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96 results for “Machine learning algorithms”
INFLAMeR: a machine learning algorithm based on large-scale perturbation screening identified new lncRNAs regulating differentiation and survival of leukaemia cells
<p><strong>Abstract</strong></p> <p>Long non-coding RNAs (lncRNAs) are a diverse group of transcripts with poorly understood<br> functionality. To address this gap, we developed INFLAMeR, an advanced machine learning<br> model trained on CRISPRi screening data, to predict functional lncRNAs using comprehensive<br> genetic features. We experimentally validated the predictions by assessing their impact on cell<br> proliferation and anticancer drug resistance. Among the selected lncRNAs, 85% showed<br> significant effects upon knockdown, while low-scoring lncRNAs had no discernible impact.<br> Notably, our study elucidated the functional role of SNHG6 in hematopoietic differentiation.<br> INFLAMeR greatly enhances the prediction of functional lncRNAs, providing valuable insights<br> into their regulatory landscape. By integrating INFLAMeR with experimental validation, we can<br> identify and characterize functional lncRNAs in a cell-type-specific manner, contributing to a<br> deeper understanding of their involvement in cellular processes. Our findings revealed insights<br> into lncRNA biology and a framework for improving the identification of functional lncRNAs.</p>
A Feasibility Study to Improve Colorectal Cancer Screening Among Racially Diverse Zip Codes in a Persistent Poverty County Using Navigation and Machine Learning Predictive Algorithms
ClinicalTrials.gov study NCT05383976. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Validation of Machine Learning Based Personalized Nutrition Algorithm to Reduce Postprandial Glycemic Excursions Among North American Individuals With Newly Diagnosed Type 2 Diabetes
ClinicalTrials.gov study NCT03053518. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Safety and Feasibility of a Machine-Learning Bolus Priming Added to Existing Control Algorithm
ClinicalTrials.gov study NCT06728059. IPD Sharing: YES. Countries: 1. Publications: 0.
RCT of Sepsis Machine Learning Algorithm
ClinicalTrials.gov study NCT03882476. IPD Sharing: Not stated. Countries: 0. Publications: 5.
Identification of immune-related genes and small-molecule drugs in hypertension-induced left ventricular hypertrophy based on machine learning algorithms and molecular docking
GEO Series GSE261273. Mus musculus. 24 samples. Type: Expression profiling by high throughput sequencing.
Accurate age prediction from blood using a small set of DNA methylation sites and a cohort-based machine learning algorithm
GEO Series GSE207605. Homo sapiens. 0 samples. Type: Methylation profiling by array; Third-party reanalysis.
Finite element data collected and Machine learning algorithms to predict the mechanical properties of innovative CLT
<p>This folder includes the data collected from the finite element simulations of the innovative CLT to compute its mechanical properties, the error of the closed-form solutions predicting the bending stiffness in the minor direction D22, the variation of the distance between the Reissner Mindlin and Bending Gradient theory in terms of spacing between lateral lamellas, the hyperparameters tuning of several ML algorithms (Regression Tree, Random Forest, Gradient Boosting and Artificial Neural Network), the ML evaluations, the saved artificial neural network algorithms to predict each mechanical property of innovative CLT, and the ML application to use it.</p>
Large-scale comparison of machine learning algorithms for target prediction of natural products
<p>Supplement Materials of the article named "Large-scale comparison of machine learning algorithms for target prediction of natural products".</p>
Open data for "Predicting dominant terrestrial biomes at a Global Scale: Assessments of machine learning algorithms, climate variables indexing, and extreme climate"
<p>______________________________________________________<br> This page contains public-domain data required to reconstruct simulation results in the manuscript "Predicting dominant terrestrial biomes at a Global Scale: Assessments of machine learning algorithms, climate variables indexing, and extreme climate," submitted by the following author.</p> <p>Author: Hisashi SATO (JAMSTEC) <br> email : hsatoscb_(at)_gmail.com</p> <p>______________________________________________________<br> 1. Folder "Code"<br> Detailed descriptions are available on the code. </p> <p>1-1. MachineLearningComparison.R<br> Machine learning programs using random forest (RF), naive Bayes classifier (NV), and support vector machine (SVM) algorithms.</p> <p>1-2. Analyse_MapSimilarity.R<br> Calculate coincidences of simulated potential natural vegetation (PNV) maps simulated by different models.</p> <p>1-3. Visualize_VCE.R<br> Generating VCE (Visualize Climate Image) for training CNN models.</p> <p>1-4. Visualize_Maps.R<br> Visualizing global PNV maps.</p> <p>1-5. Visualize_ClimateHistgrams.R<br> Visualizing histograms of climate datasets.</p> <p>______________________________________________________<br> 2. Folder "Input"</p> <p>2-1. Unified_BIOCLIM_WorldClim.csv<br> Input data for the current climate.<br> This file contains the following variables.<br> lon Longitude at the center of the grid<br> lat Latitude at the center of the grid<br> bio1~19 Average climate indices from BIOCLIM (AveI)<br> CDD~WSDI Extreme climate indices (CEI)<br> c1~c16 Fraction of PNV from MODIS data<br> tavg01~tavg12 Monthly mean air temperature from January to December (Ave)<br> prec01~prec12 Monthly precipitation from January to December (Ave)</p> <p>2-2. Unified_BIOCLIM_WorldClimFutureRCP85.csv<br> Input data for future climate (@RCP8.5)<br> Including variables are the same as Unified_BIOCLIM_WorldClim.csv</p> <p>2-3. BIOCLIM_RefNo.csv<br> This CSV file contains the following information for each grid.<br> lat: Latitude at the center of the grid<br> lon: Longitude at the center of the grid<br> latNo: Latitude number corresponding to the image file name<br> lonNo: Longitude number corresponding to the image file name<br> lineNo: No use. Don't mind.<br> vegNo: Most dominant PNV based on the Unified_BIOCLIM_WorldClim.csv</p> <p>______________________________________________________<br> 3. Folder "Output"</p> <p>3-1. PNV_sim<br> 3-2. PNV_sim_RCP85.csv<br> Current and future PNV maps from various models. These files are the main output files from the code MachineLearningComparison.R. For PNV maps from CNN models (m4p1~6) were supplemented. Detailed methods to build CNN models, please refer to the following manuscript.<br> Sato, H. & T. Ise (2022). "Predicting global terrestrial biomes with the LeNet convolutional neural network." Geoscientific Model Development 15(7): 3121-3132.</p> <p>Labels indicate combinations of machine-learning-algorithm and dataset for training the model. For example, In case of "m1p1", that column shows the simulation result of models trained with randomForest (RF) algorithm and Ave dataset.<br> m1: randomForest (RF)<br> m2: Support vector machine (SVM)<br> m3: Naive Bayes (NB)<br> m4: Convolutional Neural Network (CNN), which is NOT analysed in this code<br> p1: Ave<br> p2: Ave + CEI<br> p3: Ave + CEIpart<br> p4: AveI <br> p5: AveI + CEI<br> p6: AveI + CEIpart</p>
Use of Machine-learning Algorithms, Biomarkers and Measures of Quality of Life to Personalize Medical Management of Liver and Heart Transplant Recipients
ClinicalTrials.gov study NCT06774768. IPD Sharing: Not stated. 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.
Assess the Utility of a Speech-based Machine Learning Algorithm to Predict Treatment Response to Psychiatric Interventions
ClinicalTrials.gov study NCT06823024. IPD Sharing: UNDECIDED. 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.
With the Development of Research, New Algorithms and Technologies Have Emerged, One of Which is Machine Learning. Machine Learning Can Extract Key Factors From Vast Amounts of Data, Identify Underlyin
ClinicalTrials.gov study NCT07121309. IPD Sharing: NO. Countries: 1. Publications: 0.
Identification of Important Symptoms and Diagnostic Hypothyroidism Patients Using Machine Learning Algorithms
ClinicalTrials.gov study NCT06112886. IPD Sharing: YES. Countries: 1. Publications: 0.
inContAlert: Machine Learning Algorithms for Individual Bladder Filling Level Prediction
ClinicalTrials.gov study NCT05952700. IPD Sharing: NO. Countries: 1. Publications: 0.
Heuristics, Algorithms and Machine Learning: Evaluation & Testing in Radiation Therapy
ClinicalTrials.gov study NCT04060706. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Machine Learning Algorithm for Predicting Postoperative Delirium in Elderly Patients After Thoracic Surgery
ClinicalTrials.gov study NCT06226480. IPD Sharing: NO. 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.
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.