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We previously reported the neurotoxic effects of arsenic in the hippocampus. Here, we explored the involvement of Wnt pathway, which contributes to neuronal functions. Administering environmentally relevant arsenic concentrations to postnatal day-60 (PND60) mice demonstrated a dose-dependent increase in hippocampal Wnt3a and its components, Frizzled, phospho-LRP6, Dishevelled and Axin1 at PND90 and PND120. However, p -GSK3-β(Ser9) and β-catenin levels although elevated at PND90, decreased at PND120. Additionally, treatment with Wnt-inhibitor, rDkk1, reduced p -GSK3-β(Ser9) and β-catenin at PND90, but failed to affect their levels at PND120, indicating a time-dependent link with Wnt. To explore other underlying factors, we assessed epidermal growth factor receptor (EGFR) pathway, which interacts with GSK3-β and appears relevant to neuronal functions. We primarily found that arsenic reduced hippocampal phosphorylated-EGFR and its ligand, Heparin-binding EGF-like growth factor (HB-EGF), at both PND90 and PND120. Moreover, treatment with HB-EGF rescued p -GSK3-β(Ser9) and β-catenin levels at PND120, suggesting their HB-EGF/EGFR-dependent regulation at this time point. Additionally, rDkk1, LiCl (GSK3-β-activity inhibitor), or β-catenin protein treatments induced a time-dependent recovery in HB-EGF, indicating potential inter-dependent mechanism between hippocampal Wnt/β-catenin and HB-EGF/EGFR following arsenic exposure. Fluorescence immunolabeling then validated these findings in hippocampal neurons. Further exploration of hippocampal neuronal survival and apoptosis demonstrated that treatment with rDkk1, LiCl, β-catenin and HB-EGF improved Nissl staining and NeuN levels, and reduced cleaved-caspase-3 levels in arsenic-treated mice. Supportively, we detected improved Y-Maze and Passive Avoidance performances for learning-memory functions in these mice. Overall, our study provides novel insights into Wnt/β-catenin and HB-EGF/EGFR pathway interaction in arsenic-induced hippocampal neurotoxicity. [Display omitted] • A novel mechanism of arsenic-induced hippocampal neurotoxicity is proposed. • The mechanism includes an interlinked Wnt3a and HB-EGF/EGFR signaling pathway. • GSK3β/β-catenin participated via time-dependent association with Wnt3a and HB-EGF. • This arsenic-induced unique pathway caused neuronal loss and cognitive deficit. • Hence, the pathway may be targeted in future to mitigate hippocampal neurotoxicity. [ABSTRACT FROM AUTHOR]

Abstract: We investigate the relationship between mortality decline and urbanization, which has hitherto been proposed by demographers but has yet to be tested rigorously in a global context. Using cross‐national panel data, we find evidence of a robust negative correlation between crude death rates and urbanization. The use of instrumental variables suggest that this relationship is causal, while historical data from the early 20th century suggest that this relationship holds in earlier periods as well. Finally, we find robust evidence that mortality decline is correlated with urbanization through the creation of new cities rather than promoting urban growth in already‐extant cities. [ABSTRACT FROM AUTHOR]

Exploiting user trust information for developing a recommendation system has gained increasing research interest in recent years. Due to the exchange of opinions about items over the social network, trust plays a crucial role for a user to like or dislike an item. Graph Neural Networks (GNNs), which have the intrinsic power of integrating node information and topological structure, have a high potential to advance the field of trust-aware social recommendation. However, as of now, this area is little explored, with most of the existing GNN-based models ignoring the trust propagation and trust composition properties. To address this issue, in this paper, we propose a novel GNN-based framework that can capture such trust propagation and trust composition aspects by incorporating the concept of 'user-reliability.' Our proposed user-reliability-aware social recommendation framework, termed as SoURA, generates the user-embedding and item-embedding with consideration to the user-reliability values, which, in turn, helps in better evaluation of the user trust. Experimental evaluations on the benchmark Ciao and Epinion datasets demonstrate the effectiveness of incorporating user-reliability for finding user-embedding and item embedding in a social recommendation system. The proposed SoURA is found to show a minimum of 25% improvement over the state-of-the-art GNN-based recommendation algorithms. [ABSTRACT FROM AUTHOR]

Background: Predicting novel interactions between HIV-1 and human proteins contributes most promising area in HIV research. Prediction is generally guided by some classification and inference based methods using single biological source of information. Results: In this article we have proposed a novel framework to predict protein-protein interactions (PPIs) between HIV-1 and human proteins by integrating multiple biological sources of information through non negative matrix factorization (NMF). For this purpose, the multiple data sets are converted to biological networks, which are then utilized to predict modules. These modules are subsequently combined into meta-modules by using NMF based clustering method. The integrated meta-modules are used to predict novel interactions between HIV-1 and human proteins. We have analyzed the significant GO terms and KEGG pathways in which the human proteins of the meta-modules participate. Moreover, the topological properties of human proteins involved in the meta modules are investigated. We have also performed statistical significance test to evaluate the predictions. Conclusions: Here, we propose a novel approach based on integration of different biological data sources, for predicting PPIs between HIV-1 and human proteins. Here, the integration is achieved through non negative matrix factorization (NMF) technique. Most of the predicted interactions are found to be well supported by the existing literature in PUBMED. Moreover, human proteins in the predicted set emerge as 'hubs' and 'bottlenecks' in the analysis. Low p-value in the significance test also suggests that the predictions are statistically significant. [ABSTRACT FROM AUTHOR]

Discovery of natural groups of similarly functioning individuals is a key task in analysis of real world networks. Also, overlap between community pairs is commonplace in large social and biological graphs, in particular. In fact, overlaps between communities are known to be denser than the non-overlapped regions of the communities. However, most of the existing algorithms that detect overlapping communities assume that the communities are denser than their surrounding regions, and falsely identify overlaps as communities. Further, many of these algorithms are computationally demanding and thus, do not scale reasonably with varying network sizes. In this article, we propose Fast Overlapped Community Search (FOCS), an algorithm that accounts for local connectedness in order to identify overlapped communities. FOCS is shown to be linear in number of edges and nodes. It additionally gains in speed via simultaneous selection of multiple near-best communities rather than merely the best, at each iteration. FOCS outperforms some popular overlapped community finding algorithms in terms of computational time while not compromising with quality. [ABSTRACT FROM AUTHOR]

The computational or in silico approaches for analysing the HIV-1-human protein-protein interaction (PPI) network, predicting different host cellular factors and PPIs and discovering several pathways are gaining popularity in the field of HIV research. Although there exist quite a few studies in this regard, no previous effort has been made to review these works in a comprehensive manner. Here we review the computational approaches that are devoted to the analysis and prediction of HIV-1-human PPIs. We have broadly categorized these studies into two fields: computational analysis of HIV-1-human PPI network and prediction of novel PPIs. We have also presented a comparative assessment of these studies and proposed some methodologies for discussing the implication of their results. We have also reviewed different computational techniques for predicting HIV-1-human PPIs and provided a comparative study of their applicability. We believe that our effort will provide helpful insights to the HIV research community. [ABSTRACT FROM AUTHOR]

Background: Detecting protein complexes within protein-protein interaction (PPI) networks is a major step toward the analysis of biological processes and pathways. Identification and characterization of protein complexes in PPI network is an ongoing challenge. Several high-throughput experimental techniques provide substantial number of PPIs which are widely utilized for compiling the PPI network of a species. Results: Here we focus on detecting human protein complexes by developing a multiobjective framework. For this large human PPI network is partitioned into modules which serves as protein complex. For building the objective functions we have utilized topological properties of PPI network and biological properties based on Gene Ontology semantic similarity. The proposed method is compared with that of some state-of-the-art algorithms in the context of different performance metrics. For the purpose of biological validation of our predicted complexes we have also employed a Gene Ontology and pathway based analysis here. Additionally, we have performed an analysis to associate resulting protein complexes with 22 key disease classes. Two bipartite networks are created to clearly visualize the association of identified protein complexes with the disorder classes. Conclusions: Here, we present the task of identifying protein complexes as a multiobjective optimization problem. Identified protein complexes are found to be associated with several disorders classes like 'Cancer', 'Endocrine' and 'Multiple'. This analysis uncovers some new relationships between disorders and predicted complexes that may take a potential role in the prediction of multi target drugs. [ABSTRACT FROM AUTHOR]

In this article, an unsupervised feature selection algorithm is proposed using an improved version of a recently developed Differential Evolution technique called MoDE. The proposed algorithm produces an optimal feature subset while optimizing three criteria, namely, the average standard deviation of the selected feature subset, the average dissimilarity of the selected features, and the average similarity of non-selected features with respect to their first nearest neighbor selected features. Normalized mutual information score is employed for computing both the similarity as well as the dissimilarity measures. The experimental results confirm the superiority of the proposed algorithm over the other state-of-the-art unsupervised feature selection algorithms for eight different kinds of datasets with the number of points ranging from 80 to 6238 and the number of dimensions ranging from 30 to 649. [ABSTRACT FROM AUTHOR]

Highlights: [•] Unsupervised feature selection approach using densest finding approach followed by feature clustering. [•] Usage of normalized mutual information scores for computing similarity as well as dissimilarity. [•] Superiority over three existing methods is established for eight data sets. [ABSTRACT FROM AUTHOR]

Abstract: The neurotoxicity of amyloid beta (Aβ), a major cleavage product of the amyloid precursor protein (APP), is enhanced by iron, as found in the amyloid plaques of Alzheimer's disease (AD) patients. By contrast, the long-known neuroprotective activity of APP is evident after α-secretase cleavage of the precursor to release sAPPα, and depends on the iron export actions of APP itself. The latter underlie its neurotrophic and protective effects in facilitating the homeostatic actions of ferroportin mediated-iron export. Thus APP-dependent iron export may alleviate oxidative stress by minimizing labile iron thus protecting neurons from iron overload during stroke and hemorrhage. Consistent with this, altered phosphorylation of iron-regulatory protein-1 (IRP1) and its signaling processes play a critical role in modulating APP translation via the 5′ untranslated region (5′UTR) of its transcript. The APP 5′UTR region encodes a functional iron-responsive element (IRE) RNA stem loop that represents a potential target for modulating APP production. Targeted regulation of APP gene expression via the modulation of 5′UTR sequence function represents a novel approach for the potential treatment of AD since altering APP translation can be used to improve both the protective brain iron balance and provide anti-amyloid efficacy. Approved drugs including paroxetine and desferrioxamine and several novel compounds have been identified that suppress abnormal metal-promoted Aβ accumulation with a subset of these acting via APP 5′UTR-dependent mechanisms to modulate APP translation and cleavage to generate the non-toxic sAPPα. [Copyright &y& Elsevier]

We reported that iron influx drives the translational expression of the neuronal amyloid precursor protein (APP), which has a role in iron efflux. This is via a classic release of repressor interaction of APP mRNA with iron-regulatory protein-1 (IRP1) whereas IRP2 controls the mRNAs encoding the L- and H-subunits of the iron storage protein, ferritin. Here, we identified thirteen potent APP translation blockers that acted selectively towards the uniquely configured iron-responsive element (IRE) RNA stem loop in the 5′ untranslated region (UTR) of APP mRNA. These agents were 10-fold less inhibitory of 5′UTR sequences of the related prion protein (PrP) mRNA. Western blotting confirmed that the ‘ninth’ small molecule in the series selectively reduced neural APP production in SH-SY5Y cells at picomolar concentrations without affecting viability or the expression of α-synuclein and ferritin. APP blocker-9 (JTR-009), a benzimidazole, reduced the production of toxic Aβ in SH-SY5Y neuronal cells to a greater extent than other well tolerated APP 5′UTR-directed translation blockers, including posiphen, that were shown to limit amyloid burden in mouse models of Alzheimer's disease (AD). RNA binding assays demonstrated that JTR-009 operated by preventing IRP1 from binding to the IRE in APP mRNA, while maintaining IRP1 interaction with the H-ferritin IRE RNA stem loop. Thus, JTR-009 constitutively repressed translation driven by APP 5′UTR sequences. Calcein staining showed that JTR-009 did not indirectly change iron uptake in neuronal cells suggesting a direct interaction with the APP 5′UTR. These studies provide key data to develop small molecules that selectively reduce neural APP and Aβ production at 10-fold lower concentrations than related previously characterized translation blockers. Our data evidenced a novel therapeutic strategy of potential impact for people with trisomy of the APP gene on chromosome 21, which is a phenotype long associated with Down syndrome (DS) that can also cause familial Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Summary: Nation-building has long been seen as an important focus of post-colonial African governments. However, up until now there has been no empirical analysis of the consequences of nation-building policies. Here we compile an original dataset measuring nine different types of nation-building policies. We first use logistic regressions to test the effect of nation-building policies on civil wars using two different datasets of civil wars, and find no evidence of a negative relationship between nation-building and civil war onset. Furthermore, a survival analysis exercise reveals a robust relationship between the proximity of nation-building policies and the onset of civil war. [ABSTRACT FROM AUTHOR]

Acemoglu, Johnson, and Robinson have claimed that the world income distribution underwent a ‘Reversal of Fortune’ from 1500 to the present, whereby formerly rich countries in what is now the developing world became poor while poor ones grew rich. We question their analysis with regard to both of their proxies for pre-modern income, namely urbanisation and population density. More specifically, an alternative measure of urbanisation with more observations generates a positive (but not significant) correlation between pre-modern and contemporary income, while a better measure of population density on arable land no longer produces a robust relationship. [ABSTRACT FROM PUBLISHER]

RNA sequence data from SARS CoV2 patients helps to construct a gene network related to this disease. A detailed analysis of the human host response to SARS CoV2 with expression profiling by high‐throughput sequencing has been accomplished with primary human lung epithelial cell lines. Using this data, the clustered gene annotation and gene network construction are performed with the help of the String database. Among the four clusters identified, only 1 with 44 genes could be annotated. Interestingly, this corresponded to basal cells with p = 1.37e − 05, which is relevant for respiratory tract infection. Functional enrichment analysis of genes present in the gene network has been completed using the String database and the Network Analyst tool. Among three types of cell–cell communication, only the anchoring junction between the basal cell membrane and the basal lamina in the host cell is involved in the virus transmission. In this junction point, a hemidesmosome structure plays a vital role in virus spread from one cell to basal lamina in the respiratory tract. In this protein complex structure, different integrin protein molecules of the host cell are used to promote the spread of virus infection into the extracellular matrix. So, small molecular blockers of different anchoring junction proteins, such as integrin alpha 3, integrin beta 1, can provide efficient protection against this deadly viral disease. ORF8 from SARS CoV2 virus can interact with both integrin proteins of human host. By using molecular docking technique, a ternary complex of these three proteins is modelled. Several oligopeptides are predicted as modulators for this ternary complex. In silico analysis of these modulators is very important to develop novel therapeutics for the treatment of SARS CoV2. [ABSTRACT FROM AUTHOR]

Predicting the functions of unannotated genes is one of the major challenges of biological investigation. In this study, we propose a weighted power scoring framework, called weighted power biological score (WPBS), for combining different biological data sources and predicting the function of some of the unclassified yeast Saccharomyces cerevisiae genes. The relative power and weight coefficients of different data sources, in the proposed score, are estimated systematically by utilizing functional annotations [yeast Gene Ontology (GO)-Slim: Process] of classified genes, available from Saccharomyces Genome Database. Genes are then clustered by applying k-medoids algorithm on WPBS, and functional categories of 334 unclassified genes are predicted using a P-value cutoff 1 \times 10^-5. The WPBS is available online at http://www.isical.ac.in/\simshubhra/WPBS/WPBS.html, where one can download WPBS, related files, and a MATLAB code to predict functions of unclassified genes. [ABSTRACT FROM AUTHOR]

Background: Machine learning based miRNA-target prediction algorithms often fail to obtain a balanced prediction accuracy in terms of both sensitivity and specificity due to lack of the gold standard of negative examples, miRNA-targeting site context specific relevant features and efficient feature selection process. Moreover, all the sequence, structure and machine learning based algorithms are unable to distribute the true positive predictions preferentially at the top of the ranked list; hence the algorithms become unreliable to the biologists. In addition, these algorithms fail to obtain considerable combination of precision and recall for the target transcripts that are translationally repressed at protein level. Methodology/Principal Finding: In the proposed article, we introduce an efficient miRNA-target prediction system MultiMiTar, a Support Vector Machine (SVM) based classifier integrated with a multiobjective metaheuristic based feature selection technique. The robust performance of the proposed method is mainly the result of using high quality negative examples and selection of biologically relevant miRNA-targeting site context specific features. The features are selected by using a novel feature selection technique AMOSA-SVM, that integrates the multi objective optimization technique Archived Multi-Objective Simulated Annealing (AMOSA) and SVM. Conclusions/Significance: MultiMiTar is found to achieve much higher Matthew's correlation coefficient (MCC) of 0.583 and average class-wise accuracy (ACA) of 0.8 compared to the others target prediction methods for a completely independent test data set. The obtained MCC and ACA values of these algorithms range from 20.269 to 0.155 and 0.321 to 0.582, respectively. Moreover, it shows a more balanced result in terms of precision and sensitivity (recall) for the translationally repressed data set as compared to all the other existing methods. An important aspect is that the true positive predictions are distributed preferentially at the top of the ranked list that makes MultiMiTar reliable for the biologists. MultiMiTar is now available as an online tool at www.isical.ac.in/,bioinfo_miu/multimitar. htm. MultiMiTar software can be downloaded from www.isical.ac.in/,bioinfo_miu/multimitar-download.htm. [ABSTRACT FROM AUTHOR]

Abstract: The identification of a suitable clustering algorithm to partition data and assessment of the validity of the resultant partitioning are ongoing quests in unsupervised learning. In this study, a fuzzy granulation–degranulation criterion is proposed to evaluate the goodness of a fuzzy partitioning of the data. This, in turn, is used to determine the appropriate clustering algorithm suitable for a particular data set. In general, the quality of a partitioning is measured by computing the variance within it, which is a measure of compactness of the obtained partitioning. Here a new error function, which reflects how well the computed cluster centers represent the whole data set, is used as the goodness measure of the obtained partitioning. Thus a clustering algorithm, providing a good set of cluster centers which approximate well the whole data set, is considered to be the most suited. Thereafter this new fuzzy granulation–degranulation criterion is used to develop six new cluster validity indices. These indices mimic the definitions of the existing and well-known cluster validity indices, such as PBM-index, XB-index, PS-index, FS-index, K-index and SV-index, but use the new fuzzy granulation–degranulation based error function instead of cluster compactness. In order to evaluate the effectiveness of the proposed error function in correctly identifying the appropriate clustering algorithm for a particular data set, eight well-known clustering algorithms, K-means, Fuzzy C-means, GAK-means (genetic algorithm based K-means algorithm), a newly developed genetic point symmetry based clustering technique (GAPS-clustering), Average Linkage clustering algorithm, Expectation Maximization (EM) clustering algorithm, Self-Organizing Map (SOM) and Spectral clustering technique are evaluated on a set of six artificially generated and six real-life data sets. Results show that GAK-means is the most appropriate for most of the data sets used for the experiments. Thereafter the effectiveness of the proposed cluster validity indices in identifying the appropriate number of clusters automatically from different data sets are shown for above mentioned 12 data sets. For the purpose of comparison, results obtained with the original versions of the proposed cluster validity indices and results obtained by a density based clustering technique are also presented. [Copyright &y& Elsevier]

In this paper, a new multi-objective (MO) fuzzy-clustering technique is developed for unsupervised land-cover classification in remote-sensing images. This technique automatically partitions the pixels in the spectral domain into several clusters. The new MO clustering algorithm uses the newly developed simulated-annealing-based multi-objective optimization technique (AMOSA) as the underlying optimization strategy. Centre-based cluster encoding is used. Each cluster is divided into several small hyperspherical sub-clusters, and the centres of all these small sub-clusters are encoded in a string to represent the whole cluster. For membership-value computation of points to different clusters, these sub-clusters are considered individually. However, for the purpose of objective-function evaluation, these sub-clusters are merged appropriately to form some variable number of whole clusters. Three objective functions, one reflecting the total compactness of the partitioning based on the Euclidean distance, one reflecting the total point-symmetrical compactness of the obtained partitioning and one reflecting the total line-symmetrical compactness of the obtained partitioning are considered here. These are optimized simultaneously using AMOSA in order to detect the appropriate number of clusters and the appropriate partitioning from remote-sensing image datasets. The point symmetry present in a particular partitioning is measured using a newly developed point-symmetry-based distance, whereas the line symmetry present in a particular partitioning is measured using a newly developed line-symmetry-based distance. Since AMOSA, as well as any other MO optimization technique, provides a set of Pareto-optimal solutions, a new method is also developed to determine a single solution from this set. Different land-cover regions in remote-sensing imagery have been classified using the proposed technique to establish its efficiency. Results are compared with those obtained by fuzzy C-means (FCM) clustering technique and a recently developed symmetry-based automatic clustering technique, the variable string length genetic point symmetry (VGAPS) technique, both qualitatively and quantitatively. Qualitative comparisons are also carried out with the segmentation results obtained by the mean-shift-based segmentation technique. [ABSTRACT FROM AUTHOR]

With the advancement of microarray technology, it is now possible to study the expression profiles of thousands of genes across different experimental conditions or tissue samples simultaneously. Microarray cancer datasets, organized as samples versus genes fashion, are being used for classification of tissue samples into benign and malignant or their subtypes. They are also useful for identifying potential gene markers for each cancer subtype, which helps in successful diagnosis of particular cancer types. In this article, we have presented an unsupervised cancer classification technique based on multiobjective genetic clustering of the tissue samples. In this regard, a real-coded encoding of the cluster centers is used and cluster compactness and separation are simultaneously optimized. The resultant set of near-Pareto-optimal solutions contains a number of non-dominated solutions. A novel approach to combine the clustering information possessed by the non-dominated solutions through Support Vector Machine (SVM) classifier has been proposed. Final clustering is obtained by consensus among the clusterings yielded by different kernel functions. The performance of the proposed multiobjective clustering method has been compared with that of several other microarray clustering algorithms for three publicly available benchmark cancer datasets. Moreover, statistical significance tests have been conducted to establish the statistical superiority of the proposed clustering method. Furthermore, relevant gene markers have been identified using the clustering result produced by the proposed clustering method and demonstrated visually. Biological relationships among the gene markers are also studied based on gene ontology. The results obtained are found to be promising and can possibly have important impact in the area of unsupervised cancer classification as well as gene marker identification for multiple cancer subtypes. [ABSTRACT FROM AUTHOR]

The problem of fuzzy clustering of categorical data, where no natural ordering among the elements of a categorical attribute domain can be found, is an important problem in exploratory data analysis. As a result, a few clustering algorithms with focus on categorical data have been proposed. In this paper, a modified differential evolution (DE)-based fuzzy c-medoids (FCMdd) clustering of categorical data has been proposed. The algorithm combines both local as well as global information with adaptive weighting. The performance of the proposed method has been compared with those using genetic algorithm, simulated annealing, and the classical DE technique, besides the FCMdd, fuzzy k-modes, and average linkage hierarchical clustering algorithm for four artificial and four real life categorical data sets. Statistical test has been carried out to establish the statistical significance of the proposed method. To improve the result further, the clustering method is integrated with a support vector machine (SVM), a well-known technique for supervised learning. A fraction of the data points selected from different clusters based on their proximity to the respective medoids is used for training the SVM. The clustering assignments of the remaining points are thereafter determined using the trained classifier. The superiority of the integrated clustering and supervised learning approach has been demonstrated. [ABSTRACT FROM AUTHOR]

Abstract: In this paper the problem of automatic clustering a data set is posed as solving a multiobjective optimization (MOO) problem, optimizing a set of cluster validity indices simultaneously. The proposed multiobjective clustering technique utilizes a recently developed simulated annealing based multiobjective optimization method as the underlying optimization strategy. Here variable number of cluster centers is encoded in the string. The number of clusters present in different strings varies over a range. The points are assigned to different clusters based on the newly developed point symmetry based distance rather than the existing Euclidean distance. Two cluster validity indices, one based on the Euclidean distance, XB-index, and another recently developed point symmetry distance based cluster validity index, Sym-index, are optimized simultaneously in order to determine the appropriate number of clusters present in a data set. Thus the proposed clustering technique is able to detect both the proper number of clusters and the appropriate partitioning from data sets either having hyperspherical clusters or having point symmetric clusters. A new semi-supervised method is also proposed in the present paper to select a single solution from the final Pareto optimal front of the proposed multiobjective clustering technique. The efficacy of the proposed algorithm is shown for seven artificial data sets and six real-life data sets of varying complexities. Results are also compared with those obtained by another multiobjective clustering technique, MOCK, two single objective genetic algorithm based automatic clustering techniques, VGAPS clustering and GCUK clustering. [Copyright &y& Elsevier]

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 ~76.5%, the accuracy of SFSSClass is found to be ~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., ~70.5% for SFSSClass versus ~58.8% in classifying a set of 17 MDT samples from 9 tumor types, ~91.7% for SFSSClass versus ~75% in classifying 12 cell lines from 6 tumor types and ~82.3% for SFSSClass versus ~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 cancermiRNA 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. [ABSTRACT FROM AUTHOR]

Background: MicroRNAs are a class of small noncoding RNAs that are abnormally expressed in different cancer cells. Molecular signature of miRNAs in different malignancies suggests that these are not only actively involved in the pathogenesis of human cancer but also have a significant role in patients survival. The differential expression patterns of specific miRNAs in a specific cancer tissue type have been reported in hundreds of research articles. However limited attempt has been made to collate this multitude of information and obtain a global perspective of miRNA dysregulation in multiple cancer types. Results: In this article a cancer-miRNA network is developed by mining the literature of experimentally verified cancermiRNA relationships. This network throws up several new and interesting biological insights which were not evident in individual experiments, but become evident when studied in the global perspective. From the network a number of cancer-miRNA modules have been identified based on a computational approach to mine associations between cancer types and miRNAs. The modules that are generated based on these association are found to have a number of common predicted target onco/tumor suppressor genes. This suggests a combinatorial effect of the module associated miRNAs on target gene regulation in selective cancer tissues or cell lines. Moreover, neighboring miRNAs (group of miRNAs that are located within 50 kb of genomic location) of these modules show similar dysregulation patterns suggesting common regulatory pathway. Besides this, neighboring miRNAs may also show a similar dysregulation patterns (differentially coexpressed) in the cancer tissues. In this study, we found that in 67% of the cancer types have at least two neighboring miRNAs showing downregulation which is statistically significant (P < 10-7, Randomization test). A similar result is obtained for the neighboring miRNAs showing upregulation in specific cancer type. These results elucidate the fact that the neighboring miRNAs might be differentially coexpressed in cancer tissues as that of the normal tissue types. Additionally, cancer-miRNA network efficiently detect hub miRNAs dysregulated in many cancer types and identify cancer specific miRNAs. Depending on the expression patterns, it is possible to identify those hubs that have strong oncogenic or tumor suppressor characteristics. Conclusions: Limited work has been done towards revealing the fact that a number of miRNAs can control commonly altered regulatory pathways. However, this becomes immediately evident by accompanying the analysis of cancermiRNA relationships in the proposed network model. These raise many unaddressed issues in miRNA research that have never been reported previously. These observations are expected to have an intense implication in cancer and may be useful for further research. [ABSTRACT FROM AUTHOR]

Background: Some of the recent investigations in systems biology have revealed the existence of a complex regulatory network between genes, microRNAs (miRNAs) and transcription factors (TFs). In this paper, we focus on TF to miRNA regulation and provide a novel interface for extracting the list of putative TFs for human miRNAs. A putative TF of an miRNA is considered here as those binding within the close genomic locality of that miRNA with respect to its starting or ending base pair on the chromosome. Recent studies suggest that these putative TFs are possible regulators of those miRNAs. Description: The interface is built around two datasets that consist of the exhaustive lists of putative TFs binding respectively in the 10 kb upstream region (USR) and downstream region (DSR) of human miRNAs. A web server, named as PuTmiR, is designed. It provides an option for extracting the putative TFs for human miRNAs, as per the requirement of a user, based on genomic locality, i.e., any upstream or downstream region of interest less than 10 kb. The degree distributions of the number of putative TFs and miRNAs against each other for the 10 kb USR and DSR are analyzed from the data and they explore some interesting results. We also report about the finding of a significant regulatory activity of the YY1 protein over a set of oncomiRNAs related to the colon cancer. Conclusion: The interface provided by the PuTmiR web server provides an important resource for analyzing the direct and indirect regulation of human miRNAs. While it is already an established fact that miRNAs are regulated by TFs binding to their USR, this database might possibly help to study whether an miRNA can also be regulated by the TFs binding to their DSR. [ABSTRACT FROM AUTHOR]

In this paper, the automatic segmentation of multispectral magnetic resonance image of the brain is posed as a clustering problem in the intensity space. Thereafter an automatic clustering technique is proposed to solve this problem. The proposed real-coded variable string length genetic clustering technique (MCVGAPS clustering) is able to evolve the number of clusters present in the data set automatically. Each cluster is divided into several small hyperspherical subclusters and the centers of all these small sub-clusters are encoded in a string to represent the whole clustering. For assigning points to different clusters, these local sub-clusters are considered individually. For the purpose of objective function evaluation, these sub-clusters are merged appropriately to form a variable number of global clusters. A recently developed point symmetry distance based cluster validity index, Sym-index, is optimized to automatically evolve the appropriate number of clusters present in an MR brain image. The proposed method is applied on several simulated T1-weighted, T2- weighted and proton density normal and MS lesion magnetic resonance brain images. Superiority of the proposed method over Fuzzy C-means, Expectation Maximization clustering algorithms are demonstrated quantitatively. The automatic segmentation obtained by multiseed based multiobjective clustering technique (MCVGAPS) is also compared with the available ground truth information. [ABSTRACT FROM AUTHOR]

An important problem of knowledge discovery that has recently evolved in various reallife networks is identifying the largest set of vertices that are functionally associated. The topology of many real-life networks shows scale-freeness, where the vertices of the underlying graph follow a power-law degree distribution. Moreover, the graphs corresponding to most of the real-life networks are weighted in nature. In this article, the problem of finding the largest group or association of vertices that are dense (denoted as dense vertexlet) in a weighted scale-free graph is addressed. Density quantifies the degree of similarity within a group of vertices in a graph. The density of a vertexlet is defined in a novel way that ensures significant participation of all the vertices within the vertexlet. It is established that the problem is NP-complete in nature. An upper bound on the order of the largest dense vertexlet of a weighted graph, with respect to certain density threshold value, is also derived. Finally, an O(n^2 log n) (n denotes the number of vertices in the graph) heuristic graph mining algorithm that produces an approximate solution for the problem is presented. [ABSTRACT FROM AUTHOR]

In this paper, an evolutionary technique for the semi-supervised clustering is proposed. The proposed technique uses a point symmetry based distance measure. Semi-supervised classification uses aspects of both unsupervised and supervised learning to improve upon the performance of traditional classification methods. In this paper the existing point symmetry based genetic clustering technique, GAPS-clustering, is extended in two different ways to handle the semi-supervised classi- fication problem. The proposed semi-GAPS clustering algorithmis able to detect any type of clusters irrespective of shape, size and convexity as long as they possess the point symmetry property. Kdtree based nearest neighbor search is used to reduce the complexity of finding the closest symmetric point. Adaptive mutation and crossover probabilities are used. Experimental results demonstrate practical performance benefits of the methodology in detecting classes having symmetrical shapes in case of semi-supervised clustering. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we have proposed a multiobjective clustering technique which optimizes simultaneously two objectives, one reflecting the total ‘goodness’ present in the data set in terms of total compactness (measured using Euclidean distance) of the clusters, and the other reflecting the total symmetry present in the clusters of the data set. The proposed algorithm uses a simulated annealing based multiobjective optimization method as the underlying optimization criterion. Center based encoding is used. The proposed multiobjective clustering technique is able to suitably evolve these cluster centers in such a way so that the two objectives are optimized ‘simultaneously’. Assignment of points to different clusters is done based on the newly developed point symmetry based distance rather than the Euclidean distance. Results on eight artificial and six real-life data sets show that the proposed technique is well-suited to detect true partitioning from data sets with clusters having either the hyperspherical shape or point symmetric structure. Results are compared with those obtained by five existing clustering techniques, one multiobjective clustering technique, MOCK, average linkage clustering algorithm, expectation maximization clustering algorithm, well-known genetic algorithm based K-means clustering technique (GAK-means) and a newly developed genetic algorithm with point symmetry based clustering technique (GAPS). [Copyright &y& Elsevier]

Motivation: Prediction of microRNA (miRNA) target mRNAs using machine learning approaches is an important area of research. However, most of the methods suffer from either high false positive or false negative rates. One reason for this is the marked deficiency of negative examples or miRNA non-target pairs. Systematic identification of non-target mRNAs is still not addressed properly, and therefore, current machine learning approaches are compelled to rely on artificially generated negative examples for training. [ABSTRACT FROM PUBLISHER]

The article presents a study on the identification of the correct number of clusters in data mining. It highlights the use of the Euclidean distance and observes the properties of symmetry in most clustering solutions. It outlines the proposal of nine cluster validity indexes and determines that the incorporation of the property symmetry enhances the capabilities of the indexes. It also mentions the use of the point symmetry (PS)-based genetic clustering technique as well as the genetic-algorithm-based clustering technique (GAPS) as the partitioning algorithm. Information on the favorable result of the study is presented.

In this paper, a novel point symmetry based pattern classifier (PSC) is proposed. A recently developed point symmetry based distance is utilized to determine the amount of point symmetry of a particular test pattern with respect to a class prototype. Kd-tree based nearest neighbor search is used for reducing the complexity of point symmetry distance computation. The proposed point symmetry based classifier is well-suited for classifying data sets having point symmetric classes, irrespective of any convexity, overlap or size. In order to classify data sets having line symmetry property, a line symmetry based classifier (LSC) along the lines of PSC is thereafter proposed in this paper. To measure the total amount of line symmetry of a particular point in a class, a new definition of line symmetry based distance is also provided. Proposed LSC preserves the advantages of PSC. The performance of PSC and LSC are demonstrated in classifying fourteen artificial and real-life data sets of varying complexities. For the purpose of comparison, k-NN classifier and the well-known support vector machine (SVM) based classifiers are executed on the data sets used here for the experiments. Statistical analysis, ANOVA, is also performed to compare the performance of these classification techniques. [ABSTRACT FROM AUTHOR]

Motivation: One of the important goals of biological investigation is to predict the function of unclassified gene. Al- though there is a rich literature on multi data source integration for gene function prediction, there is hardly any similar work in the framework of data source weighting using functional annotations of classified genes. In this investigation, we propose a new scoring framework, called biological score (BS) and incorporating data source weighting, for predicting the function of some of the unclassified yeast genes. Methods: The BS is computed by first evaluating the similarities between genes, arising from different data sources, in a common framework, and then integrating them in a linear combination style through weights. The relative weight of each data source is determined adaptively by utilizing the information on yeast gene ontology (GO)-slim process annotations of classified genes, available from Saccharomyces Genome Database (SGD). Genes are clustered by a method called K-BS, where, for each gene, a cluster comprising that gene and its K nearest neighbors is computed using the proposed score (BS). The performances of BS and K-BS are evaluated with gene annotations available from Munich Information Center for Protein Sequences (MIPS). Results: We predict the functional categories of 417 classified genes from 417 clusters with 0.98 positive predictive value using K-BS. The functional categories of 12 unclassified yeast genes are also predicted. Conclusion: Our experimental results indicate that considering multiple data sources and estimating their weights with annotations of classified genes can considerably enhance the performance of BS. It has been found that even a small proportion of annotated genes can provide improvements in finding true positive gene pairs using BS. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various articles within the issue on topics including protein-protein interactions; use of canonical correlation algorithm for DNA pattern recognition; and development of a method for gene regulatory networks prediction.

In this paper, a new symmetry-based genetic clustering algorithm is proposed which automatically evolves the number of clusters as well as the proper partitioning from a data set. Strings comprise both real numbers and the don't care symbol in order to encode a variable number of clusters. Here, assignment of points to different clusters are done based on a point symmetry (PS)-based distance rather than the euclidean distance. A newly proposed PS-based cluster validity index, Sym-index, is used as a measure of the validity of the corresponding partitioning. The algorithm is, therefore, able to detect both convex and nonconvex clusters irrespective of their sizes and shapes as long as they possess the symmetry property. Kd-tree-based nearest neighbor search is used to reduce the complexity of computing PS-based distance. A proof on the convergence property of variable string length genetic algorithm with PS- distance-based clustering (VGAPS-clustering) technique is also provided. The effectiveness of VGAPS-clustering compared to variable string length Genetic K-means algorithm (GCUK-clustering) and one recently developed weighted sum validity function-based hybrid niching genetic algorithm (HNGA-clustering) is demonstrated for nine artificial and five real-life data sets. [ABSTRACT FROM AUTHOR]

The problem of classifying an image into different homogeneous regions is viewed as the task of clustering the pixels in the intensity space. In particular, satellite images contain landcover types some of which cover significantly large areas, while some (e.g., bridges and roads) occupy relatively much smaller regions. Automatically detecting regions or clusters of such widely varying sizes presents a challenging task. In this paper, a newly developed real-coded variable string length genetic fuzzy clustering technique with a new point symmetry distance is used for this purpose. The proposed algorithm is capable of automatically determining the number of segments present in an image. Here assignment of pixels to different clusters is done based on the point symmetry based distance rather than the Euclidean distance. The cluster centers are encoded in the chromosomes, and a newly developed fuzzy point symmetry distance based cluster validity index, FSym-index, is used as a measure of the validity of the corresponding partition. This validity index is able to correctly indicate presence of clusters of different sizes and shapes as long as they are internally symmetrical. The space and time complexities of the proposed algorithm are also derived. The effectiveness of the proposed technique is first demonstrated in identifying two small objects from a large background from an artificially generated image and then in identifying different landcover regions in remote sensing imagery. Results are compared with those obtained using the well known fuzzy C-means algorithm both qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]

Abstract: Granulocyte-macrophage colony-stimulating factor (GM-CSF) holds immunotherapeutic promise in prostate cancer as it activates the host immune system. Increased production of GM-CSF by cancer cells may facilitate host immunosurveillence by the dendritic cells (DC). Here, we studied the effects of kaempferol (K) and quercetin (Q) on the production of GM-CSF in PC-3 cells. Human cytokine antibody array revealed that treatment with K or Q increased GM-CSF release by PC-3 cells. We further observed by ELISA that K and Q in a concentration-dependent manner increased GM-CSF production without affecting its mRNA levels. Inhibitors of vesicular traffic through the endoplasmic reticulum and Golgi-blocked GM-CSF secretory stimulation. A microtubule-stabilizing agent stimulated GM-CSF release, whereas tubulin and actin depolymerizers suppressed K- or Q-stimulated secretion of GM-CSF. Depletion of extracellular or intracellular calcium ion inhibited the GM-CSF secretion upregulated by both K and Q. Furthermore, we showed that K- and Q-stimulated GM-CSF production involves PLC, PKC, and MEK1/2 activation. Treating human DC with the conditioned medium of K- or Q-incubated PC-3 cells increased chemotaxis of DC, which was significantly attenuated when the conditioned medium was incubated with the neutralizing antibody against GM-CSF. Taken together, our results demonstrate that K and Q activate an immune response in the prostate cancer cells by stimulating GM-CSF production, which in turn could result in the recruitment of DCs to the tumor site. [Copyright &y& Elsevier]

Abstract: In this paper we present a genetic solution to the outlier detection problem. The essential idea behind this technique is to define outliers by examining those projections of the data, along which the data points have abnormal or inconsistent behavior (defined in terms of their sparsity values). We use a partitioning method to divide the data set into groups such that all the objects in a group can be considered to behave similarly. We then identify those groups that contain outliers. The algorithm assigns an ‘outlier-ness’ value that gives a relative measure of how strong an outlier group is. An evolutionary search computation technique is employed for determining those projections of the data over which the outliers can be identified. A new data structure, called the grid count tree (GCT), is used for efficient computation of the sparsity factor. GCT helps in quickly determining the number of points within any grid defined over the projected space and hence facilitates faster computation of the sparsity factor. A new crossover is also defined for this purpose. The proposed method is applicable for both numeric and categorical attributes. The search complexity of the GCT traversal algorithm is provided. Results are demonstrated for both artificial and real life data sets including four gene expression data sets. [Copyright &y& Elsevier]

The article surveys the existing classical and computational intelligence approaches for gene identification. The authors begin by briefly describing the computational and classical methods based on Hidden Markov model (HMM), Bayesian networks and dynamic programming, followed by discussion of these methods' application to gene finding. They also present a list of available gene finders and their corresponding Web sites. Moreover, they summarize recent developments in various gene finding methods, particularly those using computational intelligence such as neural networks and genetic algorithms.

Abstract: In this paper, an evolutionary clustering technique is described that uses a new point symmetry-based distance measure. The algorithm is therefore able to detect both convex and non-convex clusters. Kd-tree based nearest neighbor search is used to reduce the complexity of finding the closest symmetric point. Adaptive mutation and crossover probabilities are used. The proposed GA with point symmetry (GAPS) distance based clustering algorithm is able to detect any type of clusters, irrespective of their geometrical shape and overlapping nature, as long as they possess the characteristic of symmetry. GAPS is compared with existing symmetry-based clustering technique SBKM, its modified version, and the well-known -means algorithm. Sixteen data sets with widely varying characteristics are used to demonstrate its superiority. For real-life data sets, ANOVA and MANOVA statistical analyses are performed. [Copyright &y& Elsevier]

A central step in the analysis of gene expression data is the identification of groups of genes that exhibit similar expression patterns. Clustering and ordering the genes using gene expression data into homogeneous groups was shown to be useful in functional annotation, tissue classification, regulatory motifidentifi cation, and other applications. Although there is a rich literature on gene ordering in hierarchical clustering framework for gene expression analysis, there is no work addressing and evaluating the importance of gene ordering in partitive clustering framework, to the best knowledge of the authors. Outside the framework of hierarchical clustering, different gene ordering algorithms are applied on the whole data set, and the domain of partitive clustering is still unexplored with gene ordering approaches. A new hybrid method is proposed for ordering genes in each of the clusters obtained from partitive clustering solution, using microarray gene expressions. Two existing algorithms for optimally ordering cities in travelling salesman problem (TSP), namely, FRAG_GALK and Concorde, are hybridized individually with self organizing MAP to show the importance of gene ordering in partitive clustering framework. We validated our hybrid approach using yeast and fi broblast data and showed that our approach improves the result quality of partitive clustering solution, by identifying subclusters within big clusters, grouping functionally correlated genes within clusters, minimization of summation of gene expression distances, and the maximization of biological gene ordering using MIPS categorization. Moreover, the new hybrid approach, finds comparable or sometimes superior biological gene order in less computation time than those obtained by optimal leaf ordering in hierarchical clustering solution. [ABSTRACT FROM AUTHOR]

This investigation deals with a new distance measure for genes using their microarray expressions and a new algorithm for fast gene ordering without clustering. This distance measure is called "Maxrange distance," where the distance between two genes corresponding to a particular type of experiment is computed using a normalization factor, which is dependent on the dynamic range of the gene expression values of that experiment. The new gene-ordering method called "Minimal Neighbor" is based on the concept of nearest neighbor heuristic involving O(n²) time complexity. The superiority of this distance measure and the comparability of the ordering algorithm have been extensively established on widely studied microarray data sets by performing statistical tests. An interesting application of this ordering algorithm is also demonstrated for finding useful groups of genes within clusters obtained from a nonhierarchical clustering method like the self-organizing map. [ABSTRACT FROM AUTHOR]

An important approach for unsupervised Iandcover classification in remote sensing images is the clustering of pixels in the spectral domain into several fuzzy partitions. In this paper, a multiobjective optimization algorithm is utilized to tackle the problem of fuzzy partitioning where a number of fuzzy cluster validity indexes are simultaneously optimized. The resultant set of near-Pareto-optimal solutions contains a number of nondominated solutions, which the user can judge relatively and pick up the most promising one according to the problem requirements. Real-coded encoding of the cluster centers is used for this purpose. Results demonstrating the effectiveness of the proposed technique are provided for numeric remote sensing data described in terms of feature vectors. Different landcover regions in remote sensing imagery have also been classified using the proposed technique to establish its efficiency. [ABSTRACT FROM AUTHOR]

The article presents an overview of the application of evolutionary algorithms in bioinformatics tasks such as gene sequence analysis, gene mapping, and deoxyribonucleic acid (DNA) fragment assembly, among others. It also provides the relevance of using evolutionary algorithms. The limitations are presented along with a bibliography.

Abstract: The physiological effects of the flavone, apigenin on bone cells were studied. We first show that apigenin inhibits tumor necrosis factor alpha (TNFα)- and interferon gamma (IFNγ)-induced secretion of several osteoclastogenic cytokines from MC3T3-E1 mouse calvarial osteoblast cell line. Ligands of the TNF receptor family constitute the most potent osteoclastic cytokines. In MC3T3-E1 cells, apigenin dose-dependently (from 5 to 20μM) inhibits TNFα-induced production of the osteoclastogenic cytokines, IL-6 (interleukin-6), RANTES (regulated upon activation, normal T cell-expressed and -secreted), monocyte chemoattractant protein-1 (MCP-1) and MCP-3. In addition, apigenin inhibits IFNγ-stimulated secretion of monokines, CXCL-9, and -10 in MC3T3-E1 cells. Next, we show that apigenin strongly inhibits differentiation of 3T3-L1 preadipocytes to adipocytes with attendant inhibition of adipocyte differentiation-induced IL-6, MCP-1, and leptin production. Inhibition of adipogenic differentiation by apigenin could be due to induction of osteogensis as it robustly upregulates mRNA levels of bone morphogenetic protein-6 (BMP-6). Finally, the presence of apigenin inhibited osteoclast differentiation from the RAW 264.7 cell line by reducing receptor activator of nuclear factor kappa ligand (RANKL)-induced expression of tartrate-resistant acid phosphatase (TRAP), RANK, and calcitonin receptor but not CCR1, resulting in the inhibition of multinucleated osteoclast formation. Similarly, apigenin inhibited expression of the osteoclast differentiation markers TRAP, RANK, and c-Fms in osteoclast precursor cells obtained from mouse bone marrow following treatment with RANKL and macrophage colony stimulating factor (MCSF). Furthermore, apigenin induced apoptosis of mature osteoclasts obtained from rabbit long bone and inhibited bone resorption. In all instances, a structurally related compound, flavone had no significant effect. These data suggest that apigenin has multiple effects on all three bone cells that could prevent bone loss in vivo. [Copyright &y& Elsevier]