AIMC Topic: Neoplasm Proteins

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Loss of the integral nuclear envelope protein SUN1 induces alteration of nucleoli.

Nucleus (Austin, Tex.)
A supervised machine learning algorithm, which is qualified for image classification and analyzing similarities, is based on multiple discriminative morphological features that are automatically assembled during the learning processes. The algorithm ...

Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory.

BMC bioinformatics
BACKGROUND: Non-small cell lung cancer (NSCLC) is one of the leading causes of death globally, and research into NSCLC has been accumulating steadily over several years. Drug repositioning is the current trend in the pharmaceutical industry for ident...

canSAR: an updated cancer research and drug discovery knowledgebase.

Nucleic acids research
canSAR (http://cansar.icr.ac.uk) is a publicly available, multidisciplinary, cancer-focused knowledgebase developed to support cancer translational research and drug discovery. canSAR integrates genomic, protein, pharmacological, drug and chemical da...

Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection.

IEEE/ACM transactions on computational biology and bioinformatics
Recently, feature selection and dimensionality reduction have become fundamental tools for many data mining tasks, especially for processing high-dimensional data such as gene expression microarray data. Gene expression microarray data comprises up t...

Evaluation and integration of cancer gene classifiers: identification and ranking of plausible drivers.

Scientific reports
The number of mutated genes in cancer cells is far larger than the number of mutations that drive cancer. The difficulty this creates for identifying relevant alterations has stimulated the development of various computational approaches to distingui...

Prediction of cancer proteins by integrating protein interaction, domain frequency, and domain interaction data using machine learning algorithms.

BioMed research international
Many proteins are known to be associated with cancer diseases. It is quite often that their precise functional role in disease pathogenesis remains unclear. A strategy to gain a better understanding of the function of these proteins is to make use of...

TPD52 as a Therapeutic Target Identified by Machine Learning Shapes the Immune Microenvironment in Breast Cancer.

Journal of cellular and molecular medicine
Breast cancer (BRCA) is one of the most common malignancies and a leading cause of cancer-related mortality among women globally. Despite advances in diagnosis and treatment, the heterogeneity of BRCA presents significant challenges for effective man...

Classification and gene selection of triple-negative breast cancer subtype embedding gene connectivity matrix in deep neural network.

Briefings in bioinformatics
Triple-negative breast cancer (TNBC) has been a challenging breast cancer subtype for oncological therapy. Normally, it can be classified into different molecular subtypes. Accurate and stable classification of the six subtypes is essential for perso...