AIMC Topic: Neoplasms

Clear Filters Showing 1641 to 1650 of 2356 articles

Random Subspace Aggregation for Cancer Prediction with Gene Expression Profiles.

BioMed research international
. Precisely predicting cancer is crucial for cancer treatment. Gene expression profiles make it possible to analyze patterns between genes and cancers on the genome-wide scale. Gene expression data analysis, however, is confronted with enormous chall...

TNM-O: ontology support for staging of malignant tumours.

Journal of biomedical semantics
BACKGROUND: Objectives of this work are to (1) present an ontological framework for the TNM classification system, (2) exemplify this framework by an ontology for colon and rectum tumours, and (3) evaluate this ontology by assigning TNM classes to re...

Fuzzy-Rough Entropy Measure and Histogram Based Patient Selection for miRNA Ranking in Cancer.

IEEE/ACM transactions on computational biology and bioinformatics
MicroRNAs (miRNAs) are known as an important indicator of cancers. The presence of cancer can be detected by identifying the responsible miRNAs. A fuzzy-rough entropy measure (FREM) is developed which can rank the miRNAs and thereby identify the rele...

Investigation of serum Ki-67 as a biomarker in tumor-bearing dogs.

Research in veterinary science
Because of the limited number of tumor markers in veterinary medicine, there is need for identifying new markers. Ki-67 has been investigated as a tissue marker of malignant alterations. We hypothesized that Ki-67 would also be measurable in serum an...

Prediction of anti-cancer drug response by kernelized multi-task learning.

Artificial intelligence in medicine
MOTIVATION: Chemotherapy or targeted therapy are two of the main treatment options for many types of cancer. Due to the heterogeneous nature of cancer, the success of the therapeutic agents differs among patients. In this sense, determination of chem...

Predictive time-series modeling using artificial neural networks for Linac beam symmetry: an empirical study.

Annals of the New York Academy of Sciences
Over half of cancer patients receive radiotherapy (RT) as partial or full cancer treatment. Daily quality assurance (QA) of RT in cancer treatment closely monitors the performance of the medical linear accelerator (Linac) and is critical for continuo...

Unsupervised detection of cancer driver mutations with parsimony-guided learning.

Nature genetics
Methods are needed to reliably prioritize biologically active driver mutations over inactive passengers in high-throughput sequencing cancer data sets. We present ParsSNP, an unsupervised functional impact predictor that is guided by parsimony. ParsS...

Risk Assessment for Venous Thromboembolism in Chemotherapy-Treated Ambulatory Cancer Patients.

Medical decision making : an international journal of the Society for Medical Decision Making
OBJECTIVE: To design a precision medicine approach aimed at exploiting significant patterns in data, in order to produce venous thromboembolism (VTE) risk predictors for cancer outpatients that might be of advantage over the currently recommended mod...

DMAK: A curated pan-cancer DNA methylation annotation knowledgebase.

Bioengineered
Pan-cancer analysis can identify cell- and tissue-specific genomic loci and regions with underlying biological functions. Here we present an online curated DNA Methylation Annotation Knowledgebase, DMAK, which includes the pan-cancer analysis results...

Identification of informative genes and pathways using an improved penalized support vector machine with a weighting scheme.

Computers in biology and medicine
Incorporation of pathway knowledge into microarray analysis has brought better biological interpretation of the analysis outcome. However, most pathway data are manually curated without specific biological context. Non-informative genes could be incl...