CSNet : A Novel Protein Complex- based Feature Selection Method in Clinical Proteomics

سال انتشار: 1398
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 313

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شناسه ملی سند علمی:

IBIS09_016

تاریخ نمایه سازی: 19 اسفند 1399

چکیده مقاله:

In the past decade and a half, many proteomics data statistical analysis approaches have been proposed to identify proteins whose expression profile is useful for the diagnosis, prognosis-treatment, and deciphering cause of a disease [1]. Unfortunately, the problems with using these approaches are a large number of false positives, lacking reproducibility and stability, irrelevance selected features and batch effects [2]. Recently, there has been a paradigm shift from looking at individual proteins to preselected protein complexes. Protein complex-based feature selection (PCBFS) methods divide into two classes. The first -class contains methods that use primary protein expression data to select features .The second -class contains methods that work on normalized expression data [3,4]. We present powerful method, method, CSNet ,that uses a combination of both expression data and normalized data to calculate the complexes score .CSNet is particularly resistant against batch effects and selects features strongly correlated with class but not batch.

نویسندگان

Behnam Reihani Matanagh

Department of Computer Scinces, Faculty of Mathematics, Shahid Beheshti University, Tehran, Iran

Soheil Jahangiri Tazehkana

Princess Margaret Cancer Research Center, UHN, University of Toronto, Toronto, Canada

Changiz Eslahchi

School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran