Intelligent Microarray Data Analysis through Non-negative Matrix Factorization to Study Human Multiple Myeloma Cell Lines

Gene Expression Analysis through Parallel Non-Negative Matrix Factorization

Genetic expression analysis is a principal tool to explain the behavior of genes in an organism when exposed to different experimental conditions. In the state of art, many clustering algorithms have been proposed. It is overwhelming the amount of biological data whose high-dimensional structure exceeds mostly current computational architectures. The computational time and memory consumption optimization actually become decisive factors in choosing clustering algorithms. We propose a clustering algorithm based on Non-negative Matrix Factorization and K-means to reduce data dimensionality but whilst preserving the biological context and prioritizing gene selection, and it is implemented within parallel GPU-based environments through the CUDA library. A well-known dataset is used in our tests and the quality of the results is measured through the Rand and Accuracy Index. The results show an increase in the acceleration of 6.22× compared to the sequential version. The algorithm is competitive in the biological datasets analysis and it is invariant with respect to the classes number and the size of the gene expression matrix.

A Block Coordinate Descent-Based Projected Gradient Algorithm for Orthogonal Non-Negative Matrix Factorization

This article uses the projected gradient method (PG) for a non-negative matrix factorization problem (NMF), where one or both matrix factors must have orthonormal columns or rows. We penalize the orthonormality constraints and apply the PG method via a block coordinate descent approach. This means that at a certain time one matrix factor is fixed and the other is updated by moving along the steepest descent direction computed from the penalized objective function and projecting onto the space of non-negative matrices. Our method is tested on two sets of synthetic data for various values of penalty parameters. The performance is compared to the well-known multiplicative update (MU) method from Ding (2006), and with a modified global convergent variant of the MU algorithm recently proposed by Mirzal (2014). We provide extensive numerical results coupled with appropriate visualizations, which demonstrate that our method is very competitive and usually outperforms the other two methods.

Explaining Ovarian Cancer Gene Expression Profiles with Fuzzy Rules and Genetic Algorithms

The analysis of gene expression data is a complex task, and many tools and pipelines are available to handle big sequencing datasets for case-control (bivariate) studies. In some cases, such as pilot or exploratory studies, the researcher needs to compare more than two groups of samples consisting of a few replicates. Both standard statistical bioinformatic pipelines and innovative deep learning models are unsuitable for extracting interpretable patterns and information from such datasets. In this work, we apply a combination of fuzzy rule systems and genetic algorithms to analyze a dataset composed of 21 samples and 6 classes, useful for approaching the study of expression profiles in ovarian cancer, compared to other ovarian diseases. The proposed method is capable of performing a feature selection among genes that is guided by the genetic algorithm, and of building a set of if-then rules that explain how classes can be distinguished by observing changes in the expression of selected genes. After testing several parameters, the final model consists of 10 genes involved in the molecular pathways of cancer and 10 rules that correctly classify all samples.

An attempt to chemically state the cross-talk between monomers of COX homodimers by double/hybrid inhibitors mofezolac-spacer-mofezolac and mofezolac-spacer-arachidonic acid

Cardiovascular diseases (CVDs) account for over 17 million death globally each year, including arterial thrombosis. Platelets are key components in the pathogenesis of this disease and modulating their activity is an effective strategy to treat such thrombotic events. Cyclooxygenase-1 (COX-1) isoenzyme is involved in platelet activation and is the main target of non-steroidal anti-inflammatory drugs (NSAIDs) and new selective inhibitor research. Inhibitors of general formula mofezolac-spacer-mofezolac (mof-spacer-mof) and mofezolac-spacer-arachidonic acid (mof-spacer-AA) were projected to investigate the possible cross-talk between the two monomers (E_allo and E_cat) forming the COX-1 homodimer. Mofezolac was chosen as either one or two moieties of these molecules being the known most potent and selective COX-1 inhibitor and administrated to humans as Disopain™, then arachidonic acid (AA) was used to develop molecules bearing, in the same compound, in addition to the inhibitor moiety (mofezolac) also the natural COX substrate. Depending on the nature of the spacer, COX-1 and COX-2 activity was differently inhibited by mof-spacer-mof set with a preferential COX-1 inhibition. The highest COX-1 selectivity was exhibited by the compound in which the spacer was the benzidine [N,N'-(biphenyl-4,4'-di-yl)bis (2-[3,4-bis(4-methoxyphenyl)isoxazol-5-yl]acetamide) (15): COX-1 IC₅₀ = 0.08 μM, COX-2 IC₅₀ > 50 μM, Selectivity Index (SI) > 625]. In the case of mof-spacer-AA set, the COX inhibitory potency and also the isoform preference changed. (5Z, 8Z, 11Z, 14Z)-N-(4-{2-[3,4-Bis(4-methoxyphenyl)isoxazol-5-yl]acetamido}butyl)icosa-5,8,11,14-tetraenamide (19) and (5Z, 8Z, 11Z, 14Z)-N-(4'-{2-[3,4-bis(4-methoxyphenyl)isoxazol-5-yl]acetamido}-[1,1'-biphenyl]-4-yl)icosa-5,8,11,14-tetraenamide (21), in which the spacer is the 1,2-diaminobutane or benzidine, respectively, selectively inhibited the COX-2, whereas when the spacer is the 1,4-phenylendiamine [(5Z, 8Z, 11Z, 14Z)-N-(4-{2-[3,4-bis(4-methoxyphenyl)isoxazol-5-yl]acetamido}phenyl)icosa-5,8,11,14-tetraenamide) (20) the COX preference is COX-1 (COX-1 IC₅₀ = 0.05 μM, COX-2 IC₅₀ > 50 μM, with a COX-1 selectivity > 1000). Molecular modelling by using FLAP algorithm shows fundamental interactions of the novel compounds at the entry channel of COX and inside its catalytic cavity. The effect of these mof-spacer-mof and mof-spacer-AA in inhibiting in vitro free arachidonic acid-induced platelet aggregation was also determined. A positive profile of hemocompatibility in relation to their influence on the blood coagulation cascade and erythrocyte toxicity was observed. Cytotoxicity and genotoxicity safety were also found for these two novel sets of compounds.