Search Results - cancer+target

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Subgraph Matching for High-Throughput DNA-Aptamer Secondary Structure Classification and Machine Learning Interpretability (Case No. 2025-104)
Intro Sentence: UCLA researchers in the Department of Mathematics have developed machine learning methods to rapidly identify novel aptamer sequences for target binding to accelerate highly-accurate diagnostic and therapeutic development. Background: Aptamers are single-stranded nucleotide polymers that bind with high affinity to targets such as...
Published: 7/22/2025   |   Inventor(s): Andrea Bertozzi, Anne Andrews, Matthew Tyler, Paolo Climaco, Noelle Mitchell
Keywords(s): Advanced Computing / AI, advanced computing methods, Aptamers, Artifical Intelligence (Machine Learning, Data Mining), artificial intelligence/machine learning models, bioinformatics pipeline, cancer target, clustering, computational efficiency, computational efficiency and analysis, design software, DNA clustering, DNA oligomer, DNA Sequencing, Drug, Drug Delivery, Drug Development, Drug Discovery, drug screening, high throughput, high throughput assays, high throughput testing, high-throughput analysis, High-Throughput Screening, interpretability, pipeline, large-scale parallelization, Machine Learning, machine learning modeling, motif structures, open source, open source code, OpenAI, Pharmaceutical Drug, protein classification, secondary structure, SELEX, sequences of interest, single strand DNA sequences, Software, Software & Algorithms, Software Development Tools, Software-enabled learning, subgraph matching, target binding, target detection, Targeted Therapy, Targets And Assays, tissue targeting accuracy
Category(s): Software & Algorithms, Software & Algorithms > AI Algorithms, Software & Algorithms > Artificial Intelligence & Machine Learning, Software & Algorithms > Data Analytics, Life Science Research Tools, Life Science Research Tools > Research Methods, Life Science Research Tools > Screening Libraries, Platforms, Platforms > Drug Delivery, Diagnostic Markers > Targets And Assays, Diagnostic Markers, Software & Algorithms > Bioinformatics
Full Spectrum Computer Vision for Photon Counting CT (Case No. 2024-058)
Summary: Researchers in the Department of Radiological Sciences have developed a machine learning algorithm that processes multispectral photon counting CT data for accurate medical imaging. Background: Photon counting computed tomography (PCCT) is a tremendous engineering advancement, enabling high resolution spectral imaging with myriad applications....
Published: 7/29/2025   |   Inventor(s): Matthew Brown, Dieter Enzmann, John Hoffman, Michael Mcnitt-Gray
Keywords(s): AI algorithms, Algorithm, Algorithm Optical Coherence Tomography, algorithmic cancer detection, Artifical Intelligence (Machine Learning, Data Mining), artificial electromagnetic materials, Artificial Intelligence, artificial intelligence augmentation, Artificial Neural Network, Artificial Neural Network Artificial Neuron, artificial-intelligent materials, Big Data, Bladder Cancer, blood cancers, Brain cancer, Breast Cancer, Cancer, cancer antigen, cancer detection, Cancer Immunotherapy, Cancer stem cells, cancer target, Computed tomography, CT, Deep Learning, design software, Digital Pathology, generative artificial intelligence, Genetic Algorithm, Histopathological image analysis, Histopathology, histopathology images, hyperparameter optimization, Image Analysis, Image Processing, lympathic cancers, lymphatic cancer, Medical artificial intelligence (AI), Mesenchymal Stem Cell Derived Cancer Cells, Orthotopic cancer models, Pancreatic cancer, pathology image analysis, Photon counting computed tomography (PCCT), prostate cancer, Radiology, Radiology / Radiomitigation, Software, Software & Algorithms, Software Development Tools, Software-enabled learning
Category(s): Software & Algorithms, Software & Algorithms > Image Processing, Software & Algorithms > Artificial Intelligence & Machine Learning, Software & Algorithms > Data Analytics
Integrated Molecular and Lipidomic Analysis of Glioma Tumors Identifies Therapeutic Vulnerabilities (UCLA Case No. 2023-210)
UCLA researchers in the Department of Molecular and Medical Pharmacology have uncovered a novel therapeutic target for Glioblastoma leveraging an extensive lipidomic and transcriptomic database. BACKGROUND: Glioblastoma (GBM) is a fast-growing and aggressive brain tumor. The National Brain Tumor Society predicted that over 14,000 people in the United...
Published: 7/17/2025   |   Inventor(s): David Nathanson
Keywords(s): Brain cancer, Brain Tumor, cancer target, cell death, ferroptosis, Glioblastoma, lipidomics
Category(s): Therapeutics > Oncology