AIMC Topic: Deep Learning

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Wine discrimination based on multi-sensor fusion of GASF and Mel spectrogram features using an enhanced EfficientNet-B0 model.

Food chemistry
This study presents a novel multi-sensor fusion strategy for discriminating wines made from eight different raw materials using identical brewing processes. Aroma and taste signals were collected using a broad-spectrum electronic nose and noble metal...

Exploring the role of preprocessing combinations in hyperspectral imaging for deep learning colorectal cancer detection.

Scientific reports
This study compares various preprocessing techniques for hyperspectral deep learning-based cancer diagnostics. The study considers different spectrum scaling and noise reduction options across spatial and spectral axes of hyperspectral datacubes, as ...

Understanding Cancer Survivorship Care Needs Using Amazon Reviews: Content Analysis, Algorithm Development, and Validation Study.

JMIR cancer
BACKGROUND: Complementary therapies are being increasingly used by cancer survivors. As a channel for customers to share their feelings, outcomes, and perceived knowledge about the products purchased from e-commerce platforms, Amazon consumer reviews...

MRI detection and grading of knee osteoarthritis - a pilot study using an AI technique with a novel imaging-based scoring system.

Biomaterials science
Precise and rapid identification of knee osteoarthritis (OA) is essential for efficient management and therapy planning. Conventional diagnostic techniques frequently depend on subjective interpretation, which have shortcomings, particularly during t...

Sentiment analysis of classical Chinese literature: An unsupervised deep learning model with BERT and graph attention networks.

PloS one
Sentiment analysis has become a transformative technology in various contexts, particularly in Natural Language Processing (NLP), social media analytics, and literary analysis, as it can extract information from a wide range of texts. The advancement...

DeepExpDR: Drug Response Prediction through Molecular Topological Grouping and Substructure-Aware Expert.

Journal of chemical information and modeling
Cancer remains a major threat to human health. Tumor heterogeneity often leads to differences in tumor growth rate, invasion capacity, drug sensitivity, and prognosis, which complicates treatment strategies. Currently, drug responses are often verifi...

Deep Raman Quantitative Profiling and Augmented Features for Biologically Interpretable GI Cancer Detection.

Analytical chemistry
Early diagnosis of gastrointestinal (GI) cancer is critical. Raman spectroscopy combined with deep learning offers a noninvasive molecular quantification approach. This study developed a synergistic framework integrating Raman spectroscopy and convol...

GeneRAIN: multifaceted representation of genes via deep learning of gene expression networks.

Genome biology
We develop GeneRAIN, a suite of Transformer-based models that learn gene expression relationships from 410 K human bulk RNA-seq samples. Featuring a novel Binning-By-Gene normalization technique, our models capture diverse biological information beyo...

Deciphering the sequence basis and application of transcriptional initiation regulation in plant genomes through deep learning.

Genome biology
BACKGROUND: Transcription initiation is a key checkpoint in plant gene regulation, yet the DNA features that determine where and the frequency of the genes start transcription remain unclear.

Identification of type 2 diabetes- and obesity-associated human β-cells using deep transfer learning.

eLife
Diabetes affects >10% of adults worldwide and is caused by impaired production or response to insulin, resulting in chronic hyperglycemia. Pancreatic islet β-cells are the sole source of endogenous insulin, and our understanding of β-cell dysfunction...