Prognostic outcome prediction by semi-supervised least squares classification.

Publisher: Oxford University Press Country of Publication: England NLM ID: 100912837 Publication Model: Print Cited Medium: Internet ISSN: 1477-4054 (Electronic) Linking ISSN: 14675463 NLM ISO Abbreviation: Brief Bioinform Subsets: MEDLINE

Publication: Oxford : Oxford University Press
Original Publication: London ; Birmingham, AL : H. Stewart Publications, [2000-

Databases, Nucleic Acid* ; Gene Expression Profiling* ; Gene Expression Regulation, Neoplastic* ; MicroRNAs*/biosynthesis ; MicroRNAs*/genetics ; RNA, Messenger*/biosynthesis ; RNA, Messenger*/genetics ; Supervised Machine Learning*; Neoplasms/*genetics ; Neoplasms/*metabolism; Humans ; Neoplasms/diagnosis ; Predictive Value of Tests ; Prognosis ; RNA, Neoplasm

Although great progress has been made in prognostic outcome prediction, small sample size remains a challenge in obtaining accurate and robust classifiers. We proposed the Rescaled linear square Regression based Least Squares Learning (RRLSL), a jointly developed semi-supervised feature selection and classifier, for predicting prognostic outcome of cancer patients. RRLSL used the least square regression to identify the scale factors and then rank the features in available multiple types of molecular data. We applied the unlabeled multiple molecular data in conjunction with the labeled data to develop a similarity graph. RRLSL produced the constraint with kernel functions to bridge the gap between label information and geometry information from messenger RNA and microRNA expression profiling. Importantly, this semi-supervised model proposed the least squares learning with L2 regularization to develop a semi-supervised classifier. RRLSL suggested the performance improvement in the prognostic outcome prediction and successfully discriminated between the recurrent patients and non-recurrent ones. We also demonstrated that RRLSL improved the accuracy and Area Under the Precision Recall Curve (AUPRC) as compared to the baseline semi-supervised methods. RRLSL is available for a stand-alone software package (https://github.com/ShiMGLab/RRLSL). A short abstract We proposed the Rescaled linear square Regression based Least Squares Learning (RRLSL), a jointly developed semi-supervised feature selection and classifier, for predicting prognostic outcome of cancer patients. RRLSL used the least square regression to identify the scale factors to rank the features in available multiple types of molecular data. RRLSL produced the constraint with kernel functions to bridge the gap between label information and geometry information from messenger RNA and microRNA expression profiling. Importantly, this semi-supervised model proposed the least squares learning with L2 regularization to develop the semi-supervised classifier. RRLSL suggested the performance improvement in the prognostic outcome prediction and successfully discriminated between the recurrent patients and non-recurrent ones.
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Keywords: least squares learning; prognostic outcome prediction; rescaled linear square regression; semi-supervised learning

0 (MicroRNAs)
0 (RNA, Messenger)
0 (RNA, Neoplasm)

Date Created: 20201023 Date Completed: 20211119 Latest Revision: 20211119

20231215

10.1093/bib/bbaa249

33094318