AIMC Topic: Neural Networks, Computer

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A neural network to create super-resolution MR from multiple 2D brain scans of pediatric patients.

Medical physics
BACKGROUND: High-resolution (HR) 3D MR images provide detailed soft-tissue information that is useful in assessing long-term side-effects after treatment in childhood cancer survivors, such as morphological changes in brain structures. However, these...

Harnessing artificial neural networks to model caffeine degradation by High-Yield biodiesel algae Desmodesmus pannonicus.

Bioresource technology
In this study, Desmodesmus pannonicus IITISM-DIX2, outperforming Chlorella sorokiniana IITISM-DIX3 in caffeine degradation, was used to develop an artificial neural network (ANN) model for predicting caffeine removal efficiency under varying pH, phot...

MultiSCCHisto-Net-KD: A deep network for multi-organ explainable squamous cell carcinoma diagnosis with knowledge distillation.

Computers in biology and medicine
Squamous cell carcinoma is a prevalent cancer type that affects various organs in the human body. Manual analysis for detecting squamous cell carcinoma in histopathological images is time-consuming and may be subjective. Squamous cell carcinoma diagn...

Rapid diagnosis of bacterial vaginosis using machine-learning-assisted surface-enhanced Raman spectroscopy of human vaginal fluids.

mSystems
UNLABELLED: Bacterial vaginosis (BV) is an abnormal gynecological condition caused by the overgrowth of specific bacteria in the vagina. This study aims to develop a novel method for BV detection by integrating surface-enhanced Raman scattering (SERS...

Classification of acute myeloid leukemia by pre-trained deep neural networks: A comparison with different activation functions.

Medical engineering & physics
Acute Myeloid Leukemia(AML) is a rapidly progressing cancer affecting blood and bone marrow, marked by the swift proliferation of abnormal myeloid cells. Effective treatment requires precise classification of AML subtypes. Conventional classification...

Enhancing Thyroid Pathology With Artificial Intelligence: Automated Data Extraction From Electronic Health Reports Using RUBY.

JCO clinical cancer informatics
PURPOSE: Thyroid nodules are common in the general population, and assessing their malignancy risk is the initial step in care. Surgical exploration remains the sole definitive option for indeterminate nodules. Extensive database access is crucial fo...

Forecasting air pollution with deep learning with a focus on impact of urban traffic on PM10 and noise pollution.

PloS one
Air pollution constitutes a significant worldwide environmental challenge, presenting threats to both our well-being and the purity of our food supply. This study suggests employing Recurrent Neural Network (RNN) models featuring Long Short-Term Memo...

circ2DGNN: circRNA-Disease Association Prediction via Transformer-Based Graph Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics
Investigating the associations between circRNA and diseases is vital for comprehending the underlying mechanisms of diseases and formulating effective therapies. Computational prediction methods often rely solely on known circRNA-disease data, indire...

An End-to-End Knowledge Graph Fused Graph Neural Network for Accurate Protein-Protein Interactions Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Protein-protein interactions (PPIs) are essential to understanding cellular mechanisms, signaling networks, disease processes, and drug development, as they represent the physical contacts and functional associations between proteins. Recent advances...

LEC-Codec: Learning-Based Genome Data Compression.

IEEE/ACM transactions on computational biology and bioinformatics
In this paper, we propose a Learning-based gEnome Codec (LEC), which is designed for high efficiency and enhanced flexibility. The LEC integrates several advanced technologies, including Group of Bases (GoB) compression, multi-stride coding and bidir...