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95 results for “Interactive Learning”
RepliChrom: Interpretable machine learning predicts cancer-associated enhancer-promoter interactions using DNA replication timing
<p>This dataset accompanies the study "RepliChrom: Interpretable machine learning predicts cancer-associated enhancer-promoter interactions using DNA replication timing". The study introduces RepliChrom, a computational framework designed to predict enhancer–promoter interactions (EPIs) by leveraging multi-scale replication timing (RT) signals. This approach addresses the fundamental challenge of distinguishing gene targets regulated by distal enhancers from those activated by proximal transcriptional activity-a key problem in understanding the causal basis of complex diseases.</p> <p>Despite recent advances in high-throughput technologies such as Hi-C, ChIA-PET, and Hi-TrAC that allow genome-wide reconstruction of 3D chromatin architecture, the role of DNA replication timing in mediating these spatial interactions remains underexplored. RepliChrom fills this gap by using cell-type-specific RT profiles as predictive features for chromatin interaction inference.</p> <p>To support model development, training, and evaluation, we provide a comprehensive multi-omics dataset covering six human cell lines (K562, GM12878, HeLaS3, IMR90, NHEK, and HUVEC ), encompassing:</p> <p>Hi-C datasets: Processed chromatin interaction loops used to define positive and negative enhancer–promoter interaction pairs.</p> <p>ChIA-PET datasets: Interaction data anchored around transcription factor binding, including POLR2A and CTCF, used for model validation across different interaction types.</p> <p>Hi-TrAC datasets: Targeted chromatin accessibility-derived interaction data, offering complementary validation of the model on alternate platforms.</p> <p>Replication Timing (RT) data: Processed RT signal profiles for each cell line, used to extract multi-scale temporal features as inputs for RepliChrom.</p> <p>These datasets enable reproducibility of the model training process and serve as benchmark resources for future research into DNA replication–mediated regulation of 3D genome architecture.</p> <p><strong>Included Files</strong></p> <p>Hi-C_datasets.zip (2.23 MB): Processed Hi-C interaction pairs for six cell types.</p> <p>ChIA-PET_datasets.zip (818.18 KB): CTCF and POLR2A ChIA-PET interactions across multiple lines.</p> <p>Hi-TrAC_datasets.zip (196 bytes): Hi-TrAC-based chromatin interaction training datasets across multiple lines.</p> <p>Cellline_RT_data.zip (86.17 MB): Replication timing signal data across multiple human cell types for multi-scale replication timing feature extraction.</p> <p><strong>Usage</strong><br>All datasets are intended for academic, non-commercial use. The provided files can be directly used to reproduce the training and evaluation of RepliChrom, and may also support broader applications in enhancer–promoter modeling, replication-timing analysis, and 3D genomics studies. For detailed usage instructions and code implementation, please refer to the GitHub repository: https://github.com/DaoFuying/RepliChrom</p>
Deep learning improves prediction of drug-drug and drug-food interactions
<p>Information on drug-drug interactions predicted by DeepDDI</p> <p> </p> <p> </p>
A machine learning model reveals expansive downregulation of ligand-receptor interactions enhancing lymphocyte infiltration in melanoma with developed resistance to Immune Checkpoint Blockade
<p>Data repository containing the data to reproduce the results and findings that are published in:</p> <p>Sahni, S., Wang, B., Wu, D. <em>et al.</em> A machine learning model reveals expansive downregulation of ligand-receptor interactions that enhance lymphocyte infiltration in melanoma with developed resistance to immune checkpoint blockade. <em>Nat Commun</em> <strong>15</strong>, 8867 (2024). https://doi.org/10.1038/s41467-024-52555-4</p>
PIGNet2: A versatile deep learning-based protein-ligand interaction prediction model for accurate binding affinity scoring and virtual screening
<p>Training and test datasets of the paper "Improving the versatility of deep learning-based protein-ligand interaction prediction for accurate binding affinity scoring and virtual screening".</p>
NGS data from: Deploying synthetic coevolution and machine learning to engineer protein-protein interactions
<p>Fine-tuning of protein-protein interactions occurs naturally through coevolution, but this process is difficult to recapitulate in the laboratory. We describe a synthetic platform for protein-protein coevolution that can isolate matched pairs of interacting muteins from complex libraries. This large dataset of coevolved complexes<span class="Apple-converted-space"> </span>drove a systems-level analysis of molecular recognition between Z domain-affibody pairs spanning a wide range of structures, affinities, cross-reactivities, and orthogonalities, and captured a broad spectrum of coevolutionary networks. Furthermore, we harnessed pre-trained protein language models to expand, <em>in silico</em>, the amino acid diversity of our coevolution screen, predicting remodeled interfaces beyond the reach of the experimental library. The integration of these approaches provides a means of generating protein complexes with diverse molecular recognition properties as tools for biotechnology and synthetic biology.</p>
Accurate TCR-pMHC Interaction Prediction Using a BERT-based Transfer Learning Method
<p>The datasets used for the TABR-BERT. For the complete training and testing code of TABR-BERT, see <a href="https://github.com/Freshwind-Bioinformatics/TABR-BERT">Freshwind-Bioinformatics/TABR-BERT: TABR-BERT: an Accurate and Robust BERT-based Transfer Learning Model for TCR-pMHC Interaction Prediction (github.com)</a>.</p>
Capturing the interactions in the BaSnF$_4$ ionic conductor: a comparison of machine-learning potentials and polarizable force fields
<p>The current files contain all the original data for our work "Capturing the interactions in the BaSnF<sub>4 </sub>ionic conductor: a comparison of machine-learning potentials and polarizable force fields". </p>
Deep Learning-based 3D single-cell imaging analysis pipeline for quantifying cell-cell interaction dynamics in the tumor microenvironment
<p>These are 3D live-cell imaging datasets of gastric tumor organoids in co-culture with primary human Natural Killer (NK) cells. The datasets were analyzed by a new, deep learning-based 3D image analysis software tool, SiQ-3D, which we developed and presented in the paper titled "Deep Learning-based 3D single-cell imaging analysis pipeline for quantifying cell-cell interaction dynamics in the tumor microenvironment". Interested users can download the SiQ-3D software code from GitHub (https://github.com/simonlbd1/SiQ-3D) or Code Ocean (https://codeocean.com/capsule/6676007/tree/v2), analyze the 3D image datasets locally, and cross-check the results with the SiQ-3D quantified results that we provided here.</p>
Modulating Interaction of Motor Learning Networks in Rehabilitation of Stroke
ClinicalTrials.gov study NCT03086551. IPD Sharing: NO. Countries: 1. Publications: 3.
NGS data from: Deploying synthetic coevolution and machine learning to engineer protein-protein interactions
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Development of compositionality through interactive learning of language and action of robots
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Data from: Costs of deception and learned resistance in deceptive interactions
The costs that species suffer when deceived are expected to drive learned resistance, although this relationship has seldom been studied experimentally. Flowers that elicit mating behaviour from male insects by mimicking conspecific females provide an ideal system for such investigation. Here, we explore interactions between a sexually deceptive daisy with multiple floral forms that vary in deceptiveness, and the male flies that pollinate it. We show that male pollinators are negatively impacted by the interaction, suffering potential mating costs in terms of their ability and time taken to locate genuine females within deceptive inflorescences. The severity of these costs is determined by the amount of mating behaviour elicited by deceptive inflorescences. However, inexperienced male flies exhibit the ability to learn to discriminate the most deceptive inflorescences as female mimics and subsequently reduce the amount of mating behaviour they exhibit on them with increased exposure. Experienced males, which interact with sexually deceptive forms naturally, exhibit similar patterns of reduced mating behaviour on deceptive inflorescences in multiple populations, indicating that pollinator learning is widespread. As sexually deceptive plants are typically dependent on the elicitation of mating behaviour from male pollinators for pollination, this may result in antagonistic coevolution within these systems.
Data from: Interspecific interactions and learning variability jointly drive geographic differences in mate preferences
Co-occurrence of closely related species can cause behavioral interference in mating and increase hybridization risk. Theoretically, this could lead to the evolution of more species-specific mate preferences and sexual signaling traits. Alternatively, females can learn to reject heterospecific males, to avoid male sexual interference from closely related species. Such learned mate discrimination could also affect conspecific mate preferences if females generalize from between species differences to prefer more species-specific mating signals. Female damselflies of the banded demoiselle (Calopteryx splendens) learn to reject heterospecific males of the beautiful demoiselle (C. virgo) through direct premating interactions. These two species co-occur in a geographic mosaic of sympatric and microallopatric populations. Whereas C. virgo males have fully melanized wings, male C. splendens wings are partly melanized. We show that C. splendens females in sympatry with C. virgo prefer smaller male wing patches in conspecific males after learning to reject heterospecific males. In contrast, allopatric C. splendens females with experimentally induced experience with C. virgo males did not discriminate against larger male wing patches. Wing patch size might indicate conspecific male quality in allopatry. Co-occurrence with C. virgo therefore causes females to prefer conspecific male traits that are more species specific, contributing to population divergence and geographic variation in female mate preferences.
Datasets for "Physicochemical graph neural network for learning protein-ligand interaction fingerprints from sequence data"
<div> <p>Datasets used for implementing the <a href="https://github.com/huankoh/PSICHIC">PSICHIC</a> experiments shown in the <a href="https://doi.org/10.1101/2023.09.17.558145">manuscript</a>.</p> <p> </p> </div>
Dataset from the second wave of a pre-post study to test an interactive blended learning tool for delirium management in Belgian Nursing Homes by measuring delirium knowledge and evaluation of the tool
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Consolidated dataset of a pre-post study to test an interactive blended learning approach for delirium management in Belgian Nursing Homes
<p><span>Consolidated dataset of a pre-post study to test an interactive blended learning approach for delirium management in Belgian Nursing Homes </span><span>by measuring delirium knowledge (with the delirium knowledge questionnaire (DKQ) in both waves), strain of care for delirium (with the strain of care for delirium index (SCDI) in the first wave) and evaluation of the blended learning approach after the second wave.</span></p>
Dataset from the first wave of a pre-post study to test an interactive blended learning tool for delirium management in Belgian Nursing Homes by measuring delirium knowledge and strain of care for delirium
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The Opposite Interaction Between Academic Achievement and Self-Regulated Learning on Critical Thinking in Undergraduate Medical Students
<p>This is a single center survey of critical thinking disposition in undergraduate medical students. The survey was performed in three different batches in that school.</p>
Predicting the pro-longevity or anti-longevity effect of model organism genes with enhanced Gaussian noise augmentation-based contrastive learning on protein-protein interaction networks
<p>The datasets used to evaluate Enhanced Gaussian noise augmentation-based contrastive learning (EGsCL) against predicting the pro-longevity or anti-longevity effect of model organism gene. This repo also includes the pretrained encoders that obtained the best predictive performance for each organism (see Table 2).</p>
DrugBLIP: Exploring the Protein-Molecule Interaction Mechanisms with a Multi-task Learning Graph Transformer
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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.