AIMC Topic: Neoplasms

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A Tool for Predicting Regulatory Approval After Phase II Testing of New Oncology Compounds.

Clinical pharmacology and therapeutics
We developed an algorithm (ANDI) for predicting regulatory marketing approval for new cancer drugs after phase II testing has been conducted, with the objective of providing a tool to improve drug portfolio decision-making. We examined 98 oncology dr...

An ensemble approach to accurately detect somatic mutations using SomaticSeq.

Genome biology
SomaticSeq is an accurate somatic mutation detection pipeline implementing a stochastic boosting algorithm to produce highly accurate somatic mutation calls for both single nucleotide variants and small insertions and deletions. The workflow currentl...

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...

Knowledge-Based Query Construction Using the CDSS Knowledge Base for Efficient Evidence Retrieval.

Sensors (Basel, Switzerland)
Finding appropriate evidence to support clinical practices is always challenging, and the construction of a query to retrieve such evidence is a fundamental step. Typically, evidence is found using manual or semi-automatic methods, which are time-con...

ReCAP: Feasibility and Accuracy of Extracting Cancer Stage Information From Narrative Electronic Health Record Data.

Journal of oncology practice
PURPOSE: Cancer stage, one of the most important prognostic factors for cancer-specific survival, is often documented in narrative form in electronic health records (EHRs). Such documentation results in tedious and time-consuming abstraction efforts ...

A Minimum Spanning Forest-Based Method for Noninvasive Cancer Detection With Hyperspectral Imaging.

IEEE transactions on bio-medical engineering
GOAL: The purpose of this paper is to develop a classification method that combines both spectral and spatial information for distinguishing cancer from healthy tissue on hyperspectral images in an animal model.

Automatic ICD-10 classification of cancers from free-text death certificates.

International journal of medical informatics
OBJECTIVE: Death certificates provide an invaluable source for 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 na...

Identifying relevant group of miRNAs in cancer using fuzzy mutual information.

Medical & biological engineering & computing
MicroRNAs (miRNAs) act as a major biomarker of cancer. All miRNAs in human body are not equally important for cancer identification. We propose a methodology, called FMIMS, which automatically selects the most relevant miRNAs for a particular type of...

Prediction of core cancer genes using a hybrid of feature selection and machine learning methods.

Genetics and molecular research : GMR
Machine learning techniques are of great importance in the analysis of microarray expression data, and provide a systematic and promising way to predict core cancer genes. In this study, a hybrid strategy was introduced based on machine learning tech...