Mostrando 1 - 9 Resultados de 9 Para Buscar '"RFEC"', tiempo de consulta: 0.19s Limitar resultados
  1. 1
    “…We show that RFECS not only leads to more accurate and precise prediction of enhancers than previous methods, but also helps identify the most informative and robust set of three chromatin marks for enhancer prediction.…”
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    RFECS: A Random-Forest Based Algorithm for Enhancer Identification from Chromatin State
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    “…Remote-Field Eddy-Current (RFEC) technology is often used as a Non-Destructive Evaluation (NDE) method to prevent water pipe failures. …”
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    Defect Detection and Segmentation Framework for Remote Field Eddy Current Sensor Data
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    FEC91#13
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    “…Afterwards, the precorrected phenotypes were the dependent traits (rFEC-GIN, rFEC-FH, and rFLC-DV) in GWAS based on 423,654 SNPs from 148 DSN cows. …”
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    Genome-wide associations and functional gene analyses for endoparasite resistance in an endangered population of native German Black Pied cattle
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    “…Without the restriction of skin effect, remote field eddy current (RFEC) has great advantages in detecting buried depth defects. …”
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    Study on Remote Field Eddy Current Testing Technology for Crack-like Defects in Long Truss Structure of Aircraft
  6. 6
    “…We perform same-cell and cross-cell predictions to quantify the validation rate and compare against two state-of-the-art methods, DEEP-ENCODE and RFECS. We find that EP-DNN has superior accuracy with a validation rate of 91.6%, relative to 85.3% for DEEP-ENCODE and 85.5% for RFECS, for a given number of enhancer predictions and also scales better for a larger number of enhancer predictions. …”
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    EP-DNN: A Deep Neural Network-Based Global Enhancer Prediction Algorithm
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    “…We previously developed an algorithm RFECS to discover the most informative modifications associated with the classification or prediction of mammalian enhancers. …”
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    Distinct and Predictive Histone Lysine Acetylation Patterns at Promoters, Enhancers, and Gene Bodies
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    “…We perform EP-DNN predictions to quantify the validation rate for different levels of confidence in the predictions and also perform comparisons against two state-of-the-art computational models for enhancer predictions, DEEP-ENCODE and RFECS. RESULTS: We find that EP-DNN has superior accuracy and takes less time to make predictions. …”
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    Opening up the blackbox: an interpretable deep neural network-based classifier for cell-type specific enhancer predictions
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    “…Specifically, Aikyatan-CNN achieved 40% higher validation rate versus CSIANN and the same accuracy as RFECS. CONCLUSIONS: Our exhaustive experiments using an array of ML tools validate the need for a model that is not only expressive but can scale with increasing data volumes and diversity. …”
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    AIKYATAN: mapping distal regulatory elements using convolutional learning on GPU
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