AIMC Topic: Neoplasm Proteins

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

IEEE/ACM transactions on computational biology and bioinformatics Sep 14, 2015
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...
Neoplasms Genetic Predisposition to Disease Databases, Genetic Neoplasm Proteins Genetic Markers Data Mining Pattern Recognition, Automated Genes, Neoplasm Machine Learning Algorithms Reproducibility of Results Sensitivity and Specificity Humans
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Machine Learning-based Classification of Diffuse Large B-cell Lymphoma Patients by Their Protein Expression Profiles.

Molecular & cellular proteomics : MCP Aug 26, 2015
Characterization of tumors at the molecular level has improved our knowledge of cancer causation and progression. Proteomic analysis of their signaling pathways promises to enhance our understanding of cancer aberrations at the functional level, but ...
Phosphoprotein Phosphatases Tissue Embedding Tissue Fixation Lymphoma, Large B-Cell, Diffuse Formaldehyde Humans Cell Line, Tumor Proteome Isotope Labeling Machine Learning Gene Expression Regulation, Neoplastic Signal Transduction Apoptosis Regulatory Proteins Biomarkers, Tumor Proteomics GTPase-Activating Proteins Neoplasm Proteins Neprilysin Principal Component Analysis
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Evaluation and integration of cancer gene classifiers: identification and ranking of plausible drivers.

Scientific reports May 11, 2015
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...
Genes, Neoplasm Humans United States Machine Learning Databases, Genetic Animals Neoplasm Proteins Mutation
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Prediction of cancer proteins by integrating protein interaction, domain frequency, and domain interaction data using machine learning algorithms.

BioMed research international Mar 17, 2015
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...
Sequence Analysis, Protein Neoplasms Humans Databases, Protein Machine Learning Neoplasm Proteins Algorithms Protein Structure, Tertiary Animals
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TPD52 as a Therapeutic Target Identified by Machine Learning Shapes the Immune Microenvironment in Breast Cancer.

Journal of cellular and molecular medicine Jan 1, 2025
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...
Humans Machine Learning Prognosis Female Cell Line, Tumor Single-Cell Analysis Computational Biology Gene Expression Regulation, Neoplastic Neoplasm Proteins Breast Neoplasms Tumor Microenvironment Biomarkers, Tumor Gene Expression Profiling
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Classification and gene selection of triple-negative breast cancer subtype embedding gene connectivity matrix in deep neural network.

Briefings in bioinformatics Sep 2, 2021
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...
Humans Antineoplastic Agents Gene Regulatory Networks RNA, Messenger Gene Expression Regulation, Neoplastic Triple Negative Breast Neoplasms Neural Networks, Computer Algorithms Precision Medicine Female RNA, Long Noncoding Neoplasm Proteins
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Bioinformatics and machine learning methodologies to identify the effects of central nervous system disorders on glioblastoma progression.

Briefings in bioinformatics Sep 2, 2021
Glioblastoma (GBM) is a common malignant brain tumor which often presents as a comorbidity with central nervous system (CNS) disorders. Both CNS disorders and GBM cells release glutamate and show an abnormality, but differ in cellular behavior. So, t...
Molecular Sequence Annotation Gene Ontology Atlases as Topic Glutamic Acid Gene Expression Regulation, Neoplastic Brain Neoplasms Signal Transduction Gene Expression Profiling Neoplasm Proteins Gene Regulatory Networks Humans Datasets as Topic Glioblastoma Computational Biology Machine Learning Central Nervous System Proportional Hazards Models Kaplan-Meier Estimate
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Machine learning-based integrative analysis of methylome and transcriptome identifies novel prognostic DNA methylation signature in uveal melanoma.

Briefings in bioinformatics Jul 20, 2021
Uveal melanoma (UVM) is the most common primary intraocular human malignancy with a high mortality rate. Aberrant DNA methylation has rapidly emerged as a diagnostic and prognostic signature in many cancers. However, such DNA methylation signature av...
DNA, Neoplasm Male Uveal Neoplasms Middle Aged Uveal Melanoma Humans Aged Gene Expression Profiling Neoplasm Proteins Gene Expression Regulation, Neoplastic Machine Learning Aged, 80 and over Transcriptome Predictive Value of Tests Female Adult DNA Methylation Disease-Free Survival Melanoma Survival Rate
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Identification of pan-cancer Ras pathway activation with deep learning.

Briefings in bioinformatics Jul 20, 2021
The identification of hidden responders is often an essential challenge in precision oncology. A recent attempt based on machine learning has been proposed for classifying aberrant pathway activity from multiomic cancer data. However, we note several...
Neoplasm Proteins Mutation Programming Languages Gene Dosage ras Proteins Neoplasms Deep Learning RNA-Seq Signal Transduction Humans
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DeepHPV: a deep learning model to predict human papillomavirus integration sites.

Briefings in bioinformatics Jul 20, 2021
Human papillomavirus (HPV) integrating into human genome is the main cause of cervical carcinogenesis. HPV integration selection preference shows strong dependence on local genomic environment. Due to this theory, it is possible to predict HPV integr...
Viral Proteins Alphapapillomavirus Virus Integration Deep Learning Papillomavirus Infections Models, Genetic Neoplasm Proteins Humans Female Uterine Cervical Neoplasms
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