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

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 high-throughput technologies lead to production of large amounts of genomic and epigenomic data, the classification feature of SVMs is expanding its use in cancer genomics, leading to the discovery of new biomarkers, new drug targets, and a better understanding of cancer driver genes. Herein we reviewed the recent progress of SVMs in cancer genomic studies.

A 16 Yin Yang gene expression ratio signature for ER+/node- breast cancer.

Breast cancer is one of the leading causes of cancer death in women. It is a complex and heterogeneous disease with different clinical outcomes. Stratifying patients into subgroups with different outcomes could help guide clinical decision making.

RNAs specifically affect gene expression in a length, position and sequence dependent manner.

We aim to explore if RNA regulating gene expression is affected by length, sequence and position of RNA. HeLa cells were co-transfected with modulator plasmids (derived from pcDNA3.1 vector containing different length regulating sequences that produce RNAs) and reporter plasmids (derived from pEGFP-C1 vector); In addition, HeLa cells were transfected with plasmids that possess different sequences of downstream or adjacent genes of GFP reporter gene.

[Apoptosis of human leukemia K562 cell in vitro induced by Toxoplasma gondii].

Objective: To investigate whether the Toxoplasma gondii can inhibit proliferation of human leukemia K562 cells and/or induce apoptosis of the cells in vitro. Methods K562 cells (5x10(4)/ml) were harvested at mid-exponential phase and planted in 96 well plates with 100 microl each and in 50 ml culture bottles, 1.5 ml each.