Total S.A.

About: Total S.A. is a company organization based out in Paris, France. It is known for research contribution in the topics: Catalysis & Polymerization. The organization has 2465 authors who have published 2495 publications receiving 69593 citations.

Scikit-learn: Machine Learning in Python

Abstract: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.

Impact of Zeolites on the Petroleum and Petrochemical Industry

Abstract: The general features of zeolites that led to their widespread use in oil refining and petrochemistry are highlighted as well as the details of their impact on selected processes. The analysis of the catalyst market and the position of zeolites therein is a good indication of their strategic importance. Zeolites have brought many disruptive changes to these fields (e.g. FCC). They impacted also these industries in an equally important way, although more subtle, by incremental improvement of processes. The new and vast challenges facing oil refining and petrochemistry as well as the managed transition to sustainable environmental benign transport fuel industries and chemical industries will require creative science and technologies. Zeolites offer the basis of many of these technological solutions provided efficient and balanced cooperations between industry and academia are further developed.

The BioPAX community standard for pathway data sharing

Abstract: Biological Pathway Exchange (BioPAX) is a standard language to represent biological pathways at the molecular and cellular level and to facilitate the exchange of pathway data. The rapid growth of the volume of pathway data has spurred the development of databases and computational tools to aid interpretation; however, use of these data is hampered by the current fragmentation of pathway information across many databases with incompatible formats. BioPAX, which was created through a community process, solves this problem by making pathway data substantially easier to collect, index, interpret and share. BioPAX can represent metabolic and signaling pathways, molecular and genetic interactions and gene regulation networks. Using BioPAX, millions of interactions, organized into thousands of pathways, from many organisms are available from a growing number of databases. This large amount of pathway data in a computable form will support visualization, analysis and biological discovery.