AIMC Topic: Neoplasms

Clear Filters Showing 1201 to 1210 of 2356 articles

Systematic characterization of mutations altering protein degradation in human cancers.

Molecular cell
The ubiquitin-proteasome system (UPS) is the primary route for selective protein degradation in human cells. The UPS is an attractive target for novel cancer therapies, but the precise UPS genes and substrates important for cancer growth are incomple...

A novel gene selection method for gene expression data for the task of cancer type classification.

Biology direct
Cancer is a poligenetic disease with each cancer type having a different mutation profile. Genomic data can be utilized to detect these profiles and to diagnose and differentiate cancer types. Variant calling provide mutation information. Gene expres...

Optimizing ANFIS using simulated annealing algorithm for classification of microarray gene expression cancer data.

Medical & biological engineering & computing
In the medical field, successful classification of microarray gene expression data is of major importance for cancer diagnosis. However, due to the profusion of genes number, the performance of classifying DNA microarray gene expression data using st...

Investigating the relevance of major signaling pathways in cancer survival using a biologically meaningful deep learning model.

BMC bioinformatics
BACKGROUND: Survival analysis is an important part of cancer studies. In addition to the existing Cox proportional hazards model, deep learning models have recently been proposed in survival prediction, which directly integrates multi-omics data of a...

Aggregation of cohorts for histopathological diagnosis with deep morphological analysis.

Scientific reports
There have been substantial efforts in using deep learning (DL) to diagnose cancer from digital images of pathology slides. Existing algorithms typically operate by training deep neural networks either specialized in specific cohorts or an aggregate ...

Integrating Multi-Omic Data With Deep Subspace Fusion Clustering for Cancer Subtype Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
One type of cancer usually consists of several subtypes with distinct clinical implications, thus the cancer subtype prediction is an important task in disease diagnosis and therapy. Utilizing one type of data from molecular layers in biological syst...

CNAPE: A Machine Learning Method for Copy Number Alteration Prediction from Gene Expression.

IEEE/ACM transactions on computational biology and bioinformatics
Detection of DNA copy number alteration in cancer cells is critical to understanding cancer initiation and progression. Widely used methods, such as DNA arrays and genomic DNA sequencing, are relatively expensive and require DNA samples at a microgra...

Designing deep learning studies in cancer diagnostics.

Nature reviews. Cancer
The number of publications on deep learning for cancer diagnostics is rapidly increasing, and systems are frequently claimed to perform comparable with or better than clinicians. However, few systems have yet demonstrated real-world medical utility. ...

DeepMC: a deep learning method for efficient Monte Carlo beamlet dose calculation by predictive denoising in magnetic resonance-guided radiotherapy.

Physics in medicine and biology
Emerging magnetic resonance (MR) guided radiotherapy affords significantly improved anatomy visualization and, subsequently, more effective personalized treatment. The new therapy paradigm imposes significant demands on radiation dose calculation qua...