2,044 research outputs found
Language Use Matters: Analysis of the Linguistic Structure of Question Texts Can Characterize Answerability in Quora
Quora is one of the most popular community Q&A sites of recent times.
However, many question posts on this Q&A site often do not get answered. In
this paper, we quantify various linguistic activities that discriminates an
answered question from an unanswered one. Our central finding is that the way
users use language while writing the question text can be a very effective
means to characterize answerability. This characterization helps us to predict
early if a question remaining unanswered for a specific time period t will
eventually be answered or not and achieve an accuracy of 76.26% (t = 1 month)
and 68.33% (t = 3 months). Notably, features representing the language use
patterns of the users are most discriminative and alone account for an accuracy
of 74.18%. We also compare our method with some of the similar works (Dror et
al., Yang et al.) achieving a maximum improvement of ~39% in terms of accuracy.Comment: 1 figure, 3 tables, ICWSM 2017 as poste
Information theoretical study of cross-talk mediated signal transduction in MAPK pathways
Biochemical networks related to similar functional pathways are often
correlated due to cross-talk among the homologous proteins in the different
networks. Using a stochastic framework, we address the functional significance
of the cross-talk between two pathways. Our theoretical analysis on generic
MAPK pathways reveals cross-talk is responsible for developing coordinated
fluctuations between the pathways. The extent of correlation evaluated in terms
of the information theoretic measure provides directionality to net information
propagation. Stochastic time series and scattered plot suggest that the
cross-talk generates synchronization within a cell as well as in a cellular
population. Depending on the number of input and output, we identify signal
integration and signal bifurcation motif that arise due to inter-pathway
connectivity in the composite network. Analysis using partial information
decomposition quantifies the net synergy in the information propagation through
these branched pathways.Comment: Revised version, 17 pages, 5 figure
Role of relaxation time scale in noisy signal transduction
Intracellular fluctuations, mainly triggered by gene expression, are an
inevitable phenomenon observed in living cells. It influences generation of
phenotypic diversity in genetically identical cells. Such variation of cellular
components is beneficial in some contexts but detrimental in others. To
quantify the fluctuations in a gene product, we undertake an analytical scheme
for studying few naturally abundant linear as well as branched chain network
motifs. We solve the Langevin equations associated with each motif under the
purview of linear noise approximation and quantify Fano factor and mutual
information. Both quantifiable expressions exclusively depend on the relaxation
time (decay rate constant) and steady state population of the network
components. We investigate the effect of relaxation time constraints on Fano
factor and mutual information to indentify a time scale domain where a network
can recognize the fluctuations associated with the input signal more reliably.
We also show how input population affects both quantities. We extend our
calculation to long chain linear motif and show that with increasing chain
length, the Fano factor value increases but the mutual information processing
capability decreases. In this type of motif, the intermediate components are
shown to act as a noise filter that tune up input fluctuations and maintain
optimum fluctuations in the output. For branched chain motifs, both quantities
vary within a large scale due to their network architecture and facilitate
survival of living system in diverse environmental conditions.Comment: 14 pages, 6 figure
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