1. A Novel MKL Method for GBM Prognosis Prediction by Integrating Histopathological Image and Multi-Omics Data
- Author
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Ya Zhang, Ao Li, Minghui Wang, and Jie He
- Subjects
0301 basic medicine ,Prognosis prediction ,Computer science ,Feature extraction ,Life quality ,Machine learning ,computer.software_genre ,03 medical and health sciences ,0302 clinical medicine ,Health Information Management ,Cancer genome ,Humans ,Diagnosis, Computer-Assisted ,Electrical and Electronic Engineering ,Medical diagnosis ,Clinical treatment ,Multiple kernel learning ,business.industry ,Brain Neoplasms ,Brain ,Genomics ,Prognosis ,Computer Science Applications ,030104 developmental biology ,030220 oncology & carcinogenesis ,Multi omics ,Artificial intelligence ,business ,Glioblastoma ,computer ,Algorithms ,Biotechnology - Abstract
Glioblastoma multiforme (GBM) is one of the most malignant brain tumors with very short prognosis expectation. To improve patients’ clinical treatment and their life quality after surgery, researches have developed tremendous in silico models and tools for predicting GBM prognosis based on molecular datasets and have earned great success. However, pathology still plays the most critical role in cancer diagnosis and prognosis in the clinic at present. Recent advancement of storing and processing histopathological images has drawn attention of researchers. Models based on histopathological images are developed, which show great potential for computer-aided pathological diagnoses. But models based on both molecular and histopathological images that could predict GBM prognosis with high accuracy are not present yet. In our previous research, we used the simple MKL method to integrate multi-omics data to improve GBM prognosis prediction successfully. In this paper, we have developed a novel multiple kernel learning (MKL) method, named histopathological integrating multiple kernel learning (HI-MKL), that could integrate both histopathological images and multi-omics data efficiently. By using datasets from The Cancer Genome Atlas project, we have built a system that could predict the GBM prognosis with high accuracy. Our research shows that HI-MKL is an accurate, robust, and generalized MKL method, which performs well in a GBM prognosis task.
- Published
- 2019