning circuit
GEO Series GSE296723. Drosophila melanogaster. 6 samples. Type: Expression profiling by high throughput sequencing.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.