An integrated method for cancer classification and rule extraction from microarray data

Abstract

Different microarray techniques recently have been successfully used to investigate useful information for cancer diagnosis at the gene expression level due to their ability to measure thousands of gene expression levels in a massively parallel way. One important issue is to improve classification performance of microarray data. However, it would be ideal that influential genes and even interpretable rules can be explored at the same time to offer biological insight.Introducing the concepts of system design in software engineering, this paper has presented an integrated and effective method (named X-AI) for accurate cancer classification and the acquisition of knowledge from DNA microarray data. This method included a feature selector to systematically extract the relative important genes so as to reduce the dimension and retain as much as possible of the class discriminatory information. Next, diagonal quadratic discriminant analysis (DQDA) was combined to classify tumors, and generalized rule induction (GRI) was integrated to establish association rules which can give an understanding of the relationships between cancer classes and related genes.Two non-redundant datasets of acute leukemia were used to validate the proposed X-AI, showing significantly high accuracy for discriminating different classes. On the other hand, I have presented the abilities of X-AI to extract relevant genes, as well as to develop interpretable rules. Further, a web server has been established for cancer classification and it is freely available at http:// bioinformatics.myweb.hinet.net/xai.htm.

Identification and Optimization of Classifier Genes from Multi-Class Earthworm Microarray Dataset

“…Microarrays have also successfully served as a research tool in discovering novel drug targets [5] and disease- or toxicity-related biomarker genes for cancer classification [6]. In ecological risk assessment, indigenous species such as fish and earthworms are often used as bioindicators for adverse effects caused by environmental contaminants.…”

geneCBR: a translational tool for multiple-microarray analysis and integrative information retrieval for aiding diagnosis in cancer research

“…Given that systematic classification of tumor types is crucial to achieving advances in cancer treatment, different machine learning and statistical techniques have been successfully applied for cancer classification at the gene expression level. These methods include the successful application of neural networks [ 2 ], classification trees and mixture models [ 3 ], hierarchical clustering [ 4 ], support vector machines [ 5 ], shrunken centroids [ 6 , 7 ], compound covariate [ 8 ], partial least square [ 9 ], principal component analysis disjoint models [ 10 ], factor mixture models [ 11 ], consensus analysis of multiple classifiers using non-repetitive variables [ 12 ], diagonal quadratic discriminant analysis with generalized rule induction [ 13 ] etc.…”

Artificial Intelligence Pipeline to Bridge the Gap between Bench Researchers and Clinical Researchers in Precision Medicine

“…There are many approaches to extracting knowledge from free text, including named-entity extraction [39][40][41], topic modeling [42][43][44], and automatic text summarization [45][46][47]. Techniques such as clustering [48,49], frequent pattern identification [50,51], and rule extraction [52][53][54] have been used to extract knowledge from data. IPMP could build additional knowledge representations such as ontologies [55][56][57][58], knowledge graphs [12,59,60] and word embeddings [61][62][63].…”