AIMC Topic: Neoplasms

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eGARD: Extracting associations between genomic anomalies and drug responses from text.

PloS one
Tumor molecular profiling plays an integral role in identifying genomic anomalies which may help in personalizing cancer treatments, improving patient outcomes and minimizing risks associated with different therapies. However, critical information re...

Identification of the functional alteration signatures across different cancer types with support vector machine and feature analysis.

Biochimica et biophysica acta. Molecular basis of disease
Cancers are regarded as malignant proliferations of tumor cells present in many tissues and organs, which can severely curtail the quality of human life. The potential of using plasma DNA for cancer detection has been widely recognized, leading to th...

Hybrid Method Based on Information Gain and Support Vector Machine for Gene Selection in Cancer Classification.

Genomics, proteomics & bioinformatics
It remains a great challenge to achieve sufficient cancer classification accuracy with the entire set of genes, due to the high dimensions, small sample size, and big noise of gene expression data. We thus proposed a hybrid gene selection method, Inf...

Synthetic Ion Channels and DNA Logic Gates as Components of Molecular Robots.

Chemphyschem : a European journal of chemical physics and physical chemistry
A molecular robot is a next-generation biochemical machine that imitates the actions of microorganisms. It is made of biomaterials such as DNA, proteins, and lipids. Three prerequisites have been proposed for the construction of such a robot: sensors...

Classification of cancer cells using computational analysis of dynamic morphology.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Detection of metastatic tumor cells is important for early diagnosis and staging of cancer. However, such cells are exceedingly difficult to detect from blood or biopsy samples at the disease onset. It is reported that cance...

Cancer Classification Based on Support Vector Machine Optimized by Particle Swarm Optimization and Artificial Bee Colony.

Molecules (Basel, Switzerland)
Intelligent optimization algorithms have advantages in dealing with complex nonlinear problems accompanied by good flexibility and adaptability. In this paper, the FCBF (Fast Correlation-Based Feature selection) method is used to filter irrelevant an...

Meta-Path Methods for Prioritizing Candidate Disease miRNAs.

IEEE/ACM transactions on computational biology and bioinformatics
MicroRNAs (miRNAs) play critical roles in regulating gene expression at post-transcriptional levels. Numerous experimental studies indicate that alterations and dysregulations in miRNAs are associated with important complex diseases, especially cance...

Disease Gene Prediction by Integrating PPI Networks, Clinical RNA-Seq Data and OMIM Data.

IEEE/ACM transactions on computational biology and bioinformatics
Disease gene prediction is a challenging task that has a variety of applications such as early diagnosis and drug development. The existing machine learning methods suffer from the imbalanced sample issue because the number of known disease genes (po...

Structured Penalized Logistic Regression for Gene Selection in Gene Expression Data Analysis.

IEEE/ACM transactions on computational biology and bioinformatics
In gene expression data analysis, the problems of cancer classification and gene selection are closely related. Successfully selecting informative genes will significantly improve the classification performance. To identify informative genes from a l...

Open source machine-learning algorithms for the prediction of optimal cancer drug therapies.

PloS one
Precision medicine is a rapidly growing area of modern medical science and open source machine-learning codes promise to be a critical component for the successful development of standardized and automated analysis of patient data. One important goal...