AIMC Topic: Deep Learning

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Rapid Mold Detection in Chinese Herbal Medicine Using Enhanced Deep Learning Technology.

Journal of medicinal food
Mold contamination poses a significant challenge in the processing and storage of Chinese herbal medicines (CHM), leading to quality degradation and reduced efficacy. To address this issue, we propose a rapid and accurate detection method for molds i...

TransfIGN: A Structure-Based Deep Learning Method for Modeling the Interaction between HLA-A*02:01 and Antigen Peptides.

Journal of chemical information and modeling
The intricate interaction between major histocompatibility complexes (MHCs) and antigen peptides with diverse amino acid sequences plays a pivotal role in immune responses and T cell activity. In recent years, deep learning (DL)-based models have eme...

A novel radiomics approach for predicting TACE outcomes in hepatocellular carcinoma patients using deep learning for multi-organ segmentation.

Scientific reports
Transarterial chemoembolization (TACE) represent the standard of therapy for non-operative hepatocellular carcinoma (HCC), while prediction of long term treatment outcomes is a complex and multifactorial task. In this study, we present a novel machin...

Fluorescence excitation-scanning hyperspectral imaging with scalable 2D-3D deep learning framework for colorectal cancer detection.

Scientific reports
Colorectal cancer is one of the top contributors to cancer-related deaths in the United States, with over 100,000 estimated cases in 2020 and over 50,000 deaths. The most common screening technique is minimally invasive colonoscopy using either refle...

Deep learning model integrating cfDNA methylation and fragment size profiles for lung cancer diagnosis.

Scientific reports
Detecting aberrant cell-free DNA (cfDNA) methylation is a promising strategy for lung cancer diagnosis. In this study, our aim is to identify methylation markers to distinguish patients with lung cancer from healthy individuals. Additionally, we soug...

Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning.

Nature communications
Imputation techniques provide means to replace missing measurements with a value and are used in almost all downstream analysis of mass spectrometry (MS) based proteomics data using label-free quantification (LFQ). Here we demonstrate how collaborati...

Accurate prediction of CDR-H3 loop structures of antibodies with deep learning.

eLife
Accurate prediction of the structurally diverse complementarity determining region heavy chain 3 (CDR-H3) loop structure remains a primary and long-standing challenge for antibody modeling. Here, we present the H3-OPT toolkit for predicting the 3D st...

Comparison of image quality between Deep learning image reconstruction and Iterative reconstruction technique for CT Brain- a pilot study.

F1000Research
BACKGROUND: Non-contrast Computed Tomography (NCCT) plays a pivotal role in assessing central nervous system disorders and is a crucial diagnostic method. Iterative reconstruction (IR) methods have enhanced image quality (IQ) but may result in a blot...

Molecular Classification of Breast Cancer Using Weakly Supervised Learning.

Cancer research and treatment
PURPOSE: The molecular classification of breast cancer is crucial for effective treatment. The emergence of digital pathology has ushered in a new era in which weakly supervised learning leveraging whole-slide images has gained prominence in developi...