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With the rapid development of social platforms and human activities, people are now interested to solve their problems with the help of the social crowd-powered system. So, crowdsourcing has become a promising way of solving problems in a distributed manner within a specific time. Crowd workers pick up a task (simple or complex) and solve it with competing interest or collaboratively and receive an incentive (as monetary or non-monetary). In paid crowdsourcing platforms, crowd workers solve complex tasks and get remunerations through prior bidding or prior announcement of fee decided by the requester (task provider). For decomposable tasks, a single winner may not provide a significant solution to the requester due to the insufficient knowledge. So, we induce collaboration in competitive crowdsourcing markets to better handle decomposable-type tasks. In this paper, we propose an envelope game-based mechanism that ensures if the tasks are decomposable then the workers will be encouraged to collaborate with their subtasks and share their remunerations. This mechanism also increases the chance of receiving more number of cost-effective solutions. Thus both the requester and crowd workers get benefits from the system. The effectiveness of the proposed mechanism is evaluated through empirical analysis on simulated environments. [ABSTRACT FROM AUTHOR]

The proliferation and intensification of diseases have forced every researcher to take actions for a robust understanding of the organisms. This demands deep knowledge about the cells and tissues in an organ and its entire surroundings, more precisely the microbiome community which involves viruses, bacteria, archaea, among others. They play an important role in the function of our body, and act both as a deterrent as well as shelter for diseases. Therefore, it is pertinent to study the relation within the microbiome in a human body. In this work, we analyze the sequence data provided through the Human Microbiome Project to explore evolutionary relations within blood microbiome. The objective is to analyze the common proteins present in the different microbes in the blood and find their phylogeny. The analysis of the phylogenetic relation between these species provides important insights about the conservedness of phylogeny of blood microbiome. Interestingly, the co-existence of five of those common proteins is observed in human too. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) are a kind of short non-coding RNAs, of about 22 nucleotides in length, which modulate and sometimes degrade the target mRNAs thereby regulating a number of cellular functions. Recent research in this area establishes the involvement of miRNAs in various disease progressions, including certain types of cancer development. Further, genome-wide expression profiling of miRNAs has been proven to be useful for differentiating various cancer types. In this paper, we have used miRNA expression profiles over a large set of breast cancer tumor samples for identifying subtypes of breast cancers. The experimental results demonstrate that miRNAs carry a unique signature that distinguishes cancer subtypes and reveal new cancer subtypes. Additional survival analyses based on clinical data also strengthen this claim. [ABSTRACT FROM AUTHOR]

Background: Parkinson's Disease (PD) is a progressive neurologic disorder that affects movement and balance. Recent studies have revealed the importance of microRNA (miR) in PD. However, the detailed role of miR and its regulation by Transcription Factor (TF) remain unexplored. In this work for the first time we have studied TF-miR-mRNA regulatory network as well as miR co-expression network in PD. Result: We compared the 204 differentially expressed miRs from microarray data with 73 PD related miRs obtained from literature, Human MicroRNA Disease Database and found a significant overlap of 47 PD related miRs (p-value<0.05). Functional enrichment analyses of these 47 common (Group1) miRs and the remaining 157 (Group2) miRs revealed similar kinds of over-representative GO Biological Processes and KEGG pathways. This strengthens the possibility that some of the Group 2 miRs can have functional roles in PD progression, hitherto unidentified in any study. In order to explore the cross talk between TF, miR and target mRNA, regulatory networks were constructed. Study of these networks resulted in 14 Inter-Regulatory hub miRs whereas miR co-expression network revealed 18 co-expressed hub miRs. Of these 32 hub miRs, 23 miRs were previously unidentified with respect to their association with PD. Hierarchical clustering analysis further strengthens the roles of these novel miRs in different PD pathways. Furthermore hsa-miR-92a appeared as novel hub miR in both regulatory and co-expression network indicating its strong functional role in PD. High conservation patterns were observed for most of these 23 novel hub miRs across different species including human. Thus these 23 novel hub miRs can be considered as potential biomarkers for PD. Conclusion: Our study identified 23 novel miR markers which can open up new avenues for future studies and shed lights on potential therapeutic targets for PD. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) are a class of small noncoding RNAs that are known to have critical functions across various biological processes. Simultaneous activities of multiple miRNAs can be monitored from their expression profiles under various conditions. We often build up coexpression networks from such profiles. Unfortunately, due to the change of experimental setups (or conditions), the expression profiles do change, and consequently, the patterns of the coexpression networks vary. To obtain a robust functional relationship between miRNAs, by integrating different coexpression networks in a systems biology approach, we have to combine them properly. Here, we evaluate the state-of-the-art techniques and propose a novel integrative measure, and a corresponding methodology, that might be useful for identifying the dependence between coexpression and functional similarity. We establish the results by evaluating the expression profiles of miRNAs taken from bone marrow samples of patients with leukemia. The findings highlight the potential of the integrative algorithm in analyzing the expression profiles of miRNAs for further study. [ABSTRACT FROM PUBLISHER]

Predicting the transcription start sites (TSSs) of microRNAs (miRNAs) is important for understanding how these small RNA molecules, known to regulate translation and stability of protein-coding genes, are regulated themselves. Previous approaches are primarily based on genetic features, trained on TSSs of protein-coding genes, and have low prediction accuracy. Recently, a support vector machine based technique has been proposed for miRNA TSS prediction that uses known miRNA TSS for training the classifier along with a set of existing and novel CpG island based features. Current progress in epigenetics research has provided genomewide and tissue-specific reports about various phenotypic traits. We hypothesize that incorporating epigenetic characteristics into statistical models may lead to better prediction of primary transcripts of human miRNAs. In this paper, we have tested our hypothesis on brain-specific miRNAs by using epigenetic as well as genetic features to predict the primary transcripts. For this, we have used a sophisticated feature selection technique and a robust classification model. Our prediction model achieves an accuracy of more than 80% and establishes the potential of epigenetic analysis for in silico prediction of TSSs. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) are non-coding, short (21-23nt) regulators of protein-coding genes that are generally transcribed first into primary miRNA (pri-miR), followed by the generation of precursor miRNA (pre-miR). This finally leads to the production of the mature miRNA. A large amount of information is available on the pre- and mature miRNAs. However, very little is known about the pri-miRs, due to a lack of knowledge about their transcription start sites (TSSs). Based on the genomic loci, miRNAs can be categorized into two types -intragenic (intra-miR) and intergenic (inter-miR). While it is already an established fact that intra-miRs are commonly transcribed in conjunction with their host genes, the transcription machinery of intermiRs is poorly understood. Although it is assumed that miRNA promoters are similar in structure to gene promoters, since both are transcribed by RNA polymerase II (Pol II), computational validations exhibit poor performance of gene promoter prediction methods on miRNAs. In this paper, we concentrate on the problem of TSS prediction for miRNAs. The present study begins with the identification of positive and negative promoter samples from recently published data stemming from RNA-sequencing studies. From these samples of experimentally validated miRNA TSSs, a number of standard sequence features are extracted. Furthermore, to account for potential footprints related to promoter regulation by CpG dinucleotide targeted DNA methylation, a number of novel features are defined. We develop a support vector machine (SVM) with RBF kernel for the prediction of miRNA TSSs trained on human miRNA promoters. A novel feature reduction technique based on archived multi-objective simulated annealing (AMOSA) identifies the final set of features. The resulting model trained on miRNA promoters shows improved performance over the one trained on protein-coding gene promoters in terms of classification accuracy, sensitivity and specificity. Results are also reported for a completely independent biologically validated test set. In a part of the investigation, the proposed approach is used to predict protein-coding gene TSSs. It shows a significantly improved performance when compared to previously published gene TSS prediction methods. [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]

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 work we propose Monte Carlo simulation models for dynamically computing MaxVaR for a financial return series. This dynamic MaxVaR takes into account the time-varying volatility as well as non-normality of returns or innovations. We apply this methodology to five stock market indices. To validate the proposed methods we compute the number of MaxVaR violations and compare them with the expected number. We also compute the MaxVaR-to-VaR ratio and find that, on average, dynamic MaxVaR exceeds dynamic VaR by 5-7% at the 1% significance level, and by 12-14% at the 5% significance level for the selected indices. [ABSTRACT FROM AUTHOR]

Despite the existence of a number of variations of genetic algorithms for the traveling salesman problem in literature, no efforts have been made to the best of our knowledge to characterize their performance. This paper presents a detailed comparative study on some of the solution methods for the traveling salesman problem using genetic algorithms. All the operators of genetic algorithms have been given equal emphasis in the analysis. The complete simulation has been done using a number of C programs written by us for this purpose. A permanent network has been particularly considered for this task. The results shed insight on the best solution method for the problem. However, we cannot conclude how the behavior of those algorithms depends on network topology. [ABSTRACT FROM AUTHOR]

The crowd-powered systems have been shown to be highly successful in the current decade to manage collective contribution of online workers for solving different complex tasks. It can also be used for soliciting opinions from a large set of people working in a distributed manner. Unfortunately, the online community of crowd workers might involve non-experts as opinion providers. As a result, such approaches may give rise to noise making it hard to predict the appropriate (gold) judgment. Judgment analysis is in general a way of learning about human decision from multiple opinions. A spectrum of algorithms has been proposed in the last few decades to address this problem. They are broadly made up of supervised or unsupervised types. However, they have been readdressed in recent years having focus on different strategies for obtaining the gold judgment from crowdsourced opinions, viz., estimating the accuracy of opinions, difficulties of the problem, spammer identification, handling noise, etc. Besides this, investigation of various types of crowdsourced opinions to solve complex real-life problems provide new insights in this domain. In this survey, we provide a comprehensive overview of the judgment analysis problem and some of its novel variants, addressed with different approaches, where the opinions are crowdsourced. [ABSTRACT FROM AUTHOR]

In crowd opinion aggregation models, the expertise of annotators plays an important role to derive the appropriate judgment. It is seen that in most of the aggregation methods annotators’ accuracy and bias are considered as two important features and based on it the priority of annotators is assigned. But instead of relying upon these limited features, the quality of annotators can be suitably exploited using rank-based features to further improve the prediction. Basically, the annotators are ranked according to various features and therefrom multiple separate rankings are produced. These rankings, if properly weighted, can lead to obtain the final aggregated ranking in a better way. In this paper, we have developed a novel weighted rank aggregation approach and applied the same on three artificially generated ranking datasets with varying noise. Moreover, the comparative effectiveness of the proposed method is demonstrated by applying it on three Amazon Mechanical Turk datasets. [ABSTRACT FROM AUTHOR]

Background: Parkinson's disease (PD) is the second most prevalent neurodegenerative disorders in the world. Studying PD from systems biology perspective involving genes and their regulators might provide deeper insights into the complex molecular interactions associated with this disease. Result: We have studied gene co-expression network obtained from a PD-specific microarray data. The co-expression network identified 11 hub genes, of which eight genes are not previously known to be associated with PD. Further study on the functionality of these eight novel hub genes revealed that these genes play important roles in several neurodegenerative diseases. Furthermore, we have studied the tissue-specific expression and histone modification patterns of the novel hub genes. Most of these genes possess several histone modification sites those are already known to be associated with neurodegenerative diseases. Regulatory network namely mTF-miRNA-gene-gTF involves microRNA Transcription Factor (mTF), microRNA (miRNA), gene and gene Transcription Factor (gTF). Whereas long noncoding RNA (lncRNA) mediated regulatory network involves miRNA, gene, mTF and lncRNA. mTF-miRNA-gene-gTF regulatory network identified a novel feed-forward loop. lncRNA-mediated regulatory network identified novel lncRNAs of PD and revealed the two-way regulatory pattern of PD-specific miRNAs where miRNAs can be regulated by both the TFs and lncRNAs. SNP analysis of the most significant genes of the co-expression network identified 20 SNPs. These SNPs are present in the 3' UTR of known PD genes and are controlled by those miRNAs which are also involved in PD. Conclusion: Our study identified eight novel hub genes which can be considered as possible candidates for future biomarker identification studies for PD. The two regulatory networks studied in our work provide a detailed overview of the cellular regulatory mechanisms where the non-coding RNAs namely miRNA and lncRNA, can act as epigenetic regulators of PD. SNPs identified in our study can be helpful for identifying PD at an earlier stage. Overall, this study may impart a better comprehension of the complex molecular interactions associated with PD from systems biology perspective. [ABSTRACT FROM AUTHOR]

The recent increase in publicly available metagenomic datasets with geospatial metadata has made it possible to determine location-specific, microbial fingerprints from around the world. Such fingerprints can be useful for comparing microbial niches for environmental research, as well as for applications within forensic science and public health. To determine the regional specificity for environmental metagenomes, we examined 4305 shotgun-sequenced samples from the MetaSUB Consortium dataset—the most extensive public collection of urban microbiomes, spanning 60 different cities, 30 countries, and 6 continents. We were able to identify city-specific microbial fingerprints using supervised machine learning (SML) on the taxonomic classifications, and we also compared the performance of ten SML classifiers. We then further evaluated the five algorithms with the highest accuracy, with the city and continental accuracy ranging from 85–89% to 90–94%, respectively. Thereafter, we used these results to develop Cassandra, a random-forest-based classifier that identifies bioindicator species to aid in fingerprinting and can infer higher-order microbial interactions at each site. We further tested the Cassandra algorithm on the Tara Oceans dataset, the largest collection of marine-based microbial genomes, where it classified the oceanic sample locations with 83% accuracy. These results and code show the utility of SML methods and Cassandra to identify bioindicator species across both oceanic and urban environments, which can help guide ongoing efforts in biotracing, environmental monitoring, and microbial forensics (MF). [ABSTRACT FROM AUTHOR]

In urban ecosystems, microbes play a key role in maintaining major ecological functions that directly support human health and city life. However, the knowledge about the species composition and functions involved in urban environments is still limited, which is largely due to the lack of reference genomes in metagenomic studies comprises more than half of unclassified reads. Here we uncovered 732 novel bacterial species from 4728 samples collected from various common surface with the matching materials in the mass transit system across 60 cities by the MetaSUB Consortium. The number of novel species is significantly and positively correlated with the city population, and more novel species can be identified in the skin-associated samples. The in-depth analysis of the new gene catalog showed that the functional terms have a significant geographical distinguishability. Moreover, we revealed that more biosynthetic gene clusters (BGCs) can be found in novel species. The co-occurrence relationship between BGCs and genera and the geographical specificity of BGCs can also provide us more information for the synthesis pathways of natural products. Expanded the known urban microbiome diversity and suggested additional mechanisms for taxonomic and functional characterization of the urban microbiome. Considering the great impact of urban microbiomes on human life, our study can also facilitate the microbial interaction analysis between human and urban environment. • Uncovering 732 novel OTUs from MetaSUB data of urban environmental microbiome. collected from 60 cities worldwide. • Exploring the association between novel OTUs and environmental factors, revealing their diversity related to environments • Similar to microbial composition, functional terms in MetaSUB samples also have a significant geographical distinguishability. • Mining geographical specificity of BGCs and highlighting the potential value of those novel OTUs. [ABSTRACT FROM AUTHOR]