Analysing Gene Networks with PDP Systems. Arabidopsis thaliana, a Case Study

Gene Regulatory Networks (GRNs) are a useful tool for biologists to understand the interactions among genes in living organisms. A special kind of GRNs known as Logic Networks (LNs) has been recently introduced. These networks consider that the state of one or more genes can in uence another one. In a previous work, we proposed a Membrane Computing model which simulates the dynamics of LNs by drawing on the improved LAPP algorithm. In this paper we provide a case study for our LN model on a network which regulates the circadian rhythms of long{term studied plant Arabidopsis thaliana. We outline the software tools employed and propose a methodology for analysing LNs on our Membrane Computing model. At the end of the paper, some conclusions and future work are included.Ministerio de Ciencia e Innovación TIN2012-37434Junta de Andalucía P08-TIC-0420

Identification of logic relationships between genes and subtypes of non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) has two major subtypes: adenocarcinoma (AC) and squamous cell carcinoma (SCC). The diagnosis and treatment of NSCLC are hindered by the limited knowledge about the pathogenesis mechanisms of subtypes of NSCLC. It is necessary to research the molecular mechanisms related with AC and SCC. In this work, we improved the logic analysis algorithm to mine the sufficient and necessary conditions for the presence states (presence or absence) of phenotypes. We applied our method to AC and SCC specimens, and identified [Formula: see text] lower and [Formula: see text] higher logic relationships between genes and two subtypes of NSCLC. The discovered relationships were independent of specimens selected, and their significance was validated by statistic test. Compared with the two earlier methods (the non-negative matrix factorization method and the relevance analysis method), the current method outperformed these methods in the recall rate and classification accuracy on NSCLC and normal specimens. We obtained [Formula: see text] biomarkers. Among [Formula: see text] biomarkers, [Formula: see text] genes have been used to distinguish AC from SCC in practice, and other six genes were newly discovered biomarkers for distinguishing subtypes. Furthermore, NKX2-1 has been considered as a molecular target for the targeted therapy of AC, and [Formula: see text] other genes may be novel molecular targets. By gene ontology analysis, we found that two biological processes ('epidermis development' and 'cell adhesion') were closely related with the tumorigenesis of subtypes of NSCLC. More generally, the current method could be extended to other complex diseases for distinguishing subtypes and detecting the molecular targets for targeted therapy

The recall rate of genes obtained by three methods.

<p>According to each method, we rank the genes in descending order by the coefficients of genes related with phenotypes. We selecte the top genes, where . The classification accuracy is calculated based on the top genes. ‘RA’, ‘NMF’ and ‘U’ represent the relevance analysis method, the non-negative matrix factorization method and the current method, respectively.</p

Data source.

<p>‘No.’ is the accession number from the Gene Expression Omnibus (GEO) database in NCBI; ‘n’ is the number of specimens; ‘—’ means there are no specimens from the corresponding data set.</p

25 genes are related with the subtypes of NSCLC.

<p>There are genes related with subtypes of NSCLC by lower logic relationships, and each gene attaches a coefficient. The genes are ranked according to coefficients in descending order. The top genes are selected to identify biomarkers. The blue nodes represent biomarkers identified in this work. The yellow nodes represent six genes which are not related with NSCLC on the NSCLC and normal specimens. The red nodes represent subtypes, i.e. AC and SCC.</p

Higher logic function of vectors and .

<p>‘’ denotes function symbol of type of higher logic relationships, where and represents the sign for the higher logic relationships.</p

Significant GO terms.

<p>‘P-value1’ and ‘P-value2’ denote the p-value scores of GO terms based on the subtypes of NSCLC data and NSCLC and normal data, respectively. ‘E1’ and ‘E2’ are the enrichment values of GO terms based on the subtypes of NSCLC data and NSCLC and normal data, respectively.</p

A Strengthened Dominance Relation Considering Convergence and Diversity for Evolutionary Many-Objective Optimization

Tian Y, Cheng R, Zhang X, Su Y, Jin Y. A Strengthened Dominance Relation Considering Convergence and Diversity for Evolutionary Many-Objective Optimization. IEEE Transactions on Evolutionary Computation. 2019;23(2):331-345.Both convergence and diversity are crucial to evolutionary many-objective optimization, whereas most existing dominance relations show poor performance in balancing them, thus easily leading to a set of solutions concentrating on a small region of the Pareto fronts. In this paper, a novel dominance relation is proposed to better balance convergence and diversity for evolutionary many-objective optimization. In the proposed dominance relation, an adaptive niching technique is developed based on the angles between the candidate solutions, where only the best converged candidate solution is identified to be nondominated in each niche. Experimental results demonstrate that the proposed dominance relation outperforms existing dominance relations in balancing convergence and diversity. A modified NSGA-II is suggested based on the proposed dominance relation, which shows competitiveness against the state-of-the-art algorithms in solving many-objective optimization problems. The effectiveness of the proposed dominance relation is also verified on several other existing multi- and many-objective evolutionary algorithms

A Strengthened Dominance Relation Considering Convergence and Diversity for Evolutionary Many-Objective Optimization

Both convergence and diversity are crucial to evolutionary many-objective optimization, whereas most existing dominance relations show poor performance in balancing them, thus easily leading to a set of solutions concentrating on a small region of the Pareto fronts. In this paper, a novel dominance relation is proposed to better balance convergence and diversity for evolutionary manyobjective optimization. In the proposed dominance relation, an adaptive niching technique is developed based on the angles between the candidate solutions, where only the best converged candidate solution is identified to be nondominated in each niche. Experimental results demonstrate that the proposed dominance relation outperforms existing dominance relations in balancing convergence and diversity. A modified NSGA-II is suggested based on the proposed dominance relation, which shows competitiveness against the state-of-the-art algorithms in solving many-objective optimization problems. The effectiveness of the proposed dominance relation is also verified on several other existing multi- and many-objective evolutionary algorithms