AIMC Topic: Neoplasms

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Affinity network fusion and semi-supervised learning for cancer patient clustering.

Methods (San Diego, Calif.)
Defining subtypes of complex diseases such as cancer and stratifying patient groups with the same disease but different subtypes for targeted treatments is important for personalized and precision medicine. Approaches that incorporate multi-omic data...

Machine learning identifies a core gene set predictive of acquired resistance to EGFR tyrosine kinase inhibitor.

Journal of cancer research and clinical oncology
PURPOSE: Acquired resistance (AR) to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) is a major issue worldwide, for both patients and healthcare providers. However, precise prediction is currently infeasible due to the lack o...

Annotating Diseases Using Human Phenotype Ontology Improves Prediction of Disease-Associated Long Non-coding RNAs.

Journal of molecular biology
Recently, many long non-coding RNAs (lncRNAs) have been identified and their biological function has been characterized; however, our understanding of their underlying molecular mechanisms related to disease is still limited. To overcome the limitati...

A DNA Nanorobot Uprises against Cancer.

Trends in molecular medicine
DNA-based nanorobots have been shown to sense and respond to molecular triggers, such as intracellular pH and cell surface receptors. A recent report describes DNA nanorobots as potential cancer therapeutic agents that can be programmed to trigger co...

Automated extraction of Biomarker information from pathology reports.

BMC medical informatics and decision making
BACKGROUND: Pathology reports are written in free-text form, which precludes efficient data gathering. We aimed to overcome this limitation and design an automated system for extracting biomarker profiles from accumulated pathology reports.

A cure-rate model for Q-learning: Estimating an adaptive immunosuppressant treatment strategy for allogeneic hematopoietic cell transplant patients.

Biometrical journal. Biometrische Zeitschrift
Cancers treated by transplantation are often curative, but immunosuppressive drugs are required to prevent and (if needed) to treat graft-versus-host disease. Estimation of an optimal adaptive treatment strategy when treatment at either one of two st...

Extracting cancer mortality statistics from death certificates: A hybrid machine learning and rule-based approach for common and rare cancers.

Artificial intelligence in medicine
OBJECTIVE: Death certificates are an invaluable source of cancer mortality statistics. However, this value can only be realised if accurate, quantitative data can be extracted from certificates-an aim hampered by both the volume and variable quality ...

Integrative analysis and machine learning on cancer genomics data using the Cancer Systems Biology Database (CancerSysDB).

BMC bioinformatics
BACKGROUND: Recent cancer genome studies on many human cancer types have relied on multiple molecular high-throughput technologies. Given the vast amount of data that has been generated, there are surprisingly few databases which facilitate access to...

Towards precision informatics of pharmacovigilance: OAE-CTCAE mapping and OAE-based representation and analysis of adverse events in patients treated with cancer drugs.

AMIA ... Annual Symposium proceedings. AMIA Symposium
A critical issue in the usage of cancer drugs is its association with various adverse events (AEs) in some, but not all, patients. The National Cancer Institute (NCI) Common Terminology Criteria for Adverse Events (CTCAE) is a controlled terminology ...

Stage-Specific Survivability Prediction Models across Different Cancer Types.

AMIA ... Annual Symposium proceedings. AMIA Symposium
For all cancer types, survivability rates vary widely across different stages of cancer. But survivability prediction models built in past were trained using examples of all stages together and were also evaluated on all stages together. In this work...