Skin Cancer

Comprehensive single-cell RNA-seq analysis using deep interpretable generative modeling guided by biological hierarchy knowledge.

Recent advances in microfluidics and sequencing technologies allow researchers to explore cellular heterogeneity at single-cell resolution. In recent years, deep learning frameworks, such as generative models, have brought great changes to the analysis of transcriptomic data. Nevertheless, relying on the potential space of these generative models alone is insufficient to generate biological explan...

Machine Learning Links T-cell Function and Spatial Localization to Neoadjuvant Immunotherapy and Clinical Outcome in Pancreatic Cancer.

Tumor molecular data sets are becoming increasingly complex, making it nearly impossible for humans alone to effectively analyze them. Here, we demonstrate the power of using machine learning (ML) to analyze a single-cell, spatial, and highly multiplexed proteomic data set from human pancreatic cancer and reveal underlying biological mechanisms that may contribute to clinical outcomes. We designed...

PBAC: A pathway-based attention convolution neural network for predicting clinical drug treatment responses.

Precise and personalized drug application is crucial in the clinical treatment of complex diseases. Although neural networks offer a new approach to i...

Comprehensive analysis of clinical images contributions for melanoma classification using convolutional neural networks.

BACKGROUND: Timely diagnosis plays a critical role in determining melanoma prognosis, prompting the development of deep learning models to aid clinici...

Weakly Supervised Deep Learning Predicts Immunotherapy Response in Solid Tumors Based on PD-L1 Expression.

UNLABELLED: Programmed death-ligand 1 (PD-L1) IHC is the most commonly used biomarker for immunotherapy response. However, quantification of PD-L1 sta...

TEPCAM: Prediction of T-cell receptor-epitope binding specificity via interpretable deep learning.

The recognition of T-cell receptor (TCR) on the surface of T cell to specific epitope presented by the major histocompatibility complex is the key to ...

Artificial Intelligence in Lung Cancer Imaging: From Data to Therapy.

Lung cancer remains a global health challenge, leading to substantial morbidity and mortality. While prevention and early detection strategies have im...

Multiomics Analysis of Disulfidptosis Patterns and Integrated Machine Learning to Predict Immunotherapy Response in Lung Adenocarcinoma.

BACKGROUND: Recent studies have unveiled disulfidptosis as a phenomenon intimately associated with cellular damage, heralding new avenues for explorin...

Deep learning approach for skin melanoma and benign classification using empirical wavelet decomposition.

BACKGROUND: Melanoma is a malignant skin cancer that causes high mortality. Early detection of melanoma can save patients' lives. The features of the ...

DeepHLApan: A Deep Learning Approach for the Prediction of Peptide-HLA Binding and Immunogenicity.

Neoantigens are crucial in distinguishing cancer cells from normal ones and play a significant role in cancer immunotherapy. The field of bioinformati...

Insights into a Machine Learning-Based Palmitoylation-Related Gene Model for Predicting the Prognosis and Treatment Response of Breast Cancer Patients.

BACKGROUND: Breast cancer is a prevalent public health concern affecting numerous women globally and is associated with palmitoylation, a post-transla...

PD-1 Targeted Antibody Discovery Using AI Protein Diffusion.

The programmed cell death protein 1 (PD-1, CD279) is an important therapeutic target in many oncological diseases. This checkpoint protein inhibits T ...

Predicting Immune Checkpoint Inhibitor-Related Pneumonitis via Computed Tomography and Whole-Lung Analysis Deep Learning.

BACKGROUND: Immune checkpoint inhibitor-related pneumonitis (ICI-P) is a fatal adverse event of immunotherapy. However, there is a lack of methods to ...

[MOCK MOLE: PRODUCING SYNTHETIC IMAGES THAT RECAPITULATE CONFOCAL PATTERNS OF MELANOCYTIC NEVI VIA DEEP-LEARNING MODELS].

INTRODUCTION: Melanocytic nevi present microscopic patterns, which differ in their associated melanoma risk, and can be non-invasively recognized unde...

[A Case of Juvenile AFP-Producing Gastric Cancer with Virchow Lymph Node Metastasis Achieved Long-Term Survival with Multimodal Therapy].

A 25-year-old male received palliative total gastrectomy plus D1 dissection plus Roux-en-Y reconstruction for hemorrhagic gastric cancer with left Vir...

Skin Changes in Suspected Lyme Disease.

Dear Editor, Ticks carry many diseases, bacteria, and viruses and represent a very important healthcare issue both in Croatia and globally. Although m...

Unsupervised and supervised AI on molecular dynamics simulations reveals complex characteristics of HLA-A2-peptide immunogenicity.

Immunologic recognition of peptide antigens bound to class I major histocompatibility complex (MHC) molecules is essential to both novel immunotherape...

Automated detection of apoptotic bodies and cells in label-free time-lapse high-throughput video microscopy using deep convolutional neural networks.

MOTIVATION: Reliable label-free methods are needed for detecting and profiling apoptotic events in time-lapse cell-cell interaction assays. Prior stud...

Unveiling the power of convolutional neural networks in melanoma diagnosis.

Convolutional neural networks are a type of deep learning algorithm. They are mostly applied in visual recognition and can be used for the identificat...

MITNet: a fusion transformer and convolutional neural network architecture approach for T-cell epitope prediction.

Classifying epitopes is essential since they can be applied in various fields, including therapeutics, diagnostics and peptide-based vaccines. To dete...