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

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

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

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

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

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

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

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

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

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

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

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

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

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Principle of the method. by 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. by Gursimran Singh (575288)

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BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data by 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 by 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.…”

Table_3_In silico identification and verification of ferroptosis-related genes in type 2 diabetic islets.xls by Meiqi Yin (694849)

“…Ferroptosis-related DE-mRNA functional and pathway enrichment analysis in T2D islet were performed. …”

Table_1_In silico identification and verification of ferroptosis-related genes in type 2 diabetic islets.xlsx by Meiqi Yin (694849)

“…Ferroptosis-related DE-mRNA functional and pathway enrichment analysis in T2D islet were performed. …”

Table_2_In silico identification and verification of ferroptosis-related genes in type 2 diabetic islets.xlsx by Meiqi Yin (694849)

“…Ferroptosis-related DE-mRNA functional and pathway enrichment analysis in T2D islet were performed. …”