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ShareScore release 0.9.0
Dataset results
23 results for “Learning Pathways”
Deep learning models predicting gene functions and pathways using public DRKG knowledge graph and graph neural network
<p>The attached dataset contains pretrained link prediction models, as described in our paper 'Morphological Map of Under- and Over-Expression of Genes in Human Cells'.</p>
Data deposit accompanying Accurate Energy Barriers for Catalytic Reaction Pathways: An Automatic Training Protocol for Machine Learning Force Fields
<p>Dataset accompanying the paper: <em>"Accurate Energy Barriers for Catalytic Reaction Pathways: An Automatic Training Protocol for Machine Learning Force Fields"</em>. Contains the training sets curated during active learning as well as .xyz files used for creating the Figures. <br> <br> The paper highlights that the computational efficiency of ML force fields not only results in decreased computational costs for routine catalytic investigations but also facilitates more comprehensive exploration of catalytic pathways.</p> <p><strong>Published in NPJ Computational Materials</strong>: <a href="https://www.nature.com/articles/s41524-023-01124-2">https://www.nature.com/articles/s41524-023-01124-2</a><br> Formerly on Arxiv: <a href="https://arxiv.org/abs/2301.09931">https://arxiv.org/abs/2301.09931</a></p>
STM, locomotion and survival raw data for: A neural m6A/Ythdf pathway is required for learning and memory in Drosophila
<p>The files contain raw and summarized data files for the STM, locomotion and survival experiments published in the manuscript entitled " A neural m6A/Ythdf pathway is required for learning and memory in Drosophila". Instructions on how to process data and further information is found in the "summary" file.<br> </p>
Data from: Value-related learning in the olfactory bulb occurs through pathway-dependent peri-somatic inhibition of mitral cells
<p>Associating values to environmental cues is a critical aspect of learning from experiences, allowing animals to predict and maximise future rewards. Value-related signals in the brain were once considered a property of higher sensory regions, but their wide distribution across many brain regions is increasingly recognised. Here, we investigate how reward-related signals begin to be incorporated, mechanistically, at the earliest stage of olfactory processing, namely, in the olfactory bulb. In head-fixed mice performing Go/No-Go discrimination of closely related olfactory mixtures, rewarded odours evoke widespread inhibition in one class of output neurons, that is, in mitral cells but not tufted cells. The temporal characteristics of this reward-related inhibition suggest it is odour-driven, but it is also context-dependent since it is absent during pseudo-conditioning and pharmacological silencing of the piriform cortex. Further, the reward-related modulation is present in the somata but not in the apical dendritic tuft of mitral cells, suggesting an involvement of circuit component located deep in the olfactory bulb. Depth-resolved imaging from granule cell dendritic gemmules suggests that granule cells that target mitral cells receive a reward-related extrinsic drive. Thus, our study supports the notion that value-related modulation of olfactory signals is a characteristic of olfactory processing in the primary olfactory area and narrows down the possible underlying mechanisms to deeper circuit components that contact mitral cells peri-somatically.</p>
A machine learning framework for extracting information from biological pathway images in the literature
<p><strong>466 target chemicals_selected chemicals:</strong></p> <p>Target chemicals satisfying the criteria for biochemical reactions not covered by MetaNetX and KEGG.</p> <p> </p> <p><strong>466 target chemicals_statistics:</strong><br>Numbers of MetaCyc reactions, papers, and pathway images collected for 466 target chemicals from the bio-based chemicals map (Jang et al., Trends in Biotechnology, 2023).</p> <p> </p> <p><strong>arrow detection_bounding box labels:</strong><br>Bounding box labels for 6,471 images in the training and validation datasets and 100 images in the test dataset. The corresponding images are available in "arrow detection_training and validation datasets.zip".</p> <p> </p> <p><strong>arrow detection_test dataset:</strong></p> <p>Test dataset for arrow detection using Faster R-CNN model. A total of 100 images have been prepared from 89 papers searched through PubMed Central (PMC).</p> <p> </p> <p><strong>arrow detection_training and validation datasets:</strong></p> <p>Training and validation datasets for arrow detection using Faster R-CNN model. A total of 6,471 images have been prepared, including 2,332 images from five different sources and 4,139 augmented images.</p> <p> </p> <p><strong>EBPI outputs:</strong></p> <p>Reaction information extracted using EBPI from 49,846 biological pathway images across 466 target chemicals.</p> <p> </p> <p><strong>text classification_training, validation and test datasets:</strong></p> <p>Dataset for text classification using BioBERT. A total of 59,370 terms have been prepared, including 15,101 “gene” terms, 21,417 “protein” terms, and 22,852 “others” terms by combining the data from MetaCyc and the PaddleOCR results from the papers.</p>
Distinct representations of innate and learned threats within the thalamic-amygdala pathway
<p>Behavioral flexibility and timely reactions to salient stimuli are essential for survival. The subcortical thalamic-basolateral amygdala (BLA) pathway serves as a shortcut for salient stimuli ensuring rapid processing. Here, we show that BLA neuronal and thalamic axonal activity mirror the defensive behavior evoked by an innate visual threat as well as an auditory learned threat. Importantly, perturbing this pathway compromises defensive responses to both forms of threats, in that animals fail to switch from exploratory to defensive behavior. Despite the shared pathway between the two forms of threat processing, we observed noticeable differences. Blocking beta-adrenergic receptors impair the defensive response to the innate but not the learned threats. This reduced defensive response, surprisingly, is reflected in the suppression of the activity exclusively in the BLA, as the thalamic input response remains intact. Our side-by-side examination highlights the similarities and differences between innate and learned threat-processing, thus providing new fundamental insights.</p>
Data from: Nematode mind: Exploring the role of the RNAi pathway in learning, memory and beyond
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Distinct representations of innate and learned threats within the thalamic-amygdala pathway
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Data from: Value-related learning in the olfactory bulb occurs through pathway-dependent peri-somatic inhibition of mitral cells
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Personality composition determines social learning pathways within shoaling fish
<p>In shaping how individuals explore their environment and interact with others, personality may mediate both individual and social learning. Yet increasing evidence indicates that personality expression is contingent on social context, suggesting that group personality composition may be key in determining how individuals learn about their environment. Here, we used recovery latency following simulated predator attacks to identify Trinidadian guppies (<em>Poecilia reticulata</em>) that acted in a consistently bold or shy manner. We then employed network-based diffusion analysis to track the spread of a novel foraging behaviour through groups containing different proportions of bold and shy fish. Informed associates promoted learning to a greater extent in bold individuals, but only within groups composed predominately of bold fish. As the proportion of shy fish within groups increased, bold individuals instead emerged as especially effective demonstrators that facilitated learning in others. Individuals were also more likely to learn overall within shy-dominated groups than in bold-dominated ones. We demonstrate that how individuals learn is conditional on group personality composition, indicating that selection may favour traits enabling individuals to better match their behavioural phenotype to their social environment.</p>
Second Opinion Machine Learning for Fast-Track Pathway Assignment in Hip and Knee Replacement Surgery
<p>This is the anonymized training dataset for the article "Second Opinion Machine Learning for Fast-Track Pathway Assignment in Hip and Knee Replacement Surgery: The Use of Patient-Reported Outcome Measures", aimed at developing Machine Learning models for deciding on optimal pathway assignment for total joint replacement patients.</p>
Personality composition determines social learning pathways within shoaling fish
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Predicting Drug Synergy through Drug-Associated Pathways Using Deep Learning
<p>example input dataset for one cross-validation run</p>
Data of "Chemistrees: Data-Driven Identification of Reaction Pathways via Machine Learning"
<p>This is the data and assosiated in-house code for the paper:</p> <p>Chemistrees: Data-Driven Identification of Reaction Pathways via Machine Learning</p> <p>Sander Roet, Christopher D. Daub, and Enrico Riccardi</p> <p>Journal of Chemical Theory and Computation <strong>2021</strong> <em>17</em> (10), 6193-6202</p> <p>DOI: 10.1021/acs.jctc.1c00458</p>
Deep learning for explainable prediction of HPV-status in head and neck cancer using transcriptome data organized as pathway treemaps
GEO Series GSE205308. Homo sapiens. 72 samples. Type: Expression profiling by high throughput sequencing.
Machine-learning Classification Identifies Early Systemic Sclerosis Patients that Improve with Abatacept treatment by Modulating CD28-Pathways
GEO Series GSE217067. Homo sapiens. 233 samples. Type: Expression profiling by high throughput sequencing.
A neural m6A/YTHDF pathway is required for learning and memory in Drosophila
GEO Series GSE147230. Drosophila melanogaster. 29 samples. Type: Expression profiling by high throughput sequencing; Other.
A Machine-learning approach to define and predict the therapeutic landscape of pan-cancer Hippo pathway dependency (ATAC-seq)
GEO Series GSE161018. Homo sapiens. 8 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
HepaCAM knockdown impairs learning and memory in mice by regulating cholesterol synthesis pathway
GEO Series GSE280264. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
A Machine-learning approach to define and predict the therapeutic landscape of pan-cancer Hippo pathway dependency (RNA-seq)
GEO Series GSE161010. Homo sapiens. 42 samples. Type: Expression profiling by high throughput sequencing.
ScienceDex guides
Understand access before you commit
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.