Measurement set selection of parameter estimation in biological system modelling - a case study of signal transduction pathways

Parameter estimation is a challenging problem for biological systems modelling since the model is normally of high dimension, the measurement data are sparse and noisy and the cost of experiments high. Accurate recovery of parameters depends on the quality and quantity of measurement data. It is therefore important to know which measurements to be taken when and how through optimal experimental design (OED). In this paper a method was proposed to determine the most informative measurement set for parameter estimation of dynamic systems, in particular biochemical reaction systems, such that the unknown parameters can be inferred with the best possible statistical quality using the data collected from the designed experiments. System analysis using matrix theory was used to examine the number of necessary measurement variables. The priority of each measurement variable was determined by optimal experimental design based on Fisher information matrix (FIM). The applicability and advantages of the proposed method were shown through an example of signal pathway model

Determine measurement set for parameter estimation in biological systems modeling

Parameter estimation is challenging for biological systems modelling since the model is normally of high dimension, the measurement data are sparse and noisy, and the cost of experiments is high. Accurate recovery of parameters depend on the quantity and quality of measurement data. It is therefore important to know what measurements to be taken, when and how through optimal experimental design (OED). In this paper we present a method to determine the most informative measurement set for parameter estimation of dynamic systems, in particular biochemical reaction systems, such that the unknown parameters can be inferred with the best possible statistical quality using the data collected from the designed experiments. System analysis using matrix theory is introduced to examine the number of necessary measurement variables. The priority of each measurement variable is determined by optimal experimental design based on Fisher information matrix (FIM). The applicability and advantages of the proposed method are illustrated through an example of a signal pathway model

Transforming Bell's Inequalities into State Classifiers with Machine Learning

Quantum information science has profoundly changed the ways we understand, store, and process information. A major challenge in this field is to look for an efficient means for classifying quantum state. For instance, one may want to determine if a given quantum state is entangled or not. However, the process of a complete characterization of quantum states, known as quantum state tomography, is a resource-consuming operation in general. An attractive proposal would be the use of Bell's inequalities as an entanglement witness, where only partial information of the quantum state is needed. The problem is that entanglement is necessary but not sufficient for violating Bell's inequalities, making it an unreliable state classifier. Here we aim at solving this problem by the methods of machine learning. More precisely, given a family of quantum states, we randomly picked a subset of it to construct a quantum-state classifier, accepting only partial information of each quantum state. Our results indicated that these transformed Bell-type inequalities can perform significantly better than the original Bell's inequalities in classifying entangled states. We further extended our analysis to three-qubit and four-qubit systems, performing classification of quantum states into multiple species. These results demonstrate how the tools in machine learning can be applied to solving problems in quantum information science

Stable Large-Scale Perturbations in Interacting Dark-Energy Model

It is found that the evolutions of density perturbations on the super-Hubble scales are unstable in the model with dark-sector interaction $Q$ proportional to the energy density of cold dark matter (CDM) $\rho_m$ and constant equation of state parameter of dark energy $w_d$. In this paper, to avoid the instabilities, we suggest a new covariant model for the energy-momentum transfer between DE and CDM. Then we show that the the large-scale instabilities of curvature perturbations can be avoided in our model in the universe filled only by DE and CDM. Furthermore, by including the additional components of radiation and baryons, we calculate the dominant non-adiabatic modes in the radiation era and find that the modes grow in the power law with exponent at the order of unit.Comment: 14 pages, 2 figures. arXiv admin note: substantial text overlap with arXiv:1110.180

Hierarchical Exploration for Accelerating Contextual Bandits

Contextual bandit learning is an increasingly popular approach to optimizing recommender systems via user feedback, but can be slow to converge in practice due to the need for exploring a large feature space. In this paper, we propose a coarse-to-fine hierarchical approach for encoding prior knowledge that drastically reduces the amount of exploration required. Intuitively, user preferences can be reasonably embedded in a coarse low-dimensional feature space that can be explored efficiently, requiring exploration in the high-dimensional space only as necessary. We introduce a bandit algorithm that explores within this coarse-to-fine spectrum, and prove performance guarantees that depend on how well the coarse space captures the user's preferences. We demonstrate substantial improvement over conventional bandit algorithms through extensive simulation as well as a live user study in the setting of personalized news recommendation.Comment: Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012