Journal of chemical information and modeling
Jul 3, 2025
Accurate prediction of protein properties that influence cellular functions is crucial for drug design, disease research, and guiding biological wet-lab experiments. Previous methods primarily relied on physicochemical property analysis and homologou...
BACKGROUND: Accurately predicting synergistic drug combinations is critical for complex disease therapy. However, the vast search space of potential drug combinations poses significant challenges for identification through biological experiments alon...
This paper focuses on designing and developing novel architectures termed Hybrid Vision UNet-Encoder Decoder (HVU-ED) segmenter and Hybrid Vision UNet-Encoder (HVU-E) classifier for brain tumor segmentation and classification, respectively. The propo...
Viral infections have spread globally, profoundly affecting social and economic aspects of life and causing widespread suffering. Infection caused by the hepatitis B virus (HBV) is one of the significant global health challenges but can be effectivel...
Leukemia is the most prevalent form of blood cancer, affecting individuals across all age groups. Early and accurate diagnosis is crucial for effective treatment and improved clinical outcomes. Peripheral blood smear analysis, a key non-invasive diag...
Due to their similar clinical presentations, the scarcity of competent dermatologists, and the urgency of diagnosis, the accurate diagnosis of dermatological conditions such as Psoriasis and Lichen Planus is challenging. This study introduces a novel...
In the context of birdsong recognition, conventional modeling approaches often involve a significant number of parameters and high computational costs, rendering them unsuitable for deployment in embedded field monitoring devices. To improve the conv...
Humans and animals exhibit a remarkable ability to selectively filter out irrelevant information based on context. However, the neural mechanisms underlying this context-dependent selection process remain elusive. Recently, the issue of discriminatin...
Deep neural networks (DNNs) excel at extracting insights from complex data across various fields, however, their application in cognitive neuroscience remains limited, largely due to the lack of approaches with interpretability. Here, we employ two d...
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