Neural network analysis of lymphoma microarray data: prognosis and diagnosis near-perfect

O'Neill, Michael C.; Song, Li

doi:10.1186/1471-2105-4-13

Cited by 71 publications

(8 citation statements)

References 13 publications

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“…SPD represents a new class of machine learning algorithms that has not been extensively applied to microarray analysis. The more common machine learning algorithms that have been used to analyze microarray data include unsupervised clustering [19] , [24] , supervised classification [25] , [26] , [27] , [28] , and statistical tests for differential expression [20] , [29] . Although these algorithms are quite different from each other, they share a similar goal, which is to identify differences between different sample groups, i.e.…”

Section: Discussionmentioning

confidence: 99%

Discovering Biological Progression Underlying Microarray Samples

2011

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In biological systems that undergo processes such as differentiation, a clear concept of progression exists. We present a novel computational approach, called Sample Progression Discovery (SPD), to discover patterns of biological progression underlying microarray gene expression data. SPD assumes that individual samples of a microarray dataset are related by an unknown biological process (i.e., differentiation, development, cell cycle, disease progression), and that each sample represents one unknown point along the progression of that process. SPD aims to organize the samples in a manner that reveals the underlying progression and to simultaneously identify subsets of genes that are responsible for that progression. We demonstrate the performance of SPD on a variety of microarray datasets that were generated by sampling a biological process at different points along its progression, without providing SPD any information of the underlying process. When applied to a cell cycle time series microarray dataset, SPD was not provided any prior knowledge of samples' time order or of which genes are cell-cycle regulated, yet SPD recovered the correct time order and identified many genes that have been associated with the cell cycle. When applied to B-cell differentiation data, SPD recovered the correct order of stages of normal B-cell differentiation and the linkage between preB-ALL tumor cells with their cell origin preB. When applied to mouse embryonic stem cell differentiation data, SPD uncovered a landscape of ESC differentiation into various lineages and genes that represent both generic and lineage specific processes. When applied to a prostate cancer microarray dataset, SPD identified gene modules that reflect a progression consistent with disease stages. SPD may be best viewed as a novel tool for synthesizing biological hypotheses because it provides a likely biological progression underlying a microarray dataset and, perhaps more importantly, the candidate genes that regulate that progression.

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Section: Discussionmentioning

confidence: 99%

Discovering Biological Progression Underlying Microarray Samples

2011

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“… 167 , 168 When using Dempster–Shafer theory for belief updating, this implication network methodology is termed as a Dempster– Shafer belief network. 169 , 170 An implication network is a general methodology for reasoning under uncertainty, as are other alternative formalisms such as neural networks, 171 , 172 dependency networks, 173 Gaussian networks, 174 Mycin’s certainty factors, 175 Prospector’s inference nets, 176 , 177 and fuzzy sets. 167 POKSs are closed under union and intersection of implication relations, and have the formal properties of directed acyclic graphs.…”

Section: General Methodologies For Modeling Molecular Networkmentioning

confidence: 99%

Network-Based Identification of Biomarkers Coexpressed with Multiple Pathways

Guo

Wan

2014

Cancer Inform

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Unraveling complex molecular interactions and networks and incorporating clinical information in modeling will present a paradigm shift in molecular medicine. Embedding biological relevance via modeling molecular networks and pathways has become increasingly important for biomarker identification in cancer susceptibility and metastasis studies. Here, we give a comprehensive overview of computational methods used for biomarker identification, and provide a performance comparison of several network models used in studies of cancer susceptibility, disease progression, and prognostication. Specifically, we evaluated implication networks, Boolean networks, Bayesian networks, and Pearson’s correlation networks in constructing gene coexpression networks for identifying lung cancer diagnostic and prognostic biomarkers. The results show that implication networks, implemented in Genet package, identified sets of biomarkers that generated an accurate prediction of lung cancer risk and metastases; meanwhile, implication networks revealed more biologically relevant molecular interactions than Boolean networks, Bayesian networks, and Pearson’s correlation networks when evaluated with MSigDB database.

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“…For example, they have been applied to predict seizure-like event onsets, 13 to detect interictal spikes 21 and to discriminate anesthetic states, 34 to name a few. In leukemia research, they have been applied to lymphoma microarray data analysis 15,25 and to gene expression. 22,31 The approach developed in this study is based on using NeuralStudio, 1 an ANN simulator which has been designed in support of such neural network-based applications.…”

Section: Description Of the Problemmentioning

confidence: 99%

Classification of Leukemia Blood Samples Using Neural Networks

et al. 2009

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Pattern recognition applied to blood samples for diagnosing leukemia remains an extremely difficult task which frequently leads to misclassification errors due in large part to the inherent problem of data overlap. A novel artificial neural network (ANN) algorithm is proposed for optimizing the classification of multidimensional data, focusing on acute leukemia samples. The programming tool established around the ANN architecture focuses on the classification of normal vs. abnormal blood samples, namely acute lymphocytic leukemia (ALL) and acute myeloid leukemia (AML). There were 220 blood samples considered with 60 abnormal samples and 160 normal samples. The algorithm produced very high sensitivity results that improved up to 96.67% in ALL classification with increased data set size. With this type of accuracy, this programming tool provides information to medical doctors in the form of diagnostic references for the specific disease states that are considered for this study. The results obtained prove that a neural network classifier can perform remarkably well for this type of flow-cytometry data. Even more significant is the fact that experimental evaluations in the testing phase reveal that as the ALL data considered is gradually increased from small to large data sets, the more accurate are the classification results.

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Neural network analysis of lymphoma microarray data: prognosis and diagnosis near-perfect

Cited by 71 publications

References 13 publications

Discovering Biological Progression Underlying Microarray Samples

Discovering Biological Progression Underlying Microarray Samples

Network-Based Identification of Biomarkers Coexpressed with Multiple Pathways

Classification of Leukemia Blood Samples Using Neural Networks

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