Gene Expression Based Cancer Classification Research Articles

Utility of gene expression–based cancer classification in diagnosis of malignant peritoneal mesothelioma (MPeM): Filling in the gaps in standard pathologic work-up for a rare cancer.

728 Background: Malignant peritoneal mesothelioma (MPeM) is a rare cancer (incidence of 0.12 per 100,000) and an arduous diagnostic challenge, requiring skillful evaluation by expert pathologists. Subjective and experiential immunophenotyping coupled with inattention to this rare entity causes delayed or misdiagnosis and leads to suboptimal treatment and outcomes. Gene-expression-based cancer classification offers an objective molecular assessment of the tissue of origin for cancers with uncertain diagnosis and improves diagnostic accuracy for MPeM compared to standard pathology. Methods: We retrospectively evaluated 23 patients (pts) with cancer of unknown primary (CUP) or uncertain diagnoses, with peritoneal/retroperitoneal carcinomatosis and reported as malignant mesothelioma using a 92-gene assay (CancerTYPE ID), a validated classifier for predicting tumor types, between January 2013 to December 2016. Pathology was verified by central review. Clinicopathologic data including immunohistochemistry (IHC) was collected using archived pathology specimens and reports. Results: Pts had a median age of 64 years (range: 39 – 93) at diagnosis, 57% were females and 74% had biopsy from omentum/peritoneum. Original histopathology was reported as poorly differentiated in 81% and as adenocarcinoma, carcinoma and malignant neoplasm in 10 (43%), 7 (30%) and 6 (27%) of cases, respectively. The number of IHC stains performed ranged between 0 and 28 (median: 10). Key IHC stains are shown in the table. Conclusions: In this cohort of CUP or uncertain diagnoses with a molecular cancer classification of malignant mesothelioma, we found huge variability and critical gaps in pathological work-up. Integration of molecular cancer classification using the 92-gene assay helped resolve this diagnostic uncertainty, further supporting its clinical utility to complement standard pathology. The impact of these findings goes beyond just MPeM and may benefit numerous pts with orphan cancers, who account for nearly 20% of all cancers diagnosed globally. [Table: see text]

GeneNet: Transfer learning-based hybrid African buffalo optimization with genetic algorithm for gene expression based cancer classification

Gene therapy is one of the advanced medical diagnosis environments, which is used to identify the solutions to various cancers by identifying the perfect gene expressions. The computer aided detection of gene expressions is a research problem, where cancer type is identified by artificial intelligence methods. However, the conventional machine learning models were failed to provide the accurate cancer classification and resulted in poor quantitative performance. Therefore, this work focuses on the implementation of gene expression-based cancer classification (GECC) network, here after referred as GeneNet. Initially, the proposed GeneNet performs the data preprocessing to normalize the gene-expression data by replacing the unknown characters and missing symbols. Then, the visual geometry group developed VGG16 model for extraction of correlated, and structural features from pre-processed dataset. In addition, hybrid African buffalo optimization with genetic algorithm (HABO-GA) is used to select the optimal features through natural inspired hunting (searching) properties. Finally, low complex convolutional neural network (LC-CNN) model is trained with HABO-GA features, which also performs the multi-class cancer classification from test data. The simulations are conducted on publicly available GECC datasets such as lymphography, ovarian, leukemia, lung, and prostate cancers discloses the superiority of proposed GeneNetas compared to state-of-the-art approaches with 86.6 %, 100 %, 76.92 %, 100 %, and 100 % of accuracy, respectively.

Gene expression based cancer classification

Cancer classification based on molecular level investigation has gained the interest of researches as it provides a systematic, accurate and objective diagnosis for different cancer types. Several recent researches have been studying the problem of cancer classification using data mining methods, machine learning algorithms and statistical methods to reach an efficient analysis for gene expression profiles.Studying the characteristics of thousands of genes simultaneously offered a deep insight into cancer classification problem. It introduced an abundant amount of data ready to be explored. It has also been applied in a wide range of applications such as drug discovery, cancer prediction and diagnosis which is a very important issue for cancer treatment. Besides, it helps in understanding the function of genes and the interaction between genes in normal and abnormal conditions. That is done by monitoring the behavior of genes -gene expression data- under different conditions.In this paper, an effective ensemble approach is proposed. Ensemble classifiers increase not only the performance of the classification, but also the confidence of the results. The motivations beyond using ensemble classifiers are that the results are less dependent on peculiarities of a single training set and because the ensemble system outperforms the performance of the best base classifier in the ensemble.

Thinned-ECOC ensemble based on sequential code shrinking

Error-correcting output coding (ECOC) is a strategy to create classifier ensembles which reduces a multi-class problem into some binary sub-problems. A key issue in designing any ECOC classifier refers to defining optimal codematrix having maximum discrimination power and minimum number of columns. This paper proposes a heuristic method for application-dependent design of optimal ECOC matrix based on a thinning algorithm. The main idea of the proposed Thinned-ECOC method is to successively remove some redundant and unnecessary columns of any initial codematrix based on a metric defined for each column. As a result, computational cost of the ensemble is reduced while preserving its accuracy. Proposed method has been validated using the UCI machine learning database and further applied to a couple of real-world pattern recognition problems (the face recognition and gene expression based cancer classification). Experimental results emphasize the robustness of Thinned-ECOC in comparison with existing state-of-the-art code generation methods.

Dataset complexity in gene expression based cancer classification using ensembles of k-nearest neighbors

We explore the link between dataset complexity, determining how difficult a dataset is for classification, and classification performance defined by low-variance and low-biased bolstered resubstitution error made by k-nearest neighbor classifiers. Gene expression based cancer classification is used as the task in this study. Six gene expression datasets containing different types of cancer constitute test data. Through extensive simulation coupled with the copula method for analysis of association in bivariate data, we show that dataset complexity and bolstered resubstitution error are associated in terms of dependence. As a result, we propose a new scheme for generating ensembles of classifiers that selects subsets of features of low complexity for ensemble members, which constitutes the accurate members according to the found dependence relation. Experiments with six gene expression datasets demonstrate that our ensemble generating scheme based on the dependence of dataset complexity and classification error is superior to a single best classifier in the ensemble and to the traditional ensemble construction scheme that is ignorant of dataset complexity.

Risk-sensitive loss functions for sparse multi-category classification problems

In this paper, we propose two risk-sensitive loss functions to solve the multi-category classification problems where the number of training samples is small and/or there is a high imbalance in the number of samples per class. Such problems are common in the bio-informatics/medical diagnosis areas. The most commonly used loss functions in the literature do not perform well in these problems as they minimize only the approximation error and neglect the estimation error due to imbalance in the training set. The proposed risk-sensitive loss functions minimize both the approximation and estimation error. We present an error analysis for the risk-sensitive loss functions along with other well known loss functions. Using a neural architecture, classifiers incorporating these risk-sensitive loss functions have been developed and their performance evaluated for two real world multi-class classification problems, viz., a satellite image classification problem and a micro-array gene expression based cancer classification problem. To study the effectiveness of the proposed loss functions, we have deliberately imbalanced the training samples in the satellite image problem and compared the performance of our neural classifiers with those developed using other well-known loss functions. The results indicate the superior performance of the neural classifier using the proposed loss functions both in terms of the overall and per class classification accuracy. Performance comparisons have also been carried out on a number of benchmark problems where the data is normal i.e., not sparse or imbalanced. Results indicate similar or better performance of the proposed loss functions compared to the well-known loss functions.