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69 results for “feature selectivity”
Bożepole Małe 14, selection (feature 588)
Częściowa rekonstrukcja fragmentu naczynia ceramicznego (cykl łużycko-pomorski) z ob. 588 na stan. 14 w miejscowosci Bożepole Małe, woj. pomorskie. Zabytek z badań Archeobaltica (https://www.archeobaltica.pl/). Model bez skali. Partial reconstruction of Late Bronze/Early Iron Age (lausatian-pomeranian cycle) pottery vessel from feature 588. Archaeological site: Bożepole Małe 14, Pomerania, northern Poland. Excavation by Archeobaltica (https://www.archeobaltica.pl/). Model not in scale. Source: Objaverse 1.0 / Sketchfab
A method for increasing the robustness of stable feature selection for biomarker discovery in molecular medicine developed using serum small extracellular vesicle associated miRNAs and the Barrett’s o
GEO Series GSE227710. Homo sapiens. 51 samples. Type: Expression profiling by high throughput sequencing.
Function-selective targeting of the extracellular signal-regulated kinase (ERK1/2) mitigates multiple pathophysiological features of allergen-induced asthma in mice
GEO Series GSE198355. Mus musculus. 10 samples. Type: Expression profiling by high throughput sequencing.
A Local Asynchronous Distributed Privacy Preserving Feature Selection Algorithm for Large Peer-to-Peer Networks
In this paper we develop a local distributed privacy preserving algorithm for feature selection in a large peer-to-peer environment. Feature selection is often used in machine learning for data compaction and efficient learning by eliminating the curse of dimensionality. There exist many solutions for feature selection when the data is located at a central location. However, it becomes extremely challenging to perform the same when the data is distributed across a large number of peers or machines. Centralizing the entire dataset or portions of it can be very costly and impractical because of the large number of data sources, the asynchronous nature of the peer-to-peer networks, dynamic nature of the data/network and privacy concerns. The solution proposed in this paper allows us to perform feature selection in an asynchronous fashion with a low communication overhead where each peer can specify its own privacy constraints. The algorithm works based on local interactions among participating nodes. We present results on real-world datasets in order to performance of the proposed algorithm.
Origins of proprioceptor feature selectivity and topographic maps in the Drosophila leg
GEO Series GSE236232. Drosophila melanogaster. 1 samples. Type: Expression profiling by high throughput sequencing.
Feature selection in an interactive search-based PLA design approach
<p>The Product Line Architecture (PLA) is one of the most important artifacts of a Software Product Line (SPL). PLA design can be formulated as an interactive optimization problem with many conflicting factors. Incorporate Decision Makers’ (DM) preferences during the search process may help the algorithms to find more adequate solutions for their profiles. Interactive approaches allow the DM to evaluate solutions, guiding the optimization according to their preferences. However, this brings up human fatigue problems caused by the excessive amount of interactions and solutions to evaluate. A common strategy to prevent this problem is limiting the number of interactions and solutions evaluated by the DM. Machine Learning (ML) models were also used to learn how to evaluate solutions according to the DM profile and replace them after some interactions. Feature selection performs an essential task as non-relevant and/or redundant features used to train the ML model can reduce the accuracy and comprehensibility of the hypotheses induced by ML algorithms. This work aims to select features of a ML model used to prevent human fatigue in an interactive search-based PLA design approach. We applied four selectors and through results we were able to reduce 30% of features, obtaining an accuracy of 99%.</p>
Raw data for "Automatic Selection of Control Features for Electroencephalography-Based Brain-Computer Interface Assisted Motor Rehabilitation: The GUIDER Algorithm": unpublished figure and source data.
<p>Classification Performances for each stroke participant according to the GUIDER and the MANUAL procedure. </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>
Dataset for "A proposed SHCE feature selection method for estimating forest aboveground biomass from multiple satellite data"
<p>The data are associated with a submitted manuscript.</p>
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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.