Results 191 to 200 of about 180,358 (308)
Taking the neglected out of neglected tropical diseases [PDF]
openaire +3 more sources
Hand eczema: a ‘neglected’ disease [PDF]
P. Elsner, T. Agner
openaire +3 more sources
This study integrates random matrix theory (RMT) and principal component analysis (PCA) to improve the identification of correlated regions in HIV protein sequences for vaccine design. PCA validation enhances the reliability of RMT‐derived correlations, particularly in small‐sample, high‐dimensional datasets, enabling more accurate detection of ...
Mariyam Siddiqah +3 more
wiley +1 more source
Interventions for Neglected Diseases Caused by Kinetoplastid Parasites: A One Health Approach to Drug Discovery, Development, and Deployment. [PDF]
Ebiloma GU, Alhejeli A, de Koning HP.
europepmc +1 more source
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova +4 more
wiley +1 more source
Collaborative Synthesis for Neglected Diseases through the Open Synthesis Network: Structure-Activity Relationships of Arylaminopyrazoles as Chagas Disease Treatments. [PDF]
Abdulai Z +78 more
europepmc +1 more source
Cell Segmentation Beyond 2D—A Review of the State‐of‐the‐Art
Cell segmentation underpins many biological image analysis tasks, yet most deep learning methods remain limited to 2D despite the inherently 3D nature of cellular processes. This review surveys segmentation approaches beyond 2D, comparing 2.5D and fully 3D methods, analyzing 31 models and 32 volumetric datasets, and introducing a unified reference ...
Fabian Schmeisser +6 more
wiley +1 more source
Attention to the Registry of Neglected Diseases: Idiopathic Granulomatous Mastitis as an Example. [PDF]
Alipour S, Zafarghandi M, Group II.
europepmc +1 more source
scTIGER2.0 is a deep‐learning framework that infers gene regulatory networks from single‐cell RNA sequencing data. By integrating correlation, pseudotime ordering, deep learning and bootstrap‐based significance testing, it reduces false positives and reveals directional gene interactions.
Nishi Gupta +3 more
wiley +1 more source
AI-powered drug discovery for neglected diseases: accelerating public health solutions in the developing world. [PDF]
Nishan MDNH.
europepmc +1 more source

