cancer-research-structural-biochemistry-2019-posters

Page 36

Volume 3

August 5-6, 2019 | Singapore

CANCER RESEARCH AND PHARMACOLOGY

STRUCTURAL BIOCHEMISTRY, STEM CELLS AND MOLECULAR BIOLOGY

International Conference on

International Congress on

Cancer Research 2019 & Structural Biochemistry 2019

August 5-6, 2019

Journal of Cancer and Metastasis Research

On the performance of variable selection and classification via ranked based classifier

Showaib Rahaman Sarker

The University of Texas, USA

n high-dimensional gene expression data analysis,

the accuracy and reliability of cancer classification

and selection of important genes play a very crucial

role. To identify these important genes and predict

future outcomes (tumor vs. non-tumor), various

methods have been proposed in the literature. But

only few of them take into account correlation patterns

and grouping effects among the genes. In this article,

we propose a rank-based modification of the popular

penalized logistic regression procedure based on a

combination of l1 and l2 penalties capable of handling

possible correlation among genes in different groups.

While the l1 penalty maintains sparsity, the l2 penalty

induces smoothness based on the information from

the Laplacian matrix, which represents the correlation

pattern among genes. We combined logistic regression

with the BH-FDR (Benjamini and Hochberg false

discovery rate) screening procedure and a newly

developed rank-based selection method to come up

with an optimal model retaining the important genes.

Through simulation studies and real-world application

to high-dimensional colon cancer gene expression data,

we demonstrated that the proposed rank-based method

outperforms such currently popular methods as lasso,

adaptive lasso and elastic net when applied both to

gene selection and classification.

Biography

Showaib Rahman Sarker is pursuing his master’s degree in Statistics at The University of Texas at El Paso. He has expertise in

Statistical Machine learning application in High-Dimensional Gene Expression Data. Currently, he is doing his research in High through-

put cancer gene expression data. His main goal is to find out the important genes which are responsible for cancer and classify (tumor

vs non-tumor) accurately. He is passionate to apply statistical approach and machine learning approach in cancer research.