AIMC Topic: Deep Learning

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DiffRaman: A conditional latent denoising diffusion probabilistic model for enhancing bacterial identification via Raman spectra generation under limited data.

Analytica chimica acta
Raman spectroscopy has attracted significant attention in various biochemical detection fields, especially in the rapid identification of pathogenic bacteria. The integration of this technology with deep learning to facilitate automated bacterial Ram...

Semi-supervised motion flow and myocardial strain estimation in cardiac videos using distance maps and memory networks.

Computers in biology and medicine
Myocardial strain plays a crucial role in diagnosing heart failure and myocardial infarction. Its computation relies on assessing heart muscle motion throughout the cardiac cycle. This assessment can be performed by following key points on each frame...

Prediction of Fraction Unbound in Human Plasma for Per- and Polyfluoroalkyl Substances: Evaluating Transfer Learning as an Algorithmic Solution to the Problem of Sparse Data.

Journal of chemical information and modeling
Fraction unbound in plasma () is a crucial parameter in physiologically based toxicokinetic (PBTK) models, representing the fraction of a chemical compound that is not sequestered by plasma proteins when present in the bloodstream. This is often used...

m5U-HybridNet: Integrating an RNA Foundation Model with CNN Features for Accurate Prediction of 5-Methyluridine Modification Sites.

Journal of chemical information and modeling
The 5-methyluridine (m5U) modification in RNA is vital for numerous biological processes, making its precise identification a key focus in computational biology. However, traditional wet-lab detection methods are cumbersome and time-consuming, wherea...

Deep learning algorithm-assisted non-destructive detection of TBARS values of salmon flesh using multi-modal molecular spectra fusion.

Food chemistry
This study presents a deep learning framework for the non-destructive assessment of lipid oxidation in salmon flesh, quantified by thiobarbituric acid reactive substances (TBARS), under diverse storage conditions (-20, 0, 4, 20 °C, and dynamic temper...

Predicting wheat yield using deep learning and multi-source environmental data.

Scientific reports
Accurate forecasting of crop yields is essential for ensuring food security and promoting sustainable agricultural practices. Winter wheat, a key staple crop in Pakistan, faces challenges in yield prediction because of the complex interactions among ...

BPFun: a deep learning framework for bioactive peptide function prediction using multi-label strategy by transformer-driven and sequence rich intrinsic information.

BMC bioinformatics
Bioactive peptides are beneficial or have physiological effects on the life activities of biological organisms. The functions of bioactive peptides are diverse, usually with one or more, so accurately detecting the multiple functions of multi-functio...

Establishment of AI-assisted diagnosis of the infraorbital posterior ethmoid cells based on deep learning.

BMC medical imaging
OBJECTIVE: To construct an artificial intelligence (AI)-assisted model for identifying the infraorbital posterior ethmoid cells (IPECs) based on deep learning using sagittal CT images.

A deep ensemble framework for human essential gene prediction by integrating multi-omics data.

Scientific reports
Essential genes are necessary for the survival or reproduction of a living organism. The prediction and analysis of gene essentiality can advance our understanding of basic life and human diseases, and further boost the development of new drugs. We p...

A novel framework GRCornShot for corn disease detection using few shot learning with prototypical network.

Scientific reports
Precision and timeliness in the detection of plant diseases are important to limit crop losses and maintain global food security. Much work has been performed to detect plant diseases using deep learning methods. However, deep learning techniques dem...