AIMC Topic: Neoplasms

Clear Filters Showing 1421 to 1430 of 2356 articles

Machine-Learning Prediction of Tumor Antigen Immunogenicity in the Selection of Therapeutic Epitopes.

Cancer immunology research Sep 12, 2019
Current tumor neoantigen calling algorithms primarily rely on epitope/major histocompatibility complex (MHC) binding affinity predictions to rank and select for potential epitope targets. These algorithms do not predict for epitope immunogenicity usi...
Histocompatibility Antigens Class II T-Lymphocytes, Cytotoxic Humans Machine Learning Algorithms Computational Biology Cancer Vaccines Antigens, Neoplasm Peptide Fragments Neoplasms Histocompatibility Antigens Class I Epitopes, T-Lymphocyte Mice, Inbred BALB C Mice Mice, Inbred C57BL Female Animals Male
View on PubMed DOI

Artificial Intelligence in Radiation Oncology.

Hematology/oncology clinics of North America Sep 11, 2019
The integration of artificial intelligence in the radiation oncologist's workflow has multiple applications and significant potential. From the initial patient encounter, artificial intelligence may aid in pretreatment disease outcome and toxicity pr...
Quality Assurance, Health Care Radiotherapy Dosage Radiotherapy, Intensity-Modulated Neoplasms Radiation Oncology Radiotherapy Planning, Computer-Assisted Algorithms Humans Decision Support Techniques Artificial Intelligence Patient Safety
View on PubMed DOI

Facetto: Combining Unsupervised and Supervised Learning for Hierarchical Phenotype Analysis in Multi-Channel Image Data.

IEEE transactions on visualization and computer graphics Sep 10, 2019
Facetto is a scalable visual analytics application that is used to discover single-cell phenotypes in high-dimensional multi-channel microscopy images of human tumors and tissues. Such images represent the cutting edge of digital histology and promis...
Phenotype Neoplasms Systems Biology Cluster Analysis Image Interpretation, Computer-Assisted Neural Networks, Computer Machine Learning Software Humans
View on PubMed DOI

LDAPred: A Method Based on Information Flow Propagation and a Convolutional Neural Network for the Prediction of Disease-Associated lncRNAs.

International journal of molecular sciences Sep 10, 2019
Long non-coding RNAs (lncRNAs) play a crucial role in the pathogenesis and development of complex diseases. Predicting potential lncRNA-disease associations can improve our understanding of the molecular mechanisms of human diseases and help identify...
Sensitivity and Specificity Neoplasms Genetic Predisposition to Disease Neural Networks, Computer Algorithms Reproducibility of Results Computational Biology MicroRNAs RNA, Long Noncoding Humans
View on PubMed DOI

Designing combination therapies with modeling chaperoned machine learning.

PLoS computational biology Sep 9, 2019
Chemotherapy resistance is a major challenge to the effective treatment of cancer. Thus, a systematic pipeline for the efficient identification of effective combination treatments could bring huge biomedical benefit. In order to facilitate rational d...
Humans Machine Learning Algorithms Drug Therapy, Combination Signal Transduction TNF-Related Apoptosis-Inducing Ligand Neoplasms Models, Biological Drug Therapy, Computer-Assisted Antineoplastic Agents Cisplatin Computational Biology
View on PubMed DOI

Profiling of Exosomal Biomarkers for Accurate Cancer Identification: Combining DNA-PAINT with Machine- Learning-Based Classification.

Small (Weinheim an der Bergstrasse, Germany) Sep 3, 2019
Exosomes are endosome-derived vesicles enriched in body fluids such as urine, blood, and saliva. So far, they have been recognized as potential biomarkers for cancer diagnostics. However, the present single-variate analysis of exosomes has greatly li...
Humans Machine Learning Biomarkers, Tumor DNA Neoplasms Silicon Dioxide Cell Line, Tumor Exosomes Nanoparticles Nanotechnology
View on PubMed DOI

Automated Counting of Cancer Cells by Ensembling Deep Features.

Cells Sep 2, 2019
High-content and high-throughput digital microscopes have generated large image sets in biological experiments and clinical practice. Automatic image analysis techniques, such as cell counting, are in high demand. Here, cell counting was treated as a...
Image Processing, Computer-Assisted Cell Count Tumor Cells, Cultured Humans Neoplasms Deep Learning
View on PubMed DOI

CNNDLP: A Method Based on Convolutional Autoencoder and Convolutional Neural Network with Adjacent Edge Attention for Predicting lncRNA-Disease Associations.

International journal of molecular sciences Aug 30, 2019
It is well known that the unusual expression of long non-coding RNAs (lncRNAs) is closely related to the physiological and pathological processes of diseases. Therefore, inferring the potential lncRNA-disease associations are helpful for understandin...
Area Under Curve Humans Neoplasms Neural Networks, Computer ROC Curve Algorithms RNA, Long Noncoding
View on PubMed DOI

LncRNA-Disease Associations Prediction Using Bipartite Local Model With Nearest Profile-Based Association Inferring.

IEEE journal of biomedical and health informatics Aug 28, 2019
There is much evidence that long non-coding RNA (lncRNA) is associated with many diseases. However, it is time-consuming and expensive to identify meaningful lncRNA-disease associations (LDAs) through medical or biological experiments. Therefore, inv...
Models, Statistical Supervised Machine Learning Humans Neoplasms Genetic Predisposition to Disease Computational Biology RNA, Long Noncoding
View on PubMed DOI

Reverse active learning based atrous DenseNet for pathological image classification.

BMC bioinformatics Aug 28, 2019
BACKGROUND: Due to the recent advances in deep learning, this model attracted researchers who have applied it to medical image analysis. However, pathological image analysis based on deep learning networks faces a number of challenges, such as the hi...
Algorithms Reproducibility of Results Databases as Topic Humans Deep Learning Neoplasms Image Processing, Computer-Assisted Models, Theoretical
View on PubMed DOI
Popular Topics
Recent Journals