AIMC Topic: Neoplasms

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A Brief Survey of Machine Learning Application in Cancerlectin Identification.

Current gene therapy
Proteins with at least one carbohydrate recognition domain are lectins that can identify and reversibly interact with glycan moiety of glycoconjugates or a soluble carbohydrate. It has been proved that lectins can play various vital roles in mediatin...

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics.

Cancer genomics & proteomics
Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that has been used for cancer genomic classification or subtyping. Today, as advancements in h...

Annotating gene sets by mining large literature collections with protein networks.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Analysis of patient genomes and transcriptomes routinely recognizes new gene sets associated with human disease. Here we present an integrative natural language processing system which infers common functions for a gene set through automatic mining o...

Visualization of Cancer and Cardiovascular Disease Co-Occurrence With Network Methods.

JCO clinical cancer informatics
PURPOSE: Cancer and cardiovascular disease (CVD) are common causes of morbidity and mortality, and measurement and interpretation of their co-occurrence rate have important implications for public health and patient care. Here, we present the raw and...

DeepPhe: A Natural Language Processing System for Extracting Cancer Phenotypes from Clinical Records.

Cancer research
Precise phenotype information is needed to understand the effects of genetic and epigenetic changes on tumor behavior and responsiveness. Extraction and representation of cancer phenotypes is currently mostly performed manually, making it difficult t...

Using Natural Language Processing to Extract Abnormal Results From Cancer Screening Reports.

Journal of patient safety
OBJECTIVES: Numerous studies show that follow-up of abnormal cancer screening results, such as mammography and Papanicolaou (Pap) smears, is frequently not performed in a timely manner. A contributing factor is that abnormal results may go unrecogniz...

Automated classification of eligibility criteria in clinical trials to facilitate patient-trial matching for specific patient populations.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: To develop automated classification methods for eligibility criteria in ClinicalTrials.gov to facilitate patient-trial matching for specific populations such as persons living with HIV or pregnant women.