Showing 1 - 9 results of 9 for search '(( binary data resource classification algorithm ) OR ( binary task dose optimization algorithm ))', query time: 0.61s Refine Results
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    MAGIC: A tool for predicting transcription factors and cofactors driving gene sets using ENCODE data by Avtar Roopra (140005)

    Published 2020
    “…ENCODE archives 2314 ChIP-seq tracks of 684 TFs and cofactors assayed across a 117 human cell lines under a multitude of growth and maintenance conditions. The algorithm presented herein, <b>M</b>ining <b>A</b>lgorithm for <b>G</b>enet<b>I</b>c <b>C</b>ontrollers (MAGIC), uses ENCODE ChIP-seq data to look for statistical enrichment of TFs and cofactors in gene bodies and flanking regions in gene lists without an <i>a priori</i> binary classification of genes as targets or non-targets. …”
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    Natural language processing for automated quantification of bone metastases reported in free-text bone scintigraphy reports by Olivier Q. Groot (9370461)

    Published 2020
    “…The aim of this study was to develop a natural language processing (NLP) algorithm for binary classification (single metastasis versus two or more metastases) in bone scintigraphy reports of patients undergoing surgery for bone metastases.…”
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    Table 1_Non-obtrusive monitoring of obstructive sleep apnea syndrome based on ballistocardiography: a preliminary study.docx by Biyong Zhang (20906192)

    Published 2025
    “…</p>Results<p>Cross-validated on 32 subjects, the proposed approach achieved an accuracy of 71.9% for four-class severity classification and 87.5% for binary classification (AHI less than 15 or not).…”
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    Machine Learning-Ready Dataset for Cytotoxicity Prediction of Metal Oxide Nanoparticles by Soham Savarkar (21811825)

    Published 2025
    “…</p><p dir="ltr">This curated dataset addresses several limitations of existing toxicological datasets by enhancing feature diversity, standardization, and data quality control. It is publicly available via the Supplementary Information section and aims to serve as a benchmark resource for researchers developing predictive nanotoxicology models.…”