Elephant Search with Deep Learning for Microarray Data Analysis

Even though there is a plethora of research in Microarray gene expression data analysis, still, it poses challenges for researchers to effectively and efficiently analyze the large yet complex expression of genes. The feature (gene) selection method is of paramount importance for understanding the differences in biological and non-biological variation between samples. In order to address this problem, a novel elephant search (ES) based optimization is proposed to select best gene expressions from the large volume of microarray data. Further, a promising machine learning method is envisioned to leverage such high dimensional and complex microarray dataset for extracting hidden patterns inside to make a meaningful prediction and most accurate classification. In particular, stochastic gradient descent based Deep learning (DL) with softmax activation function is then used on the reduced features (genes) for better classification of different samples according to their gene expression levels. The experiments are carried out on nine most popular Cancer microarray gene selection datasets, obtained from UCI machine learning repository. The empirical results obtained by the proposed elephant search based deep learning (ESDL) approach are compared with most recent published article for its suitability in future Bioinformatics research.Comment: 12 pages, 5 Tabl

Motif Discovery through Predictive Modeling of Gene Regulation

We present MEDUSA, an integrative method for learning motif models of transcription factor binding sites by incorporating promoter sequence and gene expression data. We use a modern large-margin machine learning approach, based on boosting, to enable feature selection from the high-dimensional search space of candidate binding sequences while avoiding overfitting. At each iteration of the algorithm, MEDUSA builds a motif model whose presence in the promoter region of a gene, coupled with activity of a regulator in an experiment, is predictive of differential expression. In this way, we learn motifs that are functional and predictive of regulatory response rather than motifs that are simply overrepresented in promoter sequences. Moreover, MEDUSA produces a model of the transcriptional control logic that can predict the expression of any gene in the organism, given the sequence of the promoter region of the target gene and the expression state of a set of known or putative transcription factors and signaling molecules. Each motif model is either a $k$-length sequence, a dimer, or a PSSM that is built by agglomerative probabilistic clustering of sequences with similar boosting loss. By applying MEDUSA to a set of environmental stress response expression data in yeast, we learn motifs whose ability to predict differential expression of target genes outperforms motifs from the TRANSFAC dataset and from a previously published candidate set of PSSMs. We also show that MEDUSA retrieves many experimentally confirmed binding sites associated with environmental stress response from the literature.Comment: RECOMB 200

Assessing similarity of feature selection techniques in high-dimensional domains

Recent research efforts attempt to combine multiple feature selection techniques instead of using a single one. However, this combination is often made on an “ad hoc” basis, depending on the specific problem at hand, without considering the degree of diversity/similarity of the involved methods. Moreover, though it is recognized that different techniques may return quite dissimilar outputs, especially in high dimensional/small sample size domains, few direct comparisons exist that quantify these differences and their implications on classification performance. This paper aims to provide a contribution in this direction by proposing a general methodology for assessing the similarity between the outputs of different feature selection methods in high dimensional classification problems. Using as benchmark the genomics domain, an empirical study has been conducted to compare some of the most popular feature selection methods, and useful insight has been obtained about their pattern of agreement

An efficient statistical feature selection approach for classification of gene expression data

AbstractClassification of gene expression data plays a significant role in prediction and diagnosis of diseases. Gene expression data has a special characteristic that there is a mismatch in gene dimension as opposed to sample dimension. All genes do not contribute for efficient classification of samples. A robust feature selection algorithm is required to identify the important genes which help in classifying the samples efficiently. In order to select informative genes (features) based on relevance and redundancy characteristics, many feature selection algorithms have been introduced in the past. Most of the earlier algorithms require computationally expensive search strategy to find an optimal feature subset. Existing feature selection methods are also sensitive to the evaluation measures. The paper introduces a novel and efficient feature selection approach based on statistically defined effective range of features for every class termed as ERGS (Effective Range based Gene Selection). The basic principle behind ERGS is that higher weight is given to the feature that discriminates the classes clearly. Experimental results on well-known gene expression datasets illustrate the effectiveness of the proposed approach. Two popular classifiers viz. Nave Bayes Classifier (NBC) and Support Vector Machine (SVM) have been used for classification. The proposed feature selection algorithm can be helpful in ranking the genes and also is capable of identifying the most relevant genes responsible for diseases like leukemia, colon tumor, lung cancer, diffuse large B-cell lymphoma (DLBCL), prostate cancer

Gene Expression based Survival Prediction for Cancer Patients: A Topic Modeling Approach

Cancer is one of the leading cause of death, worldwide. Many believe that genomic data will enable us to better predict the survival time of these patients, which will lead to better, more personalized treatment options and patient care. As standard survival prediction models have a hard time coping with the high-dimensionality of such gene expression (GE) data, many projects use some dimensionality reduction techniques to overcome this hurdle. We introduce a novel methodology, inspired by topic modeling from the natural language domain, to derive expressive features from the high-dimensional GE data. There, a document is represented as a mixture over a relatively small number of topics, where each topic corresponds to a distribution over the words; here, to accommodate the heterogeneity of a patient's cancer, we represent each patient (~document) as a mixture over cancer-topics, where each cancer-topic is a mixture over GE values (~words). This required some extensions to the standard LDA model eg: to accommodate the "real-valued" expression values - leading to our novel "discretized" Latent Dirichlet Allocation (dLDA) procedure. We initially focus on the METABRIC dataset, which describes breast cancer patients using the r=49,576 GE values, from microarrays. Our results show that our approach provides survival estimates that are more accurate than standard models, in terms of the standard Concordance measure. We then validate this approach by running it on the Pan-kidney (KIPAN) dataset, over r=15,529 GE values - here using the mRNAseq modality - and find that it again achieves excellent results. In both cases, we also show that the resulting model is calibrated, using the recent "D-calibrated" measure. These successes, in two different cancer types and expression modalities, demonstrates the generality, and the effectiveness, of this approach

Unsupervised fuzzy pattern discovery in gene expression data

2010-2011 > Academic research: refereed > Publication in refereed journalpublished_fina

Predicting Genetic Regulatory Response Using Classification

We present a novel classification-based method for learning to predict gene regulatory response. Our approach is motivated by the hypothesis that in simple organisms such as Saccharomyces cerevisiae, we can learn a decision rule for predicting whether a gene is up- or down-regulated in a particular experiment based on (1) the presence of binding site subsequences (``motifs'') in the gene's regulatory region and (2) the expression levels of regulators such as transcription factors in the experiment (``parents''). Thus our learning task integrates two qualitatively different data sources: genome-wide cDNA microarray data across multiple perturbation and mutant experiments along with motif profile data from regulatory sequences. We convert the regression task of predicting real-valued gene expression measurement to a classification task of predicting +1 and -1 labels, corresponding to up- and down-regulation beyond the levels of biological and measurement noise in microarray measurements. The learning algorithm employed is boosting with a margin-based generalization of decision trees, alternating decision trees. This large-margin classifier is sufficiently flexible to allow complex logical functions, yet sufficiently simple to give insight into the combinatorial mechanisms of gene regulation. We observe encouraging prediction accuracy on experiments based on the Gasch S. cerevisiae dataset, and we show that we can accurately predict up- and down-regulation on held-out experiments. Our method thus provides predictive hypotheses, suggests biological experiments, and provides interpretable insight into the structure of genetic regulatory networks.Comment: 8 pages, 4 figures, presented at Twelfth International Conference on Intelligent Systems for Molecular Biology (ISMB 2004), supplemental website: http://www.cs.columbia.edu/compbio/geneclas