BID - Bringing Integration to Data

Bringing metabolic networks to life: integration of kinetic, metabolic, and proteomic data Supplementary material

Symbol Name N = (nil) stoichiometric matrix W = (wli) regulation matrix s = (si) vector of stationary metabolite concentrations j = (jl) vector of stationary fluxes vl(·) kinetic law of reaction l k i energy constant for metabolite i k l velocity constant for reaction l k li reactant constant for reaction l and metabolite i k li activation constant for reaction l and metabolite i k li inhibitio...

Cross-Modal Integration: Bringing Coherence to the Sensory World

The multisensory superior colliculus (SC) neuron has been used as an effective model for exploring the neural bases of multisensory integration, in part because of the high incidence of such neurons in the deep layers of the structure and, in part, because of the important role of deep layer neurons in overt attentive and orientation behavior. These factors make it possible not only to obtain a...

Integration of remote sensing and meteorological data to predict flooding time using deep learning algorithm

Accurate flood forecasting is a vital need to reduce its risks. Due to the complicated structure of flood and river flow, it is somehow difficult to solve this problem. Artificial neural networks, such as frequent neural networks, offer good performance in time series data. In recent years, the use of Long Short Term Memory networks hase attracted much attention due to the faults of frequent ne...

Data explosion: bringing order to chaos with bioinformatics.

Bacterial vaginosis is characterized by a shift from the predominant lactobacillus vaginal flora to an overgrowth of anaerobic bacteria. Bacterial vaginosis is associated with an increased risk of gyne-cologic complications, including pelvic inflammatory disease, postoperative infection, cervicitis, human immunodeficiency virus (HIV), and possibly cervical intraepithelial neoplasia (CIN). The o...

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