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99 results for “Transfer Learning”
Augment Single-cell RNA-seq data with Surface Protein Levels using Gene set-based Deep Learning and Transfer Learning Methods
<p><span>Necessary data, scripts and saved models for "Augment Single-cell RNA-seq data with Surface Protein Levels using Gene set-based Deep Learning and Transfer Learning Methods" manuscript.</span></p>
Quantifying the hardness of bioactivity prediction tasks for transfer learning
<p>This dataset contains the following information about <a href="https://github.com/microsoft/FS-Mol">FS-Mol</a> dataset:</p> <ul> <li>Embedding of all the molecules in each task with different featurization methods</li> <li>External chemical distance between train and test tasks caculated with optimal transport dataset distance (OTDD) method</li> <li>External protein distance between train and test tasks calculated from ESM-2 respresentation of the proteins</li> <li>Internal chemical hardness (which is a random forest for all the train and test tasks)</li> <li>Prototypical network performance on the test tasks</li> <li>Random forest performance on the test tasks</li> </ul> <p> </p> <p><strong>Paper Abstract:</strong></p> <p>Today, machine learning methods are widely employed in drug discovery. However, the chronic lack of data continues to hamper their further development, validation, and application. Several modern strategies aim to mitigate the challenges associated with data scarcity by learning from data on related tasks. These knowledge-sharing approaches encompass transfer learning, multi-task learning, and meta-learning. A key question remaining to be answered for these approaches is about the extent to which their performance can benefit from the relatedness of available source (training) tasks, in other words, how difficult (“hard”) a test task is to a model, given the available source tasks. This study introduces a new method for quantifying and predicting the hardness of a bioactivity prediction task based on its relation to the available training tasks. The approach involves the generation of protein and chemical representations and the calculation of distances between the bioactivity prediction task and the available training tasks. In the example of meta-learning, we demonstrate that the proposed task hardness metric is inversely correlated with performance. The metric will be useful in estimating the task-specific gain in performance that can be achieved through meta-learning. </p> <p> </p>
Feature Reuse and Scaling: Understanding Transfer Learning with Protein Language Models
<p>Data and checkpoints for 'Feature Reuse and Scaling: Understanding Transfer Learning with Protein Language Models'</p>
Dataset for paper "Ensemble transfer learning approach for gastric cancer prediction in electronic health records"
<p>This is datasets and codes for research on "Ensemble transfer learning approach for gastric cancer prediction in electronic health records"</p>
DeepC: predicting 3D genome folding using megabase-scale transfer learning
GEO Series GSE137437. Homo sapiens. 6 samples. Type: Other.
Data for "Data Imbalance, Uncertainty Quantification, and Generalization via Transfer Learning in Data-driven Parameterizations: Lessons from the Emulation of Gravity Wave Momentum Transport in WACCM"
Open the record for dataset details and reuse information.
Application of Deep Learning to Jointly Assess Embryo Development to Improve Pregnancy Outcome of Embryo Transfer
ClinicalTrials.gov study NCT05671601. IPD Sharing: Not stated. Countries: 0. Publications: 0.
A Transfer Learning Radiomics Model for Predicting Response to Initial Transarterial Embolization in Patients with Gastroenteropancreatic Neuroendocrine Tumor Liver Metastases
ClinicalTrials.gov study NCT06853457. IPD Sharing: Not stated. Countries: 0. Publications: 0.
The Impact of Different Simulator Characteristics on Transfer of Learning
ClinicalTrials.gov study NCT03280199. IPD Sharing: NO. Countries: 0. Publications: 0.
Transfer learning associates CAFs with EMT and inflammation in tumor cells in human tumors and organoid co-culture in pancreatic ductal adenocarcinoma [Bulk RNA-seq]
GEO Series GSE245319. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Transfer learning associates CAFs with EMT and inflammation in tumor cells in human tumors and organoid co-culture in pancreatic ductal adenocarcinoma
GEO Series GSE246813. Homo sapiens. 7 samples. Type: Expression profiling by high throughput sequencing.
spaTransfer: transfer learning for single-cell and spatial transcriptomics data using non-negative matrix factorization
GEO Series GSE317379. Homo sapiens. 2 samples. Type: Other.
Transfer learning associates CAFs with EMT and inflammation in tumor cells in human tumors and organoid co-culture in pancreatic ductal adenocarcinoma [MULTIseq]
GEO Series GSE246812. Homo sapiens. 1 samples. Type: Expression profiling by high throughput sequencing.
Universal prediction of cell cycle position using transfer learning
GEO Series GSE171636. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing.
Deep transfer learning for detection of breast arterial calcifications on mammograms: a comparative study
<p>Introduction Breast arterial calcifications (BAC) are common incidental findings on routine mammograms, which<br>have been suggested as a sex-specific biomarker of cardiovascular disease (CVD) risk. Previous work showed the efficacy<br>of a pretrained convolutional network (CNN), VCG16, for automatic BAC detection. In this study, we further tested<br>the method by a comparative analysis with other ten CNNs.<br>Material and methods Four-view standard mammography exams from 1,493 women were included in this retrospective<br>study and labeled as BAC or non-BAC by experts. The comparative study was conducted using eleven<br>pretrained convolutional networks (CNNs) with varying depths from five architectures including Xception, VGG,<br>ResNetV2, MobileNet, and DenseNet, fine-tuned for the binary BAC classification task. Performance evaluation<br>involved area under the receiver operating characteristics curve (AUC-ROC) analysis, F1-score (harmonic mean of precision<br>and recall), and generalized gradient-weighted class activation mapping (Grad-CAM++) for visual explanations.<br>Results The dataset exhibited a BAC prevalence of 194/1,493 women (13.0%) and 581/5,972 images (9.7%). Among<br>the retrained models, VGG, MobileNet, and DenseNet demonstrated the most promising results, achieving AUCROCs<br>> 0.70 in both training and independent testing subsets. In terms of testing F1-score, VGG16 ranked first, higher<br>than MobileNet (0.51) and VGG19 (0.46). Qualitative analysis showed that the Grad-CAM++ heatmaps generated<br>by VGG16 consistently outperformed those produced by others, offering a finer-grained and discriminative localization<br>of calcified regions within images.<br>Conclusion Deep transfer learning showed promise in automated BAC detection on mammograms, where relatively<br>shallow networks demonstrated superior performances requiring shorter training times and reduced resources.<br>Relevance statement Deep transfer learning is a promising approach to enhance reporting BAC on mammograms<br>and facilitate developing efficient tools for cardiovascular risk stratification in women, leveraging large-scale mammographic<br>screening programs.<br>Key points<br>• We tested different pretrained convolutional networks (CNNs) for BAC detection on mammograms.<br>• VGG and MobileNet demonstrated promising performances, outperforming their deeper, more complex<br>counterparts.<br>• Visual explanations using Grad-CAM++ highlighted VGG16’s superior performance in localizing BAC.</p>
spaTransfer: transfer learning for single-cell and spatial transcriptomics data using non-negative matrix factorization [DG]
GEO Series GSE317381. Homo sapiens. 4 samples. Type: Other.
Robust Augmenting Single-cell RNA-seq with Surface Protein Levels using Geneset Deep Learning and Transfer Learning
<p>This package contains all the code necessary to reproduce results in article submitted to Genes titled " Robust Augmenting Single-cell RNA-seq with Surface Protein Levels using Geneset Deep Learning and Transfer Learning"</p> <p> </p>
Accurate TCR-pMHC Interaction Prediction Using a BERT-based Transfer Learning Method
<p>The training data of TCR-BERT and pMHC-BERT and the healthy TCR dataset. 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>
Accurate TCR-pMHC Interaction Prediction using BERT-based Transfer Learning
<p>The training and test datasets 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>
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.