AIMC Topic: Deep Learning

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An interpretable geometric graph neural network for enhancing the generalizability of drug-target interaction prediction.

BMC biology
BACKGROUND: Accurate prediction of drug-target interactions (DTIs) is essential for advancing drug discovery. Although numerous computational methods have been proposed, many exhibit limited generalization, particularly when dealing with unseen drugs...

Qualitative and quantitative assessment of accelerated liver diffusion-weighted imaging using deep-learning reconstruction in oncologic patients.

BMC medical imaging
BACKGROUND: Deep-learning (DL) reconstructions could improve image quality and reduce acquisition time in diffusion-weighted imaging (DWI). This study assessed, qualitatively and quantitatively, DL-DWI in liver metastasis of colorectal cancer patient...

Fusion_f5C-Pred: a dual-branch feature fusion framework for 5-formylcytosine modification sites prediction.

BMC genomics
BACKGROUND: 5-formylcytidine (f5C) is a unique post-transcriptional RNA modification present at the wobble position of mRNAs and tRNAs, that plays a critical role in mitochondrial protein synthesis and is potentially involved in translation regulatio...

Conditional deep learning model reveals translation elongation determinants during amino acid deprivation.

Communications biology
Translation elongation plays a key role in cellular homeostasis, and dysregulation of this process has been implicated in various diseases and metabolic disorders. Uncovering the causes of intragenic heterogeneity of translation, especially in contex...

LightMG-Net: an efficient lightweight deep neural network for multiclass grading of retinal detachment using handcrafted statistical mechanisms.

Scientific reports
Retinal detachment is a severely curable eye condition that becomes a genuine factor for the increased visual acuity worldwide. If neglected, it may result significant visual impairment in individuals aged 60 to 69 years. The successful cure percenta...

High-performance parallel multi-scale attention network with explainable AI for intelligent diagnosis of leaf diseases in agricultural systems.

Scientific reports
Detecting leaf diseases is crucial for ensuring crop health and boosting agricultural productivity. An advanced deep learning-based framework is introduced for cassava and groundnut leaf disease detection, incorporating a suite of innovative techniqu...

Human visual attention-inspired knowledge distillation underlying interpretable computational pathology.

Scientific reports
Computational pathology leverages advanced deep-learning techniques to analyze medical images with high resolution. However, a trade-off exists between model lightweight, interpretability, and task performance in such real-world scenarios. Knowledge ...

An autoencoder and vision transformer based interpretability analysis on the performance differences in automated staging of second and third molars.

Scientific reports
The practical adoption of deep learning in high-stakes forensic applications, such as dental age estimation, is often limited by the 'black box' nature of the models. This study introduces a framework designed to enhance both performance and transpar...

Novel dual-input stream-based hybrid approach for wheat leaf disease classification using edge-aware features.

Scientific reports
The prevalence of diseases in wheat crops poses a significant threat to global food security, as it reduces yield and quality. Addressing these challenges is critical for sustainable agriculture. This study proposes and evaluates a hybrid deep learni...

Transformer-based deep learning for adaptive pedagogy under uncertain student preferences.

Scientific reports
As educational environments become increasingly heterogeneous, conventional teaching strategies often fall short in accommodating the diverse and evolving learning behaviors of students, particularly when individual learning preferences are ambiguous...