Interval based fuzzy systems for identification of important genes from microarray gene expression data: Application to carcinogenic development

AbstractIn the present article, we develop two interval based fuzzy systems for identification of some possible genes mediating the carcinogenic development in various tissues. The methodology involves dimensionality reduction, classifying the genes through incorporation of the notion of linguistic fuzzy sets low, medium and high, and finally selection of some possible genes mediating a particular disease, obtained by a rule generation/grouping technique. The effectiveness of the proposed methodology, is demonstrated using five microarray gene expression datasets dealing with human lung, colon, sarcoma, breast cancer and leukemia. Moreover, the superior capability of the methodology in selecting important genes, over five other existing gene selection methods, viz., Significance Analysis of Microarrays (SAM), Signal-to-Noise Ratio (SNR), Neighborhood analysis (NA), Bayesian Regularization (BR) and Data-adaptive (DA) is demonstrated, in terms of the enrichment of each GO category of the important genes based on P-values. The results are appropriately validated by earlier investigations, gene expression profiles and t-test. The proposed methodology has been able to select genes that are more biologically significant in mediating the development of a disease than those obtained by the others

Fuzzy feature evaluation index and connectionist realization

A new feature evaluation index based on fuzzy set theory and a connectionist model for its evaluation are provided. A concept of flexible membership function incorporating weighting factors, is introduced which makes the modeling of the class structures more appropriate. A neuro-fuzzy algorithm is developed for determining the optimum weighting coefficients representing the feature importance. The overall importance of the features is evaluated both individually and in a group considering their dependence as well as independence. Effectiveness of the algorithms along with comparison is demonstrated on speech and Iris data

Unsupervised feature extraction using neuro-fuzzy approach

The present article demonstrates a way of formulating a neuro-fuzzy approach for feature extraction under unsupervised training. A fuzzy feature evaluation index for a set of features is newly defined in terms of degree of similarity between two patterns in both the original and transformed feature spaces. A concept of flexible membership function incorporating weighted distance is introduced for computing membership values in the transformed space that is obtained by a set of linear transformation on the original space. A layered network is designed for performing the task of minimization of the evaluation index through unsupervised learning process. This extracts a set of optimum transformed features, by projecting n-dimensional original space directly to n'-dimensional (n'<n) transformed space, along with their relative importance. The extracted features are found to provide better classification performance than the original ones for different real life data with dimensions 3, 4, 9, 18 and 34. The superiority of the method over principal component analysis network, nonlinear discriminant analysis network and Kohonen self-organizing feature map is also established

Unsupervised neural network for single cell Multi-omics INTegration (UMINT): an application to health and disease

Multi-omics studies have enabled us to understand the mechanistic drivers behind complex disease states and progressions, thereby providing novel and actionable biological insights into health status. However, integrating data from multiple modalities is challenging due to high dimensionality and diverse nature of data, and noise associated with each platform. Sparsity in data, non-overlapping features and technical batch effects make the task of learning more complicated. Conventional machine learning (ML) tools are not quite effective against such data integration hazards due to their simplistic nature with less capacity. In addition, existing methods for single cell multi-omics integration are computationally expensive. Therefore, in this work, we have introduced a novel Unsupervised neural network for single cell Multi-omics INTegration (UMINT). UMINT serves as a promising model for integrating variable number of single cell omics layers with high dimensions. It has a light-weight architecture with substantially reduced number of parameters. The proposed model is capable of learning a latent low-dimensional embedding that can extract useful features from the data facilitating further downstream analyses. UMINT has been applied to integrate healthy and disease CITE-seq (paired RNA and surface proteins) datasets including a rare disease Mucosa-Associated Lymphoid Tissue (MALT) tumor. It has been benchmarked against existing state-of-the-art methods for single cell multi-omics integration. Furthermore, UMINT is capable of integrating paired single cell gene expression and ATAC-seq (Transposase-Accessible Chromatin) assays as well

Association among plasma levels of monocyte chemoattractant protein-1, traditional cardiovascular risk factors, and subclinical atherosclerosis

ObjectivesWe sought to evaluate the association between plasma levels of monocyte chemoattractant protein (MCP)-1 and the risk for subclinical atherosclerosis.BackgroundMonocyte chemoattractant protein is a chemokine that recruits monocytes into the developing atheroma and may contribute to atherosclerotic disease development and progression. Plasma levels of MCP-1 are independently associated with prognosis in patients with acute coronary syndromes, but few population-based data are available from subjects in earlier stages of atherosclerosis.MethodsIn the Dallas Heart Study, a population-based probability sample of adults in Dallas County ≤65 years old, plasma levels of MCP-1 were measured in 3,499 subjects and correlated with traditional cardiovascular risk factors, high-sensitivityC-reactive protein (hs-CRP), and coronary artery calcium (CAC) measured by electron beam computed tomography.ResultsHigher MCP-1 levels were associated with older age, white race, family history of premature coronary disease, smoking, hypertension, diabetes, hypercholesterolemia, and higher levels of hs-CRP (p < 0.01 for each). Similar associations were observed between MCP-1 and risk factors in the subgroup of participants without detectable CAC. Compared with the subjects in the lowest quartile of MCP-1, the odds of prevalent CAC (CAC score ≥10) for subjects in the second, third, and fourth quartiles were 1.30 (95% confidence interval [CI] 0.99 to 1.73), 1.60 (95% CI 1.22 to 2.11), and 2.02 (95% CI 1.54 to 2.63), respectively. The association between MCP-1 and CAC remained significant when adjusted for traditional cardiovascular risk factors, but not when further adjusted for age.ConclusionsIn a large population-based sample, plasma levels of MCP-1 were associated with traditional risk factors for atherosclerosis, supporting the hypothesis that MCP-1 may mediate some of the atherogenic effects of these risk factors. These findings support the potential role of MCP-1 as a biomarker target for drug development

Constraining the epoch of reionization with the variance statistic: simulations of the LOFAR case

Several experiments are underway to detect the cosmic redshifted 21-cm signal from neutral hydrogen from the Epoch of Reionization (EoR). Due to their very low signal-to-noise ratio, these observations aim for a statistical detection of the signal by measuring its power spectrum. We investigate the extraction of the variance of the signal as a first step towards detecting and constraining the global history of the EoR. Signal variance is the integral of the signal's power spectrum, and it is expected to be measured with a high significance. We demonstrate this through results from a simulation and parameter estimation pipeline developed for the Low Frequency Array (LOFAR)-EoR experiment. We show that LOFAR should be able to detect the EoR in 600 hours of integration using the variance statistic. Additionally, the redshift ($z_r$) and duration ($\Delta z$) of reionization can be constrained assuming a parametrization. We use an EoR simulation of $z_r = 7.68$ and $\Delta z = 0.43$ to test the pipeline. We are able to detect the simulated signal with a significance of 4 standard deviations and extract the EoR parameters as $z_r = 7.72^{+0.37}_{-0.18}$ and $\Delta z = 0.53^{+0.12}_{-0.23}$ in 600 hours, assuming that systematic errors can be adequately controlled. We further show that the significance of detection and constraints on EoR parameters can be improved by measuring the cross-variance of the signal by cross-correlating consecutive redshift bins.Comment: 13 pages, 14 figures, Accepted for publication in MNRA

Is a 3-mm intrafractional margin sufficient for daily image-guided intensity-modulated radiation therapy of prostate cancer?

PURPOSE: To determine whether a 3-mm isotropic target margin adequately covers the prostate and seminal vesicles (SVs) during administration of an intensity-modulated radiation therapy (IMRT) treatment fraction, assuming that daily image-guided setup is performed just before each fraction. MATERIALS AND METHODS: In-room computed tomographic (CT) scans were acquired immediately before and after a daily treatment fraction in 46 patients with prostate cancer. An eight-field IMRT plan was designed using the pre-fraction CT with a 3-mm margin and subsequently recalculated on the post-fraction CT. For convenience of comparison, dose plans were scaled to full course of treatment (75.6 Gy). Dose coverage was assessed on the post-treatment CT image set. RESULTS: During one treatment fraction (21.4+/-5.5 min), there were reductions in the volumes of the prostate and SVs receiving the prescribed dose (median reduction 0.1% and 1.0%, respectively, p\u3c0.001) and in the minimum dose to 0.1 cm(3) of their volumes (median reduction 0.5 and 1.5 Gy, p\u3c0.001). Of the 46 patients, three patients\u27 prostates and eight patients\u27 SVs did not maintain dose coverage above 70 Gy. Rectal filling correlated with decreased percentage-volume of SV receiving 75.6, 70, and 60 Gy (p\u3c0.02). CONCLUSIONS: The 3-mm intrafractional margin was adequate for prostate dose coverage. However, a significant subset of patients lost SV dose coverage. The rectal volume change significantly affected SV dose coverage. For advanced-stage prostate cancers, we recommend to use larger margins or improve organ immobilization (such as with a rectal balloon) to ensure SV coverage