AIMC Topic: Programmed Cell Death 1 Receptor
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A transformer-based deep learning survival prediction model and an explainable XGBoost anti-PD-1/PD-L1 outcome prediction model based on the cGAS-STING-centered pathways in hepatocellular carcinoma.
Briefings in bioinformatics
Nov 22, 2024
Recent studies suggest cGAS-STING pathway may play a crucial role in the genesis and development of hepatocellular carcinoma (HCC), closely associated with classical pathways and tumor immunity. We aimed to develop models predicting survival and anti...
A preliminary study on the diagnostic performance of the uPath PD-L1 (SP263) artificial intelligence (AI) algorithm in patients with NSCLC treated with PD-1/PD-L1 checkpoint blockade.
Pathologica
Aug 1, 2024
OBJECTIVE: The uPath PD-L1 (SP263) is an AI-based platform designed to aid pathologists in identifying and quantifying PD-L1 positive tumor cells in non-small cell lung cancer (NSCLC) samples stained with the SP263 assay.
Characterization of unique pattern of immune cell profile in patients with nasopharyngeal carcinoma through flow cytometry and machine learning.
Journal of cellular and molecular medicine
Jun 1, 2024
In patients with nasopharyngeal carcinoma (NPC), the alteration of immune responses in peripheral blood remains unclear. In this study, we established an immune cell profile for patients with NPC and used flow cytometry and machine learning (ML) to i...
Machine learning and radiomics for predicting efficacy of programmed cell death protein 1 inhibitor for small cell lung cancer: A multicenter cohort study.
Clinical and translational medicine
Jun 1, 2024
PD-1 Targeted Antibody Discovery Using AI Protein Diffusion.
Technology in cancer research & treatment
Jan 1, 2024
The programmed cell death protein 1 (PD-1, CD279) is an important therapeutic target in many oncological diseases. This checkpoint protein inhibits T lymphocytes from attacking other cells in the body and thus blocking it improves the clearance of tu...
Predicting antibody binders and generating synthetic antibodies using deep learning.
mAbs
Jan 1, 2022
The antibody drug field has continually sought improvements to methods for candidate discovery and engineering. Historically, most such methods have been laboratory-based, but informatics methods have recently started to make an impact. Deep learning...
Machine learning revealed stemness features and a novel stemness-based classification with appealing implications in discriminating the prognosis, immunotherapy and temozolomide responses of 906 glioblastoma patients.
Briefings in bioinformatics
Sep 2, 2021
Glioblastoma (GBM) is the most malignant and lethal intracranial tumor, with extremely limited treatment options. Immunotherapy has been widely studied in GBM, but none can significantly prolong the overall survival (OS) of patients without selection...
Atlases as Topic
Programmed Cell Death 1 Receptor
Survival Analysis
Temozolomide
Antineoplastic Agents, Alkylating
Middle Aged
Gene Expression Profiling
Transcriptome
Immunotherapy
Gene Expression Regulation, Neoplastic
Treatment Outcome
Neoplastic Stem Cells
Glioblastoma
Patient Selection
Aged
Adult
Tumor Microenvironment
Brain Neoplasms
Male
Humans
Machine Learning
Prognosis
Female
Artificial Intelligence Estimates the Importance of Baseline Factors in Predicting Response to Anti-PD1 in Metastatic Melanoma.
American journal of clinical oncology
Aug 1, 2019
OBJECTIVE: Prognosis of patients with metastatic melanoma has dramatically improved over recent years because of the advent of antibodies targeting programmed cell death protein-1 (PD1). However, the response rate is ~40% and baseline biomarkers for ...
Humans
Neural Networks, Computer
Treatment Outcome
Neutrophils
Age Factors
Mucous Membrane
Programmed Cell Death 1 Receptor
L-Lactate Dehydrogenase
Lymphocyte Count
Neoplasms, Unknown Primary
Uveal Neoplasms
Skin Neoplasms
Aged
Survival Rate
Male
Middle Aged
Forecasting
Retrospective Studies
Female
Melanoma
Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers.
Annals of oncology : official journal of the European Society for Medical Oncology
Jun 1, 2019
INTRODUCTION: Immunotherapy is regarded as one of the major breakthroughs in cancer treatment. Despite its success, only a subset of patients responds-urging the quest for predictive biomarkers. We hypothesize that artificial intelligence (AI) algori...
Algorithms
Prognosis
Tomography, X-Ray Computed
Melanoma
Survival Rate
Humans
Artificial Intelligence
Machine Learning
Follow-Up Studies
Predictive Value of Tests
Programmed Cell Death 1 Receptor
Lung Neoplasms
Immunotherapy
Antineoplastic Combined Chemotherapy Protocols
Carcinoma, Non-Small-Cell Lung
Machine learning-based predictors for immune checkpoint inhibitor therapy of non-small-cell lung cancer.
Annals of oncology : official journal of the European Society for Medical Oncology
Apr 1, 2019
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