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Full-Text Articles in Life Sciences
Therapeutic Potential Of Snake Venom: Toxin Distribution And Opportunities In Deep Learning For Novel Drug Discovery, Anas Bedraoui, Montamas Suntravat, Salim El Mejjad, Salwa Enezari, Naoual Oukkache, Elda E. Sanchez, Jacob Galan, Rachid El Fatimy, Tariq Daouda
Therapeutic Potential Of Snake Venom: Toxin Distribution And Opportunities In Deep Learning For Novel Drug Discovery, Anas Bedraoui, Montamas Suntravat, Salim El Mejjad, Salwa Enezari, Naoual Oukkache, Elda E. Sanchez, Jacob Galan, Rachid El Fatimy, Tariq Daouda
School of Medicine Publications and Presentations
Snake venom is a rich source of bioactive molecules that hold great promise for therapeutic applications. These molecules can be broadly classified into enzymes and non-enzymes, each showcasing unique medicinal properties. Noteworthy compounds such as Bradykinin Potentiating Peptides (BPP) and Three-Finger Toxins (3FTx) are showing therapeutic potential in areas like cardiovascular diseases (CVDs) and pain-relief. Meanwhile, components like snake venom metalloproteinases (SVMP), L-amino acid oxidases (LAAO), and Phospholipase A2s (PLA2) are paving new ways in oncology treatments. The full medicinal scope of these toxins is still emerging. In this review, we discuss drugs derived from snake venoms that address …
Machine Learning As A Tool For Early Detection: A Focus On Late-Stage Colorectal Cancer Across Socioeconomic Spectrums, Hadiza Galadima, Rexford Anson-Dwamena, Ashley Johnson, Ghalib Bello, Georges Adunlin, James Blando
Machine Learning As A Tool For Early Detection: A Focus On Late-Stage Colorectal Cancer Across Socioeconomic Spectrums, Hadiza Galadima, Rexford Anson-Dwamena, Ashley Johnson, Ghalib Bello, Georges Adunlin, James Blando
Community & Environmental Health Faculty Publications
Purpose: To assess the efficacy of various machine learning (ML) algorithms in predicting late-stage colorectal cancer (CRC) diagnoses against the backdrop of socio-economic and regional healthcare disparities. Methods: An innovative theoretical framework was developed to integrate individual- and census tract-level social determinants of health (SDOH) with sociodemographic factors. A comparative analysis of the ML models was conducted using key performance metrics such as AUC-ROC to evaluate their predictive accuracy. Spatio-temporal analysis was used to identify disparities in late-stage CRC diagnosis probabilities. Results: Gradient boosting emerged as the superior model, with the top predictors for late-stage CRC diagnosis being anatomic site, …