118,183 research outputs found
Super Natural II - a database of natural products
Natural products play a significant role in drug discovery and development.
Many topological pharmacophore patterns are common between natural products
and commercial drugs. A better understanding of the specific physicochemical
and structural features of natural products is important for corresponding
drug development. Several encyclopedias of natural compounds have been
composed, but the information remains scattered or not freely available. The
first version of the Supernatural database containing ∼50 000 compounds was
published in 2006 to face these challenges. Here we present a new, updated and
expanded version of natural product database, Super Natural II
(http://bioinformatics.charite.de/supernatural), comprising ∼326 000
molecules. It provides all corresponding 2D structures, the most important
structural and physicochemical properties, the predicted toxicity class for
∼170 000 compounds and the vendor information for the vast majority of
compounds. The new version allows a template-based search for similar
compounds as well as a search for compound names, vendors, specific physical
properties or any substructures. Super Natural II also provides information
about the pathways associated with synthesis and degradation of the natural
products, as well as their mechanism of action with respect to structurally
similar drugs and their target proteins
Super natural II -a database of natural products
Natural products play a significant role in drug discovery and development. Many topological pharmacophore patterns are common between natural products and commercial drugs. A better understanding of the specific physicochemical and structural features of natural products is important for corresponding drug development. Several encyclopedias of natural compounds have been composed, but the information remains scattered or not freely available. The first version of the Supernatural database containing ∼ 50,000 compounds was published in 2006 to face these challenges. Here we present a new, updated and expanded version of natural product database, Super Natural II (http://bioinformatics.charite.de/supernatural), comprising ∼ 326,000 molecules. It provides all corresponding 2D structures, the most important structural and physicochemical properties, the predicted toxicity class for ∼ 170,000 compounds and the vendor information for the vast majority of compounds. The new version allows a template-based search for similar compounds as well as a search for compound names, vendors, specific physical properties or any substructures. Super Natural II also provides information about the pathways associated with synthesis and degradation of the natural products, as well as their mechanism of action with respect to structurally similar drugs and their target proteins
Deep Markov Random Field for Image Modeling
Markov Random Fields (MRFs), a formulation widely used in generative image
modeling, have long been plagued by the lack of expressive power. This issue is
primarily due to the fact that conventional MRFs formulations tend to use
simplistic factors to capture local patterns. In this paper, we move beyond
such limitations, and propose a novel MRF model that uses fully-connected
neurons to express the complex interactions among pixels. Through theoretical
analysis, we reveal an inherent connection between this model and recurrent
neural networks, and thereon derive an approximated feed-forward network that
couples multiple RNNs along opposite directions. This formulation combines the
expressive power of deep neural networks and the cyclic dependency structure of
MRF in a unified model, bringing the modeling capability to a new level. The
feed-forward approximation also allows it to be efficiently learned from data.
Experimental results on a variety of low-level vision tasks show notable
improvement over state-of-the-arts.Comment: Accepted at ECCV 201
Deep learning approach to describe and classify fungi microscopic images
Preliminary diagnosis of fungal infections can rely on microscopic
examination. However, in many cases, it does not allow unambiguous
identification of the species by microbiologist due to their visual similarity.
Therefore, it is usually necessary to use additional biochemical tests. That
involves additional costs and extends the identification process up to 10 days.
Such a delay in the implementation of targeted therapy may be grave in
consequence as the mortality rate for immunosuppressed patients is high. In
this paper, we apply a machine learning approach based on deep neural networks
and Fisher Vector (advanced bag-of-words method) to classify microscopic images
of various fungi species. Our approach has the potential to make the last stage
of biochemical identification redundant, shortening the identification process
by 2-3 days, and reducing the cost of the diagnosis
Overlapping Free Trade Agreements of Singapore-USA-Japan: A Computational Analysis
The proliferation of overlapping free trade agreements (FTA) in the recent years has led to hub-and-spokes (HAS) throughout the world. Being avid subscribers to FTAs, many countries in the Asia-Pacific region including the USA, Japan, Singapore, South Korea, Thailand and Australia have become trade hubs to their partners who are in turn relegated to spoke status. In this paper, we question whether being a hub is welfare optimal for a small and open economy like Singapore compared to membership in a single bilateral FTA or a multi- member free trade zone. Within this context, we use a computable general equilibrium model to examine the welfare implications of the triangular trade relationship of the USA, Singapore and Japan. This is facilitated by the Japan- Singapore Economic Partnership Agreement, the USA-Singapore Free Trade Agreement, and a hypothetical USA-Japan Economic Partnership Agreement. The analysis is extended to incorporate “super-hub” effects; that is, the spoke countries can be trade hubs in other HAS systems. The experiment reveals that hub status generates positive welfare gain and is the highest Singapore can get from the trade configurations considered. Meanwhile, Japan loses more than the USA when both are relegated to spoke status. These findings prove robust under different market structures and production technologies, deeper economic integration, “super-hub” effects, as well as, uncertainty in the key model parameters and the extent of trade liberalisation shocks.hub and spokes; overlapping agreements; free trade; preference dilution; computable general equilibrium; GTAP; systems; trade configurations
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