AIMC Topic: Cell Line, Tumor

Clear Filters Showing 271 to 280 of 508 articles

Representation of features as images with neighborhood dependencies for compatibility with convolutional neural networks.

Nature communications Sep 1, 2020
Deep learning with Convolutional Neural Networks has shown great promise in image-based classification and enhancement but is often unsuitable for predictive modeling using features without spatial correlations. We present a feature representation ap...
Neoplasms Antineoplastic Agents Gene Expression Profiling Deep Learning Humans Biomarkers, Tumor Bayes Theorem High-Throughput Nucleotide Sequencing Datasets as Topic Drug Resistance, Neoplasm Oligonucleotide Array Sequence Analysis Cell Line, Tumor Image Processing, Computer-Assisted Cell Proliferation Drug Screening Assays, Antitumor
View on PubMed DOI

Deep Learning Modeling of Androgen Receptor Responses to Prostate Cancer Therapies.

International journal of molecular sciences Aug 14, 2020
Gain-of-function mutations in human androgen receptor (AR) are among the major causes of drug resistance in prostate cancer (PCa). Identifying mutations that cause resistant phenotype is of critical importance for guiding treatment protocols, as well...
Transcription, Genetic Androgen Receptor Antagonists Humans Neural Networks, Computer Male Deep Learning Cell Line, Tumor ROC Curve Prostatic Neoplasms Mutation Receptors, Androgen
View on PubMed DOI

Synergistic drug combinations and machine learning for drug repurposing in chordoma.

Scientific reports Jul 31, 2020
Chordoma is a devastating rare cancer that affects one in a million people. With a mean-survival of just 6 years and no approved medicines, the primary treatments are surgery and radiation. In order to speed new medicines to chordoma patients, a drug...
Morpholines Chordoma Drug Repositioning Piperazines Pyrimidines Benzamides Cell Line, Tumor Antineoplastic Combined Chemotherapy Protocols Neoplasm Proteins Pyridines Humans Machine Learning
View on PubMed DOI

Molecular docking and machine learning analysis of Abemaciclib in colon cancer.

BMC molecular and cell biology Jul 8, 2020
BACKGROUND: The main challenge in cancer research is the identification of different omic variables that present a prognostic value and personalised diagnosis for each tumour. The fact that the diagnosis is personalised opens the doors to the design ...
Humans Machine Learning Cell Line, Tumor Gene Expression Regulation, Neoplastic Adenocarcinoma Databases, Protein Drug Repositioning Algorithms Prognosis Molecular Docking Simulation Gene Expression Profiling Fatty Acid-Binding Proteins Gastrointestinal Hormones Epistasis, Genetic Aminopyridines Neoplasm Staging Colonic Neoplasms Survival Analysis Benzimidazoles
View on PubMed DOI

Image based cellular contractile force evaluation with small-world network inspired CNN: SW-UNet.

Biochemical and biophysical research communications Jul 7, 2020
We propose an image based cellular contractile force evaluation method using a machine learning technique. We use a special substrate that exhibits wrinkles when cells grab the substrate and contract, and the wrinkles can be used to visualize the for...
Microscopy Proto-Oncogene Proteins p21(ras) Image Processing, Computer-Assisted Osteosarcoma Bone Neoplasms Mutation Humans Neural Networks, Computer Machine Learning Algorithms Biomechanical Phenomena Cell Line, Tumor
View on PubMed DOI

Artificial intelligence-based collaborative filtering method with ensemble learning for personalized lung cancer medicine without genetic sequencing.

Pharmacological research Jun 23, 2020
In personalized medicine, many factors influence the choice of compounds. Hence, the selection of suitable medicine for patients with non-small-cell lung cancer (NSCLC) is expensive. To shorten the decision-making process for compounds, we propose a ...
Humans Artificial Intelligence Algorithms Databases, Factual Clinical Decision-Making Animals Apoptosis Antineoplastic Agents Computer Simulation Cost-Benefit Analysis Precision Medicine Cell Line, Tumor Lung Neoplasms Carcinoma, Non-Small-Cell Lung Cell Movement
View on PubMed DOI

Matrix factorization with neural network for predicting circRNA-RBP interactions.

BMC bioinformatics Jun 5, 2020
BACKGROUND: Circular RNA (circRNA) has been extensively identified in cells and tissues, and plays crucial roles in human diseases and biological processes. circRNA could act as dynamic scaffolding molecules that modulate protein-protein interactions...
Databases, Factual Neoplasms Cell Line, Tumor ROC Curve Humans RNA-Binding Proteins RNA, Circular Neural Networks, Computer Area Under Curve
View on PubMed DOI

Beyond the limitation of targeted therapy: Improve the application of targeted drugs combining genomic data with machine learning.

Pharmacological research May 28, 2020
Precision oncology involves effectively selecting drugs for cancer patients and planning an effective treatment regimen. However, for Molecular targeted drug, using genomic state of the drug target to select drugs has limitations. Many patients who c...
Cell Line, Tumor Genomics Gene Expression Regulation, Neoplastic Signal Transduction Biomarkers, Tumor Neoplasms Antineoplastic Agents Pharmacogenetics Databases, Genetic Models, Statistical Drug Screening Assays, Antitumor Molecular Targeted Therapy Humans Machine Learning Decision Support Techniques Precision Medicine Clinical Decision-Making
View on PubMed DOI

Convolutional Neural Network Can Recognize Drug Resistance of Single Cancer Cells.

International journal of molecular sciences Apr 30, 2020
It is known that single or isolated tumor cells enter cancer patients' circulatory systems. These circulating tumor cells (CTCs) are thought to be an effective tool for diagnosing cancer malignancy. However, handling CTC samples and evaluating CTC se...
Cell Line, Tumor Precision Medicine Single-Cell Analysis Humans Neoplasms Drug Resistance, Neoplasm Neural Networks, Computer Antineoplastic Agents Machine Learning Deep Learning Cells, Cultured
View on PubMed DOI

Deep Learning of Spatiotemporal Filtering for Fast Super-Resolution Ultrasound Imaging.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control Apr 15, 2020
Super-resolution ultrasound (SR-US) imaging is a new technique that breaks the diffraction limit and allows visualization of microvascular structures down to tens of micrometers. The image processing methods for the spatiotemporal filtering needed in...
Mice, Nude Neural Networks, Computer Mammary Neoplasms, Experimental Female Signal Processing, Computer-Assisted Animals Cell Line, Tumor Microbubbles Deep Learning Image Processing, Computer-Assisted Ultrasonography Mice Contrast Media
View on PubMed DOI
Popular Topics
Recent Journals