Analysis of gene expression data using self-organizing maps.

DNA microarray technologies together with rapidly increasing genomic sequence information is leading to an explosion in available gene expression data. Currently there is a great need for efficient methods to analyze and visualize these massive data sets. A self-organizing map (SOM) is an unsupervised neural network learning algorithm which has been successfully used for the analysis and organization of large data files. We have here applied the SOM algorithm to analyze published data of yeast gene expression and show that SOM is an excellent tool for the analysis and visualization of gene expression profiles.     

An SOM-based algorithm for optimization with dynamic weight updating

The self-organizing map (SOM), as a kind of unsupervised neural network, has been used for both static data management and dynamic data analysis. To further exploit its search abilities, in this paper we propose an SOM-based algorithm (SOMS) for optimization problems involving both static and dynamic functions. Furthermore, a new SOM weight updating rule is proposed to enhance the learning efficiency; this may dynamically adjust the neighborhood function for the SOM in learning system parameters. As a demonstration, the proposed SOMS is applied to function optimization and also dynamic trajectory prediction, and its performance compared with that of the genetic algorithm (GA) due to the similar ways both methods conduct searches.     

Immunological signature of the different clinical stages of the HTLV-1 infection: establishing serum biomarkers for HTLV-1-associated disease morbidity

Abstract

This study aimed at establishing the immunological signature and an algorithm for clinical management of the different clinical stages of the HTLV-1-infection based on serum biomarkers. A panel of serum biomarkers was evaluated by four sets of innovative/non-conventional data analysis approaches in samples from 87 HTLV-1 patients: asymptomatic carriers (AC), putative HTLV-1 associated myelopathy/tropical spastic paraparesis (pHAM/TSP) and HAM/TSP. The analysis of cumulative curves and molecular signatures pointed out that HAM/TSP presented a pro-inflammatory profile mediated by CXCL10/LTB-4/IL-6/TNF-α/IFN-γ, counterbalanced by IL-4/IL-10. The analysis of biomarker networks showed that AC presented a strongly intertwined pro-inflammatory/regulatory net with IL-4/IL-10 playing a central role, while HAM/TSP exhibited overall immune response toward a predominant pro-inflammatory profile. At last, the classification and regression trees proposed for clinical practice allowed for the construction of an algorithm to discriminate AC, pHAM and HAM/TSP patients with the elected biomarkers: IFN-γ, TNF-α, IL-10, IL-6, IL-4 and CysLT. These findings reveal a complex interaction among chemokine/leukotriene/cytokine in HTLV-1 infection and suggest the use of the selected but combined biomarkers for the follow-up/diagnosis of disease morbidity of HTLV-1-infected individuals.     

Ordering process of self-organizing maps improved by asymmetric neighborhood function

The Self-organizing map (SOM) is an unsupervised learning method based on the neural computation, which has found wide applications. However, the learning process sometime takes multi-stable states, within which the map is trapped to an undesirable disordered state including topological defects on the map. These topological defects critically aggravate the performance of the SOM. In order to overcome this problem, we propose to introduce an asymmetric neighborhood function for the SOM algorithm. Compared with the conventional symmetric one, the asymmetric neighborhood function accelerates the ordering process even in the presence of the defect. However, this asymmetry tends to generate a distorted map. This can be suppressed by an improved method of the asymmetric neighborhood function. In the case of one-dimensional SOM, it is found that the required steps for perfect ordering is numerically shown to be reduced from O(N ³) to O(N ²). We also discuss the ordering process of a twisted state in two-dimensional SOM, which can not be rectified by the ordinary symmetric neighborhood function.     

Analysis of codon usage diversity of bacterial genes with a self-organizing map (SOM): characterization of horizontally transferred genes with emphasis on the E. coli O157 genome

With increases in the amounts of available DNA sequence data, it has become increasingly important to develop tools for comprehensive systematic analysis and comparison of species-specific characteristics of protein-coding sequences for a wide variety of genomes. In the present study, we used a novel neural-network algorithm, a self-organizing map (SOM), to efficiently and comprehensively analyze codon usage in approximately 60,000 genes from 29 bacterial species simultaneously. This SOM makes it possible to cluster and visualize genes of individual species separately at a much higher resolution than can be obtained with principal component analysis. The organization of the SOM can be explained by the genome G+C% and tRNA compositions of the individual species. We used SOM to examine codon usage heterogeneity in the E. coli O157 genome, which contains 'O157-unique segments' (O-islands), and showed that SOM is a powerful tool for characterization of horizontally transferred genes.     

Mapping soil organic matter using the topographic wetness index: A comparative study based on different flow-direction algorithms and kriging methods

Terrain attributes derived from digital elevation models have been used widely for mapping soil organic matter (SOM). Among these attributes, the topographic wetness index (TWI), an index for quantitatively indicating the balance between water accumulation and drainage conditions at the local scale, has been shown to correlate with SOM. However, TWIs used in most studies are calculated using a single-flow-direction (SFD) algorithm, which assumes that all water from a grid cell flows into only one neighboring cell. This assumption is not always valid, especially in areas with low relief where movement of water may be divergent. To overcome this SFD limitation, a multiple-flow-direction (MFD) algorithm has been developed, which distributes flow from a grid cell to several downslope neighbors. In this study we compared the effect of TWI calculations based on SFD and MFD in predictive mapping of SOM by incorporating them into different kriging methods over a 51.76 km² area in Nenjiang County of northeastern China. We found that the MFD-based TWI was better correlated with SOM than was the SFD-based index. We then compared the accuracies of SOM maps which were derived from MFD-based TWI and SFD-based TWI incorporated by ordinary kriging (OK), simple kriging with varying local means (SKlm), kriging with external drift (KED) and collocated cokriging (CC). The MFD-based TWI, used as a secondary variable in SKlm and CC, outperforms the SFD-based TWI. For the different kriging methods, CC (incorporating either MFD-based TWI or SFD-based TWI) showed the best performance, and OK generated a better result than SKlm and KED. Both the MFD-based TWI and SFD-based TWI proved to be incompatible with KED and SKlm due to their numerical instability caused by the rough TWI surfaces. Among all predictive methods, CC incorporating the MFD-based TWI produced the best results. This is because: (1) the MFD-based TWI is best able to indicate quantitatively soil moisture and therefore has the strongest correlation with SOM; (2) CC is capable of utilizing effectively the spatial auto-correlation of SOM and the cross-correlation between SOM and the MFD-based TWI.     

A Discrete Fruit Fly Optimization Algorithm for the Traveling Salesman Problem

The fruit fly optimization algorithm (FOA) is a newly developed bio-inspired algorithm. The continuous variant version of FOA has been proven to be a powerful evolutionary approach to determining the optima of a numerical function on a continuous definition domain. In this study, a discrete FOA (DFOA) is developed and applied to the traveling salesman problem (TSP), a common combinatorial problem. In the DFOA, the TSP tour is represented by an ordering of city indices, and the bio-inspired meta-heuristic search processes are executed with two elaborately designed main procedures: the smelling and tasting processes. In the smelling process, an effective crossover operator is used by the fruit fly group to search for the neighbors of the best-known swarm location. During the tasting process, an edge intersection elimination (EXE) operator is designed to improve the neighbors of the non-optimum food location in order to enhance the exploration performance of the DFOA. In addition, benchmark instances from the TSPLIB are classified in order to test the searching ability of the proposed algorithm. Furthermore, the effectiveness of the proposed DFOA is compared to that of other meta-heuristic algorithms. The results indicate that the proposed DFOA can be effectively used to solve TSPs, especially large-scale problems.     

An efficient harris hawk optimization algorithm for solving the travelling salesman problem

Cluster Computing - Travelling Salesman Problem (TSP) is an Np-Hard problem, for which various solutions have been offered so far. Using the Harris Hawk Optimization (HHO) algorithm, this paper...     

Optimal HP configurations of proteins by combining local search with elastic net algorithm

The prediction of protein conformation from its amino-acid sequence is one of the most prominent problems in computational biology. But it is NP-hard. Here, we focus on an abstraction widely studied of this problem, the two-dimensional hydrophobic-polar protein folding problem (2D HP PFP). Mathematical optimal model of free energy of protein is established. Native conformations are often sought using stochastic sampling methods, but which are slow. The elastic net (EN) algorithm is one of fast deterministic methods as travelling salesman problem (TSP) strategies. However, it cannot be applied directly to protein folding problem, because of fundamental differences in the two types of problems. In this paper, how the 2D HP protein folding problem can be framed in terms of TSP is shown. Combination of the modified elastic net algorithm and novel local search method is adopted to solve this problem. To our knowledge, this is the first application of EN algorithm to 2D HP model. The results indicate that our approach can find more optimal conformations and is simple to implement, computationally efficient and fast.     

Optimal HP configurations of proteins by combining local search with elastic net algorithm

The prediction of protein conformation from its amino-acid sequence is one of the most prominent problems in computational biology. But it is NP-hard. Here, we focus on an abstraction widely studied of this problem, the two-dimensional hydrophobic-polar protein folding problem (2D HP PFP). Mathematical optimal model of free energy of protein is established. Native conformations are often sought using stochastic sampling methods, but which are slow. The elastic net (EN) algorithm is one of fast deterministic methods as travelling salesman problem (TSP) strategies. However, it cannot be applied directly to protein folding problem, because of fundamental differences in the two types of problems. In this paper, how the 2D HP protein folding problem can be framed in terms of TSP is shown. Combination of the modified elastic net algorithm and novel local search method is adopted to solve this problem. To our knowledge, this is the first application of EN algorithm to 2D HP model. The results indicate that our approach can find more optimal conformations and is simple to implement, computationally efficient and fast.     

Minimum energy configurations of the 2-dimensional HP-model of proteins by self-organizing networks.

We use self-organizing maps (SOM) as an efficient tool to find the minimum energy configurations of the 2-dimensional HP-models of proteins. The usage of the SOM for the protein folding problem is similar to that for the Traveling Salesman Problem. The lattice nodes represent the cities whereas the neurons in the network represent the amino acids moving towards the closest cities, subject to the HH interactions. The valid path that maximizes the HH contacts corresponds to the minimum energy configuration of the protein. We report promising results for the cases when the protein completely fills a lattice and discuss the current problems and possible extensions. In all the test sequences up to 36 amino acids, the algorithm was able to find the global minimum and its degeneracies.     

Evolution in a chronic RNA virus infection: selection on HTLV-I tax protein differs between healthy carriers and patients with tropical spastic paraparesis

HTLV-I causes T-cell leukemia and tropical spastic paraparesis (TSP) in a minority of infected people, whereas the majority remain healthy. The virus differs little in sequence between isolates but has been shown to have a quasispecies structure. Using the Nei and Gojobori algorithm, we have shown that the proportion of nonsynonymous to synonymous changes in HTLV-I proviral tax gene sequences from healthy seropositive subjects (Dn/Ds= 0.9 to 1.3) is significantly higher than those from TSP patients (Dn/Ds= 0.3 to 0.6). Here we show that the distinction between healthy seropositives and TSP patients can only be seen with proviral tax sequences, but not with cDNA, the amino-terminal or carboxy-terminal half of tax, or the rex gene. The Dn/Ds ratio of proviral tax sequences was used to analyze two TSP patients with atypical features and to investigate the influence of cytotoxic T cells (CTL) on the viral quasispecies. Received: 18 January 1995 / Accepted: 7 November 1995     

Dinocampus (=Perilitus) coccinellae teratocyte-specific polypeptide: its accumulative property,localization and characterization

Dinocampus (=Perilitus) coccinellae (Braconidae: Hymenoptera) teratocytes synthesize a teratocyte-specific polypeptide (TSP) with a high molecular weight of 540kDa. The TSP has a tendency to accumulate in the teratocyte cells without release after synthesis ([Okuda and Kadono-Okuda, 1995]), which was confirmed in this study. Pulse-chase fluorography indicated that teratocytes at a younger stage (6 days after parasitization)secreted negligible TSP into the medium after synthesis, while teratocytes at an older stage (11 days after parasitization)secreted the synthesized products into the medium, although the amount released was still low. Western blot with anti-TSP serum showed that only a small amount of TSP appeared in the parasitized host hemolymph, even when TSP synthesis by teratocytes was actively taking place, which also supported the accumulative nature of TSP. The immunoelectronmicroscopic studies revealed that the TSP was localized specifically in high electron-dense vacuoles. Lectin blot analysis identified TSP as a high mannose glycoprotein. The amino acid composition of the major subunit of the TSP was quite similar to that of nutritive proteins such as vitellogenin and storage proteins of some insects. These characterization data, together with the accumulation property of the TSP indicates that Dinocampus teratocyte primarily plays a nutritive role for the developing parasitoid larvae. TSP exhibited esterase activity, which indicates that TSP may have an additional function in the host-parasitoid reaction.     

Formation of a direction map by projection learning using Kohonen's self-organization map

 In this paper, we propose a modification of Kohonen's self-organization map (SOM) algorithm. When the input signal space is not convex, some reference vectors of SOM can protrude from it. The input signal space must be convex to keep all the reference vectors fixed on it for any updates. Thus, we introduce a projection learning method that fixes the reference vectors onto the input signal space. This version of SOM can be applied to a non-convex input signal space. We applied SOM with projection learning to a direction map observed in the primary visual cortex of area 17 of ferrets, and area 18 of cats. Neurons in those areas responded selectively to the orientation of edges or line segments, and their directions of motion. Some iso-orientation domains were subdivided into selective regions for the opposite direction of motion. The abstract input signal space of the direction map described in the manner proposed by Obermayer and Blasdel [(1993) J Neurosci 13: 4114–4129] is not convex. We successfully used SOM with projection learning to reproduce a direction-orientation joint map. Received: 29 September 2000 / Accepted: 7 March 2001     

Characterization of mouse thrombospondin 2 sequence and expression during cell growth and development.

Thrombospondin (TSP) is an extracellular matrix glycoprotein whose expression has been associated with a variety of cellular processes including growth and embryogenesis. The recent discovery of the existence of a second mouse TSP gene necessitates careful examination of the discrete biochemical and functional properties associated with each molecule. In this report, the primary structures of human TSP, mouse TSP1 (mTSP1), mouse TSP2 (mTSP2), and chicken TSP are compared; and the expression of mTSP1 and mTSP2 during embryogenesis and growth factor-mediated cell proliferation is examined. The cloning and sequencing of the entire coding regions of mTSP1 and mTSP2 revealed considerable conservation of residues critical for TSP structure and function; these data suggest that TSP2 is capable of trimer formation and many of the same cell-surface and ligand interactions that mediate TSP function. Comparison of the various TSP sequences also allowed the assignment based on sequence homology of previously reported human TSP as TSP1 and chicken TSP as TSP2. mTSP2, like mTSP1, was shown to be a primary response gene when quiescent Swiss 3T3 cells were stimulated with serum, platelet-derived growth factor BB, basic fibroblast growth factor, or interleukin-1 beta. Interestingly, TSP1 and TSP2 exhibited markedly different tissue- and stage-specific patterns of mRNA expression during mouse embryogenesis, implying that the two TSP molecules possess discrete functional properties important for development. Additionally, the TSP genes (Thbs1 and Thbs2) were mapped to single loci on mouse chromosomes 2 and 17, respectively.     

Thrombospondin stimulates motility of human neutrophils

Polymorphonuclear leukocytes (PMNs) migrate to sites of inflammation or injury in response to chemoattractants released at those sites. The presence of extracellular matrix (ECM) proteins at these sites may influence PMN accumulation at blood vessel walls and enhance their ability to move through tissue. Thrombospondin (TSP), a 450-kD ECM protein whose major proteolytic fragments are a COOH-terminal 140-kD fragment and an NH2-terminal heparin-binding domain (HBD), is secreted by platelets, endothelial cells, and smooth muscle cells. TSP binds specifically to PMN surface receptors and has been shown, in other cell types, to promote directed movement. TSP in solution at low concentrations (30-50 nM) "primed" PMNs for f-Met-Leu-Phe (fMLP)- mediated chemotaxis, increasing the response two- to fourfold. A monoclonal antibody against the HBD of TSP totally abolished this priming effect suggesting that the priming activity resides in the HBD of TSP. Purified HBD retains the priming activity of TSP thereby corroborating the antibody data. TSP alone, in solution at high concentrations (0.5-3.0 microM), stimulated chemotaxis of PMNs and required both the HBD and the 140-kD fragment of TSP. In contrast to TSP in solution, TSP bound to nitrocellulose filters in the range of 20- 70 pmol stimulated random locomotion of PMNs. The number of PMNs migrating in response to bound TSP was approximately two orders of magnitude greater than the number of cells that exhibited chemotaxis in response to soluble TSP or fMLP. Monoclonal antibody C6.7, which recognizes an epitope near the carboxyl terminus of TSP, blocked migration stimulated by bound TSP, suggesting that the activity resides in this domain. Using proteolytic fragments, we demonstrated that bound 140-kD fragment, but not HBD, promoted migration of PMNs. Therefore, TSP released at injury sites, alone or in synergy with chemotactic peptides like fMLP, could play a role in directing PMN movement.     

Nonparametric density estimation and regression achieved with topographic maps maximizing the information-theoretic entropy of their outputs

We introduce an unsupervised competitive learning rule, called the extended Maximum Entropy learning Rule (eMER), for topographic map formation. Unlike Kohonen's Self-Organizing Map (SOM) algorithm, the presence of a neighborhood function is not a prerequisite for achieving topology-preserving mappings, but instead it is intended: (1) to speed up the learning process and (2) to perform nonparametric regression. We show that, when the neighborhood function vanishes, the neural weigh t density at convergence approaches a linear function of the input density so that the map can be regarded as a nonparametric model of the input density. We apply eMER to density estimation and compare its performance with that of the SOM algorithm and the variable kernel method. Finally, we apply the ‘batch’ version of eMER to nonparametric projection pursuit regression and compare its performance with that of back-propagation learning, projection pursuit learning, constrained topolog ical mapping, and the Heskes and Kappen approach. Received: 12 August 1996 / Accepted in revised form: 9 April 1997     

Zones of influence for soil organic matter dynamics: A conceptual framework for data and models

Soil organic matter (SOM) is an indicator of sustainable land management as stated in the global indicator framework of the United Nations Sustainable Development Goals (SDG Indicator 15.3.1). Improved forecasting of future changes in SOM is needed to support the development of more sustainable land management under a changing climate. Current models fail to reproduce historical trends in SOM both within and during transition between ecosystems. More realistic spatio‐temporal SOM dynamics require inclusion of the recent paradigm shift from SOM recalcitrance as an ‘intrinsic property’ to SOM persistence as an ‘ecosystem interaction’. We present a soil profile, or pedon‐explicit, ecosystem‐scale framework for data and models of SOM distribution and dynamics which can better represent land use transitions. Ecosystem‐scale drivers are integrated with pedon‐scale processes in two zones of influence. In the upper vegetation zone, SOM is affected primarily by plant inputs (above‐ and belowground), climate, microbial activity and physical aggregation and is prone to destabilization. In the lower mineral matrix zone, SOM inputs from the vegetation zone are controlled primarily by mineral phase and chemical interactions, resulting in more favourable conditions for SOM persistence. Vegetation zone boundary conditions vary spatially at landscape scales (vegetation cover) and temporally at decadal scales (climate). Mineral matrix zone boundary conditions vary spatially at landscape scales (geology, topography) but change only slowly. The thicknesses of the two zones and their transport connectivity are dynamic and affected by plant cover, land use practices, climate and feedbacks from current SOM stock in each layer. Using this framework, we identify several areas where greater knowledge is needed to advance the emerging paradigm of SOM dynamics—improved representation of plant‐derived carbon inputs, contributions of soil biota to SOM storage and effect of dynamic soil structure on SOM storage—and how this can be combined with robust and efficient soil monitoring.     

Activation of human neutrophils increases thrombospondin receptor expression.

Thrombospondin (TSP), a 450-kDa extracellular matrix protein secreted by platelets may be instrumental in triggering polymorphonuclear leukocyte (PMN) activation and mediating PMN-endothelial cell interactions. TSP alone had no effect on O-2 generation but caused a significant increase in the chemoattractant FMLP-mediated O-2 generation. Purified HBD, but not the 140-kDa COOH-terminal fragment of TSP, retained the priming activity indicating that the priming effect was mediated through the HBD of TSP. The priming of FMLP-mediated O-2 generation by TSP, and our recent studies demonstrating that TSP stimulates PMN adhesion and motility suggested the presence of specific receptors for TSP on PMN. Binding studies on unactivated PMN, using 125I-TSP and competition with excess unlabeled TSP, demonstrated 2.4 x 10(3) binding sites/cell with an apparent Kd of 7 nM. Heparin did not compete for binding as effectively as unlabeled TSP. There were 1.5 x 10(3) heparin-inhibitable binding sites/cell with an apparent Kd of 8 nM that represented approximately 60% of the TSP-specific sites. Therefore, two distinct TSP receptors appeared to exist on unactivated PMN; one interacting with the heparin-binding domain of TSP and one interacting with a different site. Treating PMN with cytochalasin B followed by FMLP caused a 30-fold increase in TSP receptor expression. Binding studies on activated PMNs revealed 7.6 x 10(4) sites/cell; 60% of which were heparin inhibitable. The majority (5.3 x 10(4) sites/cell) of receptors expressed had an affinity of approximately 20 nM. About 50% of these sites were heparin inhibitable. In addition, there were 2.3 x 10(4) higher affinity sites/cell with an apparent Kd of 6 nM. Heparin-inhibitable sites comprised 70% of the higher affinity sites. The existence of a subset of TSP receptors that were heparin-inhibitable on PMN suggests that binding of TSP may trigger functionally independent responses. Increased receptor expression and expression of two high affinity binding sites following PMN activation may modulate PMN-endothelium or PMN-basement membrane interactions localized at the blood vessel wall.