2018/10/29 by Siddhant Jain, Jalal Ziauddin, Jain, Siddhant +5
Biochemistry, Genetics and Molecular Biology · Computer Science · #68T10 #Algorithms and Data Compression #FOS: Computer and information sciences #FOS: Physical sciences #Gene expression and cancer classification #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Bioinformatics #Quantum Physics (quant-ph)
paper · pdf · doi:10.48550/arxiv.1810.11959
openalex publication_date 2018/10/29 · openalex created_date 2018/11/02 · openalex updated_date 2026/07/28
The ability to accurately classify disease subtypes is of vital importance, especially in oncology where this capability could have a life saving impact. Here we report a classification between two subtypes of non-small cell lung cancer, namely Adeno- carcinoma vs Squamous cell carcinoma. The data consists of approximately 20,000 gene expression values for each of 104 patients. The data was curated from [1] [2]. We used an amalgamation of classical and and quantum machine learning models to successfully classify these patients. We utilized feature selection methods based on univariate statistics in addition to XGBoost [3]. A novel and proprietary data representation method developed by one of the authors called QCrush was also used as it was designed to incorporate a maximal amount of information under the size constraints of the D-Wave quantum annealing computer. The machine learning was performed by a Quantum Boltzmann Machine. This paper will report our results, the various classical methods, and the quantum machine learning approach we utilized.