From data towards knowledge: Revealing the architecture of signaling systems by unifying knowledge mining and data mining of systematic perturbation data

Genetic and pharmacological perturbation experiments, such as deleting a gene and monitoring gene expression responses, are powerful tools for studying cellular signal transduction pathways. However, it remains a challenge to automatically derive knowledge of a cellular signaling system at a conceptual level from systematic perturbation-response data. In this study, we explored a framework that unifies knowledge mining and data mining approaches towards the goal. The framework consists of the following automated processes: 1) applying an ontology-driven knowledge mining approach to identify functional modules among the genes responding to a perturbation in order to reveal potential signals affected by the perturbation; 2) applying a graph-based data mining approach to search for perturbations that affect a common signal with respect to a functional module, and 3) revealing the architecture of a signaling system organize signaling units into a hierarchy based on their relationships. Applying this framework to a compendium of yeast perturbation-response data, we have successfully recovered many well-known signal transduction pathways; in addition, our analysis have led to many hypotheses regarding the yeast signal transduction system; finally, our analysis automatically organized perturbed genes as a graph reflecting the architect of the yeast signaling system. Importantly, this framework transformed molecular findings from a gene level to a conceptual level, which readily can be translated into computable knowledge in the form of rules regarding the yeast signaling system, such as "if genes involved in MAPK signaling are perturbed, genes involved in pheromone responses will be differentially expressed"

Quark helicity distributions in transverse momentum space and transverse coordinate space

The transverse momentum dependent helicity distributions of valence quarks are calculated in the light-cone diquark model by adopting two different approaches. We use the model results to analyze the $P_{h\perp}$-dependent double spin asymmetries for $\pi^+$, $\pi^-$ and $\pi^0$ productions in semi-inclusive deep inelastic scattering, and find that the asymmetries agree with the CLAS data in one of the approaches. By taking the Fourier transform of the transverse momentum dependent helicity distributions, we obtain the helicity distributions of valence quarks in the transverse coordinate space, and then apply them further to predict the Bessel-weighted double spin asymmetries of $\pi^+$, $\pi^-$ and $\pi^0$ productions in semi-inclusive deep inelastic scattering at CLAS, COMPASS and HERMES for the first time. The shape of the Bessel-weighted double spin asymmetry thereby provides a direct probe on the transverse structure of longitudinally polarized quarks.Comment: References added, version published in PR

EMC effect in semi-inclusive deep-inelastic scattering process

By considering the $x$-dependence of $\pi^+$, $\pi^-$, $K^+$, $K^-$, $\Lambda$, $\bar{\Lambda}$, $p$, $\bar{p}$ hadron productions in charged lepton semi-inclusive deep inelastic scattering off nuclear target (using Fe as an example) and deuteron D target, % at $Q^2=5$ GeV$^2$, we find that $(\bar{\Lambda}^A/\Lambda^A)/(\bar{\Lambda}^D/\Lambda^D)$ and $({\bar{p}}^A/{p}^A)/({\bar{p}}^A/p^A)$ are ideal to figure out the nuclear sea content, which is predicted to be different by different models accounting for the nuclear EMC effect.Comment: 21 latex pages, 15 figure