Scoring schemes used to predict interfaces. حسب Chloé Dequeker (4966165)

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Number of putative partners predicted by CCD2PI. حسب Chloé Dequeker (4966165)

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Partner identification for proteins where the predicted and experimental interfaces do not match (F1-score < 0.2). حسب Chloé Dequeker (4966165)

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Properties of the known interacting pairs. حسب Chloé Dequeker (4966165)

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Assessment of CCD2PI on an independent dataset. حسب Chloé Dequeker (4966165)

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Predictive performance on the PPDBv2. حسب Chloé Dequeker (4966165)

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Predicted interaction matrices for the PPDBv2. حسب Chloé Dequeker (4966165)

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Main hyper-parameters of CCD2PI. حسب Chloé Dequeker (4966165)

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Comparison of cognate partners and competitors interfaces. حسب Chloé Dequeker (4966165)

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Detailed predictive performance for PPDBv2, when using the knowledge of the experimental interfaces. حسب Chloé Dequeker (4966165)

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Sensitivity of partner identification to approximations in the reference interfaces. حسب Chloé Dequeker (4966165)

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Examples of annotated and homology-transferred interactions. حسب Chloé Dequeker (4966165)

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Comparison with DPPI on PPDBv2. حسب Chloé Dequeker (4966165)

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Principle of the method. حسب Chloé Dequeker (4966165)

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PyNoetic’s classification module supports a range of popular and widely used ML classification models with just a single line of function call. حسب Gursimran Singh (575288)

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BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data حسب Jean-Christophe Lachance (6619307)

"…Despite its importance, no standardized computational platform is currently available to generate species-specific biomass objective functions in a data-driven, unbiased fashion. To fill this gap in the metabolic modeling software ecosystem, we implemented BOFdat, a Python package for the definition of a <b>B</b>iomass <b>O</b>bjective <b>F</b>unction from experimental <b>dat</b>a. …"

Python code for a rule-based NLP model for mapping circular economy indicators to SDGs حسب Zahir Barahmand (18008947)

"…The package includes:</p><ul><li>The complete Python codebase implementing the classification algorithm</li><li>A detailed manual outlining model features, requirements, and usage instructions</li><li>Sample input CSV files and corresponding processed output files to demonstrate functionality</li><li>Keyword dictionaries for all 17 SDGs, distinguishing strong and weak matches</li></ul><p dir="ltr">These materials enable full reproducibility of the study, facilitate adaptation for related research, and offer transparency in the methodological framework.…"

Excited-State DMRG Made Simple with FEAST حسب Alberto Baiardi (1914601)

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Excited-State DMRG Made Simple with FEAST حسب Alberto Baiardi (1914601)

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A flow diagram to explain the algorithm named 2BDP (Biomarker Discovery Process at Binomial Decision Point) that we used to find the best fitting model to predict RRiF. حسب Nabarun Chakraborty (535772)