Differential co-expression framework to quantify goodness of biclusters and compare biclustering algorithms

<p>Abstract</p> <p>Background</p> <p>Biclustering is an important analysis procedure to understand the biological mechanisms from microarray gene expression data. Several algorithms have been proposed to identify biclusters, but very little effort was made to compare the performance of different algorithms on real datasets and combine the resultant biclusters into one unified ranking.</p> <p>Results</p> <p>In this paper we propose differential co-expression framework and a differential co-expression scoring function to objectively quantify quality or goodness of a bicluster of genes based on the observation that genes in a bicluster are co-expressed in the conditions belonged to the bicluster and not co-expressed in the other conditions. Furthermore, we propose a scoring function to stratify biclusters into three types of co-expression. We used the proposed scoring functions to understand the performance and behavior of the four well established biclustering algorithms on six real datasets from different domains by combining their output into one unified ranking.</p> <p>Conclusions</p> <p>Differential co-expression framework is useful to provide quantitative and objective assessment of the goodness of biclusters of co-expressed genes and performance of biclustering algorithms in identifying co-expression biclusters. It also helps to combine the biclusters output by different algorithms into one unified ranking i.e. meta-biclustering.</p

SFSSClass: an integrated approach for miRNA based tumor classification

Background: MicroRNA (miRNA) expression profiling data has recently been found to be particularly important in cancer research and can be used as a diagnostic and prognostic tool. Current approaches of tumor classification using miRNA expression data do not integrate the experimental knowledge available in the literature. A judicious integration of such knowledge with effective miRNA and sample selection through a biclustering approach could be an important step in improving the accuracy of tumor classification. Results: In this article, a novel classification technique called SFSSClass is developed that judiciously integrates a biclustering technique SAMBA for simultaneous feature (miRNA) and sample (tissue) selection (SFSS), a cancer-miRNA network that we have developed by mining the literature of experimentally verified cancer-miRNA relationships and a classifier uncorrelated shrunken centroid (USC). SFSSClass is used for classifying multiple classes of tumors and cancer cell lines. In a part of the investigation, poorly differentiated tumors (PDT) having non diagnostic histological appearance are classified while training on more differentiated tumor (MDT) samples. The proposed method is found to outperform the best known accuracy in the literature on the experimental data sets. For example, while the best accuracy reported in the literature for classifying PDT samples is similar to 76.5%, the accuracy of SFSSClass is found to be similar to 82.3%. The advantage of incorporating biclustering integrated with the cancer-miRNA network is evident from the consistently better performance of SFSSClass (integration of SAMBA, cancer-miRNA network and USC) over USC (eg., similar to 70.5% for SFSSClass versus similar to 58.8% in classifying a set of 17 MDT samples from 9 tumor types, similar to 91.7% for SFSSClass versus similar to 75% in classifying 12 cell lines from 6 tumor types and similar to 382.3% for SFSSClass versus similar to 41.2% in classifying 17 PDT samples from 11 tumor types). Conclusion: In this article, we develop the SFSSClass algorithm which judiciously integrates a biclustering technique for simultaneous feature (miRNA) and sample (tissue) selection, the cancer-miRNA network and a classifier. The novel integration of experimental knowledge with computational tools efficiently selects relevant features that have high intra-class and low interclass similarity. The performance of the SFSSClass is found to be significantly improved with respect to the other existing approaches

The present-day number of tectonic plates

The number of tectonic plates on Earth described in the literature has expanded greatly since the start of the plate tectonic era, when only about a dozen plates were considered in global models of present-day plate motions. With new techniques of more accurate earthquake epicenter locations, modern ways of measuring ocean bathymetry using swath mapping, and the use of space based geodetic techniques, there has been a huge growth in the number of plates thought to exist. The study by Bird (2003) proposed 52 plates, many of which were delineated on the basis of earthquake locations. Because of the pattern of areas of these plates, he suggested that there should be more small plates than he could identify. In this paper, I gather together publications that have proposed a total of 107 new plates, giving 159 plates in all. The largest plate (Pacific) is about 20 % of the Earth's area or 104 Mm (super 2) , and the smallest of which (Plate number 5 from Hammond et al. 2011) is only 273 km (super 2) in area. Sorting the plates by size allows us to investigate how size varies as a function of order. There are several changes of slope in the plots of plate number organized by size against plate size order which are discussed. The sizes of the largest seven plates is constrained by the area of the Earth. A middle set of 73 plates down to an area of 97,563 km (super 2) (the Danakil plate at number 80, is the plate of median size) follows a fairly regular pattern of plate size as a function of plate number. For smaller plates, there is a break in the slope of the plate size/plate number plot and the next 32 plates follow a pattern of plate size proposed by the models of Koehn et al. (2008) down to an area of 11,638 km (super 2) (West Mojave plate # 112). Smaller plates do not follow any regular pattern of area as a function of plate number, probably because we have not sampled enough of these very small plates to reveal any clear pattern. Copyright 2016 The Author(s) and Harrison

Discovery of MK-1421, a Potent, Selective sstr3 Antagonist, as a Development Candidate for Type 2 Diabetes

The imidazolyl-tetrahydro-β-carboline class of sstr3 antagonists have demonstrated efficacy in a murine model of glucose excursion and may have potential as a treatment for type 2 diabetes. The first candidate in this class caused unacceptable QTc interval prolongation in oral, telemetrized cardiovascular (CV) dogs. Herein, we describe our efforts to identify an acceptable candidate without CV effects. These efforts resulted in the identification of (1<i>R</i>,3<i>R</i>)-3-(4-(5-fluoropyridin-2-yl)-1<i>H</i>-imidazol-2-yl)-1-(1-ethyl-pyrazol-4-yl)-1-(3-methyl-1,3,4-oxadiazol-3<i>H</i>-2-one-5-yl)-2,3,4,9-tetrahydro-1<i>H</i>-β-carboline (<b>17e</b>, MK-1421)