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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]

Crowdsourcing is a promising way of solving problems in a distributed manner within a stipulated time. The working principle of crowdsourcing platforms can be either competitive or collaborative. In both of this, crowd workers (solvers) get a remuneration either predecided through bidding or preannounced by the requester (task provider). Even being convenient, the curse of competitiveness often reduces the participation of workers. We highlight that by introducing collaboration in competitive crowdsourcing markets, we can handle decomposable-type tasks in an efficient way. Here, we propose a novel mechanism for the said purpose. If the tasks are decomposable, workers may get interest to collaborate on the subtasks. This increases the chance of receiving more number of cost-effective solutions. The proposed mechanism encourages the workers to collaborate by sharing the remuneration thoughtfully. Thus, both the requester and crowd workers get benefit from the system. We demonstrate the effectiveness of the proposed mechanism through empirical analysis. [ABSTRACT FROM AUTHOR]

Disease dietomics is an emerging area of systems biology that attempts to explore the connections between the dietary habits and diseases. Some of the topical studies highlight that foods might have different impacts over an organism either in progressing a disease (negative association) or in fighting against it (positive association). The association of foods with different diseases can be put together to build a network that might provide a global view of the entire system. Again, such disease-food networks might emerge in a more complex form while considering the disease subtypes individually. Some foods might have positive association with a particular subtype of a disease, whereas it might have no association or negative association with another subtype of the same disease. Therefore, the subtypes might have completely different network patterns. On the other hand, the same food may be helpful for a disease and harmful for another disease or even for a subtype. Analyzing such disease-food networks in different forms might give us important information about the relations between different diseases. In this paper, we have analyzed a large-scale disease-food network comprising 162 different diseases and 455 types of foods for gaining knowledge about the connection between these diseases and their subtypes. We have measured the similarity between diseases based on their patterns of association with foods. In addition to observing a high similarity between several disease subtypes, particularly for cancer, we have found strong relations between constipation–dysphagia and cancer–cardiovascular disease, which are rarely known. Tendency of occurrence of different diseases can be predicted based on such information. [ABSTRACT FROM PUBLISHER]

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]

In this work, we model the problem of mining quasi-bicliques from weighted viral-host protein-protein interaction network as a biclustering problem for identifying strong interaction modules. In this regard, a multiobjective genetic algorithm-based biclustering technique is proposed that simultaneously optimizes three objective functions to obtain dense biclusters having high mean interaction strengths. The performance of the proposed technique has been compared with that of other existing biclustering methods on an artificial data. Subsequently, the proposed biclustering method is applied on the records of biologically validated and predicted interactions between a set of HIV-1 proteins and a set of human proteins to identify strong interaction modules. For this, the entire interaction information is realized as a bipartite graph. We have further investigated the biological significance of the obtained biclusters. The human proteins involved in the strong interaction module have been found to share common biological properties and they are identified as the gateways of viral infection leading to various diseases. These human proteins can be potential drug targets for developing anti-HIV drugs. [ABSTRACT FROM PUBLISHER]

Two genes are said to be coexpressed if their expression levels have a similar spatial or temporal pattern. Ever since the profiling of gene microarrays has been in progress, computational modeling of coexpression has acquired a major focus. As a result, several similarity/distance measures have evolved over time to quantify coexpression similarity/dissimilarity between gene pairs. Of these, correlation coefficient has been established to be a suitable quantifier of pairwise coexpression. In general, correlation coefficient is good for symbolizing linear dependence, but not for nonlinear dependence. In spite of this drawback, it outperforms many other existing measures in modeling the dependency in biological data. In this paper, for the first time, we point out a significant weakness of the existing similarity/distance measures, including the standard correlation coefficient, in modeling pairwise coexpression of genes. A novel measure, called BioSim, which assumes values between -1 and +1 corresponding to negative and positive dependency and 0 for independency, is introduced. The computation of BioSim is based on the aggregation of stepwise relative angular deviation of the expression vectors considered. The proposed measure is analytically suitable for modeling coexpression as it accounts for the features of expression similarity, expression deviation and also the relative dependence. It is demonstrated how the proposed measure is better able to capture the degree of coexpression between a pair of genes as compared to several other existing ones. The efficacy of the measure is statistically analyzed by integrating it with several module-finding algorithms based on coexpression values and then applying it on synthetic and biological data. The annotation results of the coexpressed genes as obtained from gene ontology establish the significance of the introduced measure. By further extending the BioSim measure, it has been shown that one can effectively identify the variability in the expression patterns over multiple phenotypes. We have also extended BioSim to figure out pairwise differential expression pattern and coexpression dynamics. The significance of these studies is shown based on the analysis over several real-life data sets. The computation of the measure by focusing on stepwise time points also makes it effective to identify partially coexpressed genes. On the whole, we put forward a complete framework for coexpression analysis based on the BioSim measure. [ABSTRACT FROM PUBLISHER]