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117
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ShareScore release 0.9.0
Dataset results
117 results for “Machine-learning”
A Machine-learning Model to Predict Lymph Node Metastasis of Intrahepatic Cholangiocarcinoma
ClinicalTrials.gov study NCT06290739. IPD Sharing: YES. Countries: 1. Publications: 0.
Machine-learning Algorithm to Differentiate Intraoperative Ketamine Dosing Based on Electroencephalographic Density Spectrum Array Analysis
ClinicalTrials.gov study NCT05256264. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Machine-learning Optimization for Prostate Brachytherapy Planning
ClinicalTrials.gov study NCT02943824. IPD Sharing: NO. Countries: 1. Publications: 0.
Validating a New Machine-Learned Accelerometer Algorithm Using Doubly Labeled Water
ClinicalTrials.gov study NCT05736302. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Machine-learning Based Prediction Model in Primary Immune Thrombocytopenia
ClinicalTrials.gov study NCT05116423. IPD Sharing: Not stated. Countries: 1. Publications: 0.
MAP THE SMA: a Machine-learning Based Algorithm to Predict THErapeutic Response in Spinal Muscular Atrophy
ClinicalTrials.gov study NCT05769465. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Health Status, Infection and Disease in California Sea Lions (Zalophus californianus) studied using a canine microarray platform and machine-learning approaches
GEO Series GSE29497. Zalophus californianus; Canis lupus familiaris. 73 samples. Type: Expression profiling by array.
EpiGe: A cytosine methyl-genotyping PCR-based machine-learning tool for rapid classification of medulloblastoma
GEO Series GSE210723. Homo sapiens. 74 samples. Type: Methylation profiling by array.
Long-term trends of ambient nitrate (NO3-) concentrations across China based on ensemble machine-learning models
<p>The monthly NO3- concentrations across China during 2005-2015</p>
External, Multicentre Validation of a Machine-Learning Model to Predict Colonic Adenoma in Indian Adults
ClinicalTrials.gov study NCT07329816. IPD Sharing: NO. Countries: 0. Publications: 0.
Machine-learning detects non-invasive 5-hydroxymeythylcytosine biomarkers for predicting response to chemotherapy in patients with metastatic colorectal cancer
GEO Series GSE203071. Homo sapiens. 155 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 (ATAC-seq)
GEO Series GSE161018. Homo sapiens. 8 samples. Type: Genome binding/occupancy 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.
A Machine-learning approach to define and predict the therapeutic landscape of pan-cancer Hippo pathway dependency
GEO Series GSE161019. Homo sapiens. 50 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Inactive-enriched machine-learning models exploiting patent data improve structure-based virtual screening for PDL1 dimerizers
<p>The 12 VS scenarios considered in this study employing six training-test data partitions<strong> </strong>(A-F). All training sets employ the same set of 371 actives (WO2015160641A2), but differ on the considered set of inactives and hence are uniquely identified by the latter (either TrueInactives, DeepCoys, RandomDecoys or ActivesOnly). Likewise, all test sets employ the same 297 actives (WO201503820A1), none of them also included in the training set, but different sets of inactives (TrueInactives or DeepCoys). </p> <p> </p> <table align="center"> <caption>Table 1. Six virtual screening scenarios corresponding to six pairs of training-test data for each type of SFs (classification or regression)</caption> <thead> <tr> <th scope="col">Partition ID</th> <th scope="col">Training set</th> <th scope="col">Test set</th> <th scope="col">Type</th> </tr> </thead> <tbody> <tr> <td>A</td> <td>DeepCoys</td> <td>TrueInactives</td> <td>Classification</td> </tr> <tr> <td>B</td> <td>RandomDecoys</td> <td>TrueInactives</td> <td>Classification</td> </tr> <tr> <td>C</td> <td>ActivesOnly</td> <td>TrueInactives</td> <td>Classification</td> </tr> <tr> <td>D</td> <td>TrueInactives</td> <td>DeepCoys</td> <td>Classification</td> </tr> <tr> <td>E</td> <td>RandomDecoys</td> <td>DeepCoys</td> <td>Classification</td> </tr> <tr> <td>F</td> <td>ActivesOnly</td> <td>DeepCoys</td> <td>Classification</td> </tr> <tr> <td>A</td> <td>DeepCoys</td> <td>TrueInactives</td> <td>Regression</td> </tr> <tr> <td>B</td> <td>RandomDecoys</td> <td>TrueInactives</td> <td>Regression</td> </tr> <tr> <td>C</td> <td>ActivesOnly</td> <td>TrueInactives</td> <td>Regression</td> </tr> <tr> <td>D</td> <td>TrueInactives</td> <td>DeepCoys</td> <td>Regression</td> </tr> <tr> <td>E</td> <td>RandomDecoys</td> <td>DeepCoys</td> <td>Regression</td> </tr> <tr> <td>F</td> <td>ActivesOnly</td> <td>DeepCoys</td> <td>Regression</td> </tr> </tbody> </table> <p> </p>
MRI radiomics-based machine-learning classification of bone chondrosarcoma
<p><strong>Purpose: </strong>To evaluate the diagnostic performance of machine learning for discrimination between low-grade and high-grade cartilaginous bone tumors based on radiomic parameters extracted from unenhanced magnetic resonance imaging (MRI).</p> <p><strong>Methods: </strong>We retrospectively enrolled 58 patients with histologically-proven low-grade/atypical cartilaginous tumor of the appendicular skeleton (n = 26) or higher-grade chondrosarcoma (n = 32, including 16 appendicular and 16 axial lesions). They were randomly divided into training (n = 42) and test (n = 16) groups for model tuning and testing, respectively. All tumors were manually segmented on T1-weighted and T2-weighted images by drawing bidimensional regions of interest, which were used for first order and texture feature extraction. A Random Forest wrapper was employed for feature selection. The resulting dataset was used to train a locally weighted ensemble classifier (AdaboostM1). Its performance was assessed via 10-fold cross-validation on the training data and then on the previously unseen test set. Thereafter, an experienced musculoskeletal radiologist blinded to histological and radiomic data qualitatively evaluated the cartilaginous tumors in the test group.</p> <p><strong>Results: </strong>After feature selection, the dataset was reduced to 4 features extracted from T1-weighted images. AdaboostM1 correctly classified 85.7 % and 75 % of the lesions in the training and test groups, respectively. The corresponding areas under the receiver operating characteristic curve were 0.85 and 0.78. The radiologist correctly graded 81.3 % of the lesions. There was no significant difference in performance between the radiologist and machine learning classifier (P = 0.453).</p> <p><strong>Conclusions: </strong>Our machine learning approach showed good diagnostic performance for classification of low-to-high grade cartilaginous bone tumors and could prove a valuable aid in preoperative tumor characterization.</p>
Bacteria-Specific Features Selection for Enhanced Antimicrobial Peptide Activity Predictions Using Machine-Learning Methods
<p>We developed a new computational approach that allowed us to train several supervised machine-learning models using a specific set of data associated with peptides targeting E. coli bacteria. LASSO regression and Support Vector Machine techniques have been utilized to select, among more than 1500 physio-chemical descriptors, the most important features that can be used to classify a peptide as antimicrobial or ineffective against E. coli. We then performed the classification of active versus inactive AMPs using the Support Vector classifiers, Logistic Regression, and Random Forest methods. This computational study allows us to make recommendations of how to design more efficient anti-bacterial drug therapies.</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.