DOCSLIB.ORG

Leveraging SAS with KNIME

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Leveraging SAS with KNIME Leveraging SAS with KNIME Thomas Gabriel and Phil Winters Copyright © 2013 by KNIME.com AG. All rights reserved. SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration. Copyright © 2013 by KNIME.com AG. All rights reserved. 2 A bit of History: The Original SAS Concept Access Manage Present Analyse Copyright © 2013 by KNIME.com AG. All rights reserved. 4 More Products to meet new requirements EG DI RTD DB2 Macro Oracle PC FILIE Connect Access Manage MM SCL Present Analyse EM ® AF DVD QC JMP OR IML IDP ETS STAT Graph Copyright © 2013 by KNIME.com AG. All rights reserved. 5 Even More Products…. Teradata Hadoop EG DI RTD DB2 Macro Oracle PC FILIE Connect Access Manage MM SCL Present Analyse EM PMML …Model Manager …Model PMML High Performance Analytics Performance High ® AF DVD QC JMP OR IML IDP ETS STAT Graph Text Mining Social Media Analytics R…. in IML in R…. Copyright © 2013 by KNIME.com AG. All rights reserved. 6 Our new reality: You must have Choice and Control New Infrastructures New Data New Other Science Applications New New Users Methods New Business Copyright © 2013 by KNIME.com AG. All rights reserved. Challenges 7 The KNIME Platform Open, Open Source, Free on the Desktop Copyright © 2013 by KNIME.com AG. All rights reserved. 8 The question is not “which is better”…. Copyright © 2013 by KNIME.com AG. All rights reserved. 9 The question is: What’s the Big Difference? SAS KNIME A script-oriented 4GL programming language A Script-free environment in four major parts: comprised of • The DATA step • Nodes and Connectors • Procedure steps • A macro language, • Metanodes and Flow variables for packaging a metaprogramming language • ODS statements • GUIs: are most often front-ends • GUI is the interface to facilitate SAS Program script generation • Through Proprietary Technology “Provide it all” • Through Open Source “Provide it all” Copyright © 2013 by KNIME.com AG. All rights reserved. KNIME User Training 10 KNIME: Free on the desktop including over 1000 native and embedded nodes: MySQL, Oracle, etc. ETL Statistics via BIRT SAS, SPSS, etc. Row, Data Mining PMML Excel, Flat, etc. Column Machine Learning XML Hive etc. Matrix Web Analytics Databases XML, PMML Text, Image Text Mining Excel, Flat, etc. Text, Doc, Image Time Series Network Analysis Hive etc. Web Crawlers Java Social Media Analysis Text, Doc, Image Industry Specific Python WEKA Industry Specific Community / 3rd Community / 3rd R R Community / 3rd Community / 3rd JFreeChart Copyright © 2013 by KNIME.com AG. All rights reserved. 11 Community / 3rd Some 3rd party nodes are charged Accessing Data SAS Data can be read Each Node directly has a specific dialog Huge Range of Read (and write) nodes available At no cost. Copyright © 2013 by KNIME.com AG. All rights reserved. KNIME User Training 13 Accessing Data Tables available at every node. Manually execute, start, stop, test nodes and sequences Copyright © 2013 by KNIME.com AG. All rights reserved. KNIME User Training 14 Transforming data Resource allocation concepts similar: balance IO, Memory, Resources Nodes for columns, rows, matrix ETL can be mixed and matched with all other nodes. No Separate Progrmming step required Copyright © 2013 by KNIME.com AG. All rights reserved. KNIME User Training 15 Packaging Workflows Drag and Mark nodes, Right Click, Collapse into Metanode To create a Metanode that can be reused Copyright © 2013 by KNIME.com AG. All rights reserved. KNIME User Training 16 Our Example: “Next Best Offer! The Access and Transform Metanode we created Copyright © 2013 by KNIME.com AG. All rights reserved. KNIME User Training 17 More Transformation Applying Color to Attributes KNIME nodes for transforming Copyright © 2013 by KNIME.com AG. All rights reserved. KNIME User Training 18 More Transformation Calling your Favorite External Packages such as Java, Python, Matlab, SQL, REST, Or your favorite Database Copyright © 2013 by KNIME.com AG. All rights reserved. KNIME User Training 19 Using SAS Transformation Calling SAS either Locally or Remotely. Automatically passing Data into and out of SAS Copyright © 2013 by KNIME.com AG. All rights reserved. KNIME User Training 20 Using SAS Transformation Calling SAS either Locally or Remotely. Automatically passing Data into and out of SAS Controlling how that happens with Flow Variables and Quickforms Copyright © 2013 by KNIME.com AG. All rights reserved. KNIME User Training 21 Giving you instant Customized Dialogs Copyright © 2013 by KNIME.com AG. All rights reserved. KNIME User Training 22 Giving you instant Customized Dialogs No Macro Code. No SCL. No Java. No Other Programming Language required. A Nice Dialog for many Copyright © 2013 by KNIME.com AG. All rights reserved. KNIME User Training 23 types of users….. Explore and Visualize Many nodes For Exploring and Visualizing All that allow Marking and highlighting across all nodes No seperate package required. Other packages can be used if available Copyright © 2013 by KNIME.com AG. All rights reserved. KNIME User Training 24 Be Open to All Methods, and let the best model win !!! • KNIME Decision Tree • KNIME Logistic Regression • R Decision Tree • PMML model (from SPSS) • Other Methods • Weka • KNIME Uplift (courtesy of Dymatrix) • SAS Copyright © 2013 by KNIME.com AG. All rights reserved. KNIME User Training 25 Allow for best practices without learning scripting • Missing Management • Partitioning • Binning and Bagging • Boosting • Logic and Flow Control • Cross Validation • Feature Elimination • Feature Selection • Error Handling KNIME examples available for all these techniques…. Copyright © 2013 by KNIME.com AG. All rights reserved. KNIME User Training 26 Report with your favorite Tool Or use the built-in and Free BIRT open source BI Tool Copyright © 2013 by KNIME.com AG. All rights reserved. KNIME User Training 27 Deploy the Models Score any Database Or how about scoring (including Hadoop) straight into SAS via Proc SQL and the (commercial) node from Dymatrix? Use the PMML on another system (Such as ADAPA) Copyright © 2013 by KNIME.com AG. All rights reserved. KNIME User Training 28 Deploy to Excel, XML, etc.….. Or directly back to SAS! SAS Transport Dataset Copyright © 2013 by KNIME.com AG. All rights reserved. KNIME User Training 29 Power Users in Teams The KNIME Server (Commercial Software) Copyright © 2013 by KNIME.com AG. All rights reserved. 31 Business Consumers via the web The KNIME Server (Commercial Software) Copyright © 2013 by KNIME.com AG. All rights reserved. 32 Business Consumers via the web The KNIME Server (Commercial Software) Copyright © 2013 by KNIME.com AG. All rights reserved. 33 Embedding, Automating, Security The KNIME Server (Commercial Software) KNIME Server KNIME Copyright © 2013 by KNIME.com AG. All rights reserved. 34 Hot Topics with KNIME (and SAS!) users: • Social Media Analysis • Recommendation/Next Best Offer/Market Basket/etc. Analysis • Text Mining • Network Analysis • Forecasting • Realtime • Machine Learning • Making R Usable • Automated Model generation, scoring and tuning with Dynamine from Dymatrix • Big Data Copyright © 2013 by KNIME.com AG. All rights reserved. 36 Thank you [email protected] Copyright © 2013 by KNIME.com AG. All rights reserved. .
Load more

Recommended publications
  • Text Mining Course for KNIME Analytics Platform
    Text Mining Course for KNIME Analytics Platform KNIME AG Copyright © 2018 KNIME AG Table of Contents 1. The Open Analytics Platform 2. The Text Processing Extension 3. Importing Text 4. Enrichment 5. Preprocessing 6. Transformation 7. Classification 8. Visualization 9. Clustering 10. Supplementary Workflows Licensed under a Creative Commons Attribution- ® Copyright © 2018 KNIME AG 2 Noncommercial-Share Alike license 1 https://creativecommons.org/licenses/by-nc-sa/4.0/ Overview KNIME Analytics Platform Licensed under a Creative Commons Attribution- ® Copyright © 2018 KNIME AG 3 Noncommercial-Share Alike license 1 https://creativecommons.org/licenses/by-nc-sa/4.0/ What is KNIME Analytics Platform? • A tool for data analysis, manipulation, visualization, and reporting • Based on the graphical programming paradigm • Provides a diverse array of extensions: • Text Mining • Network Mining • Cheminformatics • Many integrations, such as Java, R, Python, Weka, H2O, etc. Licensed under a Creative Commons Attribution- ® Copyright © 2018 KNIME AG 4 Noncommercial-Share Alike license 2 https://creativecommons.org/licenses/by-nc-sa/4.0/ Visual KNIME Workflows NODES perform tasks on data Not Configured Configured Outputs Inputs Executed Status Error Nodes are combined to create WORKFLOWS Licensed under a Creative Commons Attribution- ® Copyright © 2018 KNIME AG 5 Noncommercial-Share Alike license 3 https://creativecommons.org/licenses/by-nc-sa/4.0/ Data Access • Databases • MySQL, MS SQL Server, PostgreSQL • any JDBC (Oracle, DB2, …) • Files • CSV, txt
    [Show full text]
  • Imagej2-Allow the Users to Use Directly Use/Update Imagej2 Plugins Inside KNIME As Well As Recording and Running KNIME Workflows in Imagej2
    The KNIME Image Processing Extension for Biomedical Image Analysis Andries Zijlstra (Vanderbilt University Medical Center The need for image processing in medicine Kevin Eliceiri (University of Wisconsin-Madison) KNIME Image Processing and ImageJ Ecosystem [email protected] [email protected] The need for precision oncology 36% of newly diagnosed cancers, and 10% of all cancer deaths in men Out of every 100 men... 16 will be diagnosed with prostate cancer in their lifetime In reality, up to 80 will have prostate cancer by age 70 And 3 will die from it. But which 3 ? In the meantime, we The goal: Diagnose patients that have over-treat many aggressive disease through Precision Medicine patients Objectives of Approach to Modern Medicine Precision Medicine • Measure many things (data density) • Improved outcome through • Make very accurate measurements (fidelity) personalized/precision medicine • Consider multiple perspectives (differential) • Reduced expense/resource allocation through • Achieve confidence in the diagnosis improved diagnosis, prognosis, treatment • Match patients with a treatment they are most • Maximize quality of life by “targeted” therapy likely to respond to. Objectives of Approach to Modern Medicine Precision Medicine • Measure many things (data density) • Improved outcome through • Make very accurate measurements (fidelity) personalized/precision medicine • Consider multiple perspectives (differential) • Reduced expense/resource allocation through • Achieve confidence in the diagnosis improved diagnosis,
    [Show full text]
  • KNIME Workbench Guide
    KNIME Workbench Guide KNIME AG, Zurich, Switzerland Version 4.4 (last updated on 2021-06-08) Table of Contents Workspaces . 1 KNIME Workbench . 2 Welcome page . 4 Workflow editor & nodes . 5 KNIME Explorer . 13 Workflow Coach . 35 Node repository . 37 KNIME Hub view . 38 Description. 40 Node Monitor. 40 Outline. 41 Console. 41 Customizing the KNIME Workbench . 42 Reset and logging . 42 Show heap status . 42 Configuring KNIME Analytics Platform . 43 Preferences . 43 Setting up knime.ini. 47 KNIME runtime options . 49 KNIME tables . 55 Data table . 55 Column types. 56 Sorting . 59 Column rendering . 59 Table storage. 61 KNIME Workbench Guide This guide describes the first steps to take after starting KNIME Analytics Platform and points you to the resources available in the KNIME Workbench for building workflows. It also explains how to customize the workbench and configure KNIME Analytics Platform to best suit specific needs. In the last part of this guide we introduce data tables. Workspaces When you start KNIME Analytics Platform, the KNIME Analytics Platform launcher window appears and you are asked to define the KNIME workspace, as shown in Figure 1. The KNIME workspace is a folder on the local computer to store KNIME workflows, node settings, and data produced by the workflow. Figure 1. KNIME Analytics Platform launcher The workflows and data stored in the workspace are available through the KNIME Explorer in the upper left corner of the KNIME Workbench. © 2021 KNIME AG. All rights reserved. 1 KNIME Workbench Guide KNIME Workbench After selecting a workspace for the current project, click Launch. The KNIME Analytics Platform user interface - the KNIME Workbench - opens.
    [Show full text]
  • Data Analytics with Knime
    DATA ANALYTICS WITH KNIME v.3.4.0 QUALIFICATIONS & EXPERIENCE ▶ 38 years of providing professional services to state and local taxing officials ▶ TMA works exclusively with government partners WHO ▶ TMA is composed of 150+ WE ARE employees in five main offices across the United States Tax Management Associates is a professional services firm that has ▶ Our main focus is on revenue served the interests of state and local enhancement services for state government since 1979. and local jurisdictions and property tax compliance efforts KNIME POWERED CUSTOM ANALYTICS ▶ TMA is a proud KNIME Trusted Consulting Partner. Visit: www.knime.org/knime-trusted-partners ▶ Successful analytics solutions: ○ Fraud Detection (Michigan Department of Treasury) ○ Entity Discovery (multiple counties) ○ Data Aggregation (Louisiana State Tax Commission) KNIME POWERED CUSTOM ANALYTICS ▶ KNIME is an open source data toolkit ▶ Active development community and core team ▶ GUI based with scripting integration ○ Easy adoption, integration, and training ▶ Data ingestion, transformation, analytics, and reporting FEATURES & TERMINOLOGY KNIME WORKBENCH TAX MANAGEMENT ASSOCIATES, INC. KNIME WORKFLOW TAX MANAGEMENT ASSOCIATES, INC. KNIME NODES TAX MANAGEMENT ASSOCIATES, INC. DATA TYPES & SOURCES DATA AGNOSTIC ▶ Flat Files ▶ Shapefiles ▶ Xls/x Reader ▶ HTTP Requests ▶ Fixed Width ▶ RSS Feeds ▶ Text Files ▶ Custom API’s/Curl ▶ Image Files ▶ Standard API’s ▶ XML ▶ JSON TAX MANAGEMENT ASSOCIATES, INC. KNIME DATA NODES TAX MANAGEMENT ASSOCIATES, INC. DATABASE AGNOSTIC ▶ Microsoft SQL ▶ Oracle ▶ MySQL ▶ IBM DB2 ▶ Postgres ▶ Hadoop ▶ SQLite ▶ Any JDBC driver TAX MANAGEMENT ASSOCIATES, INC. KNIME DATABASE NODES TAX MANAGEMENT ASSOCIATES, INC. CORE DATA ANALYTICS FEATURES KNIME DATA ANALYTICS LIFECYCLE Read Data Extract, Data Analytics Reporting or Predictive Read Transform, and/or Load (ETL) Analysis Injection Data Read Data TAX MANAGEMENT ASSOCIATES, INC.
    [Show full text]
  • Sheffield HPC Documentation
    Sheffield HPC Documentation Release November 14, 2016 Contents 1 Research Computing Team 3 2 Research Software Engineering Team5 i ii Sheffield HPC Documentation, Release The current High Performance Computing (HPC) system at Sheffield, is the Iceberg cluster. A new system, ShARC (Sheffield Advanced Research Computer), is currently under development. It is not yet ready for use. Contents 1 Sheffield HPC Documentation, Release 2 Contents CHAPTER 1 Research Computing Team The research computing team are the team responsible for the iceberg service, as well as all other aspects of research computing. If you require support with iceberg, training or software for your workstations, the research computing team would be happy to help. Take a look at the Research Computing website or email research-it@sheffield.ac.uk. 3 Sheffield HPC Documentation, Release 4 Chapter 1. Research Computing Team CHAPTER 2 Research Software Engineering Team The Sheffield Research Software Engineering Team is an academically led group that collaborates closely with CiCS. They can assist with code optimisation, training and all aspects of High Performance Computing including GPU computing along with local, national, regional and cloud computing services. Take a look at the Research Software Engineering website or email rse@sheffield.ac.uk 2.1 Using the HPC Systems 2.1.1 Getting Started If you have not used a High Performance Computing (HPC) cluster, Linux or even a command line before this is the place to start. This guide will get you set up using iceberg in the easiest way that fits your requirements. Getting an Account Before you can start using iceberg you need to register for an account.
    [Show full text]
  • Mathematica Document
    Mathematica Project: Exploratory Data Analysis on ‘Data Scientists’ A big picture view of the state of data scientists and machine learning engineers. ����� ���� ��������� ��� ������ ���� ������ ������ ���� ������/ ������ � ���������� ���� ��� ������ ��� ���������������� �������� ������/ ����� ��������� ��� ���� ���������������� ����� ��������������� ��������� � ������(�������� ���������� ���������) ������ ��������� ����� ������� �������� ����� ������� ��� ������ ����������(���� �������) ��������� ����� ���� ������ ����� (���������� �������) ����������(���������� ������� ���������� ��� ���� ���� �����/ ��� �������������� � ����� ���� �� �������� � ��� ����/���������� ��������������� ������� ������������� ��� ���������� ����� �����(���� �������) ����������� ����� / ����� ��� ������ ��������������� ���������� ����������/�++ ������/������������/����/������ ���� ������� ����� ������� ������� ����������������� ������� ������� ����/����/�������/��/��� ����������(�����/����-�������� ��������) ������������ In this Mathematica project, we will explore the capabilities of Mathematica to better understand the state of data science enthusiasts. The dataset consisting of more than 10,000 rows is obtained from Kaggle, which is a result of ‘Kaggle Survey 2017’. We will explore various capabilities of Mathematica in Data Analysis and Data Visualizations. Further, we will utilize Machine Learning techniques to train models and Classify features with several algorithms, such as Nearest Neighbors, Random Forest. Dataset : https : // www.kaggle.com/kaggle/kaggle
    [Show full text]
  • Direct Submission Or Co-Submission Direct Submission
    Z-Matrix template-based substitution approach Title for enumeration of 3D molecular structures Authors Wanutcha Lorpaiboon and Taweetham Limpanuparb* Science Division, Mahidol University International College, Affiliations Mahidol University, Salaya, Nakhon Pathom 73170, Thailand Corresponding Author’s email address [email protected] • Chemical structures • Education Keywords • Molecular generator • Structure generator • Z-matrix Direct Submission or Co-Submission Direct Submission ABSTRACT The exhaustive enumeration of 3D chemical structures based on Z-matrix templates has recently been used in the quantum chemical investigation of constitutional isomers, diastereomers and 5 rotamers. This simple yet powerful initial structure generation approach can apply beyond the investigation of compounds of identical formula by quantum chemical methods. This paper aims to provide a short description of the overall concept followed by a practical tutorial to the approach. • The four steps required for Z-matrix template-based substitution are template construction, generation of tuples for substitution sites, removal of duplicate tuples and 10 substitution on the template. • The generated tuples can be used to create chemical identifiers to query compound properties from chemical databases. • All of these steps are demonstrated in this paper by common model compounds and are very straightforward for an undergraduate audience to reproduce. A comparison of the 15 approach in this tutorial and other options is also discussed. SPECIFICATIONS TABLE Subject Area Chemistry More specific subject area Cheminformatics Method name Z-matrix template-based substitution Name and reference of original method N/A Source codes are available as supplementary information in this Resource availability paper. 2 of 10 Method details 20 1. Introduction Initial structures (Z-matrix or Cartesian coordinate) are important starting points for the in silico investigation of chemical species.
    [Show full text]
  • Role of Materials Data Science and Informatics in Accelerated Materials Innovation Surya R
    Role of materials data science and informatics in accelerated materials innovation Surya R. Kalidindi , David B. Brough , Shengyen Li , Ahmet Cecen , Aleksandr L. Blekh , Faical Yannick P. Congo , and Carelyn Campbell The goal of the Materials Genome Initiative is to substantially reduce the time and cost of materials design and deployment. Achieving this goal requires taking advantage of the recent advances in data and information sciences. This critical need has impelled the emergence of a new discipline, called materials data science and informatics. This emerging new discipline not only has to address the core scientifi c/technological challenges related to datafi cation of materials science and engineering, but also, a number of equally important challenges around data-driven transformation of the current culture, practices, and workfl ows employed for materials innovation. A comprehensive effort that addresses both of these aspects in a synergistic manner is likely to succeed in realizing the vision of scaled-up materials innovation. Key toolsets needed for the successful adoption of materials data science and informatics in materials innovation are identifi ed and discussed in this article. Prototypical examples of emerging novel toolsets and their functionality are described along with select case studies. Introduction goal of reducing the time and cost of materials development Materials innovation initiatives and deployment by 50%. 1 Essential to achieving this goal is A number of US-based, 1 – 3 as well as international, 4 , 5 efforts are the development and deployment of a supporting infrastruc- now focused on accelerated deployment of advanced materials ture that integrates a wide range of data, experimental, and in commercial products.
    [Show full text]
  • Titel Untertitel
    KNIME Image Processing Nycomed Chair for Bioinformatics and Information Mining Department of Computer and Information Science Konstanz University, Germany Why Image Processing with KNIME? KNIME UGM 2013 2 The “Zoo” of Image Processing Tools Development Processing UI Handling ImgLib2 ImageJ OMERO OpenCV ImageJ2 BioFormats MatLab Fiji … NumPy CellProfiler VTK Ilastik VIGRA CellCognition … Icy Photoshop … = Single, individual, case specific, incompatible solutions KNIME UGM 2013 3 The “Zoo” of Image Processing Tools Development Processing UI Handling ImgLib2 ImageJ OMERO OpenCV ImageJ2 BioFormats MatLab Fiji … NumPy CellProfiler VTK Ilastik VIGRA CellCognition … Icy Photoshop … → Integration! KNIME UGM 2013 4 KNIME as integration platform KNIME UGM 2013 5 Integration: What and How? KNIME UGM 2013 6 Integration ImgLib2 • Developed at MPI-CBG Dresden • Generic framework for data (image) processing algoritms and data-structures • Generic design of algorithms for n-dimensional images and labelings • http://fiji.sc/wiki/index.php/ImgLib2 → KNIME: used as image representation (within the data cells); basis for algorithms KNIME UGM 2013 7 Integration ImageJ/Fiji • Popular, highly interactive image processing tool • Huge base of available plugins • Fiji: Extension of ImageJ1 with plugin-update mechanism and plugins • http://rsb.info.nih.gov/ij/ & http://fiji.sc/ → KNIME: ImageJ Macro Node KNIME UGM 2013 8 Integration ImageJ2 • Next-generation version of ImageJ • Complete re-design of ImageJ while maintaining backwards compatibility • Based on ImgLib2
    [Show full text]
  • Introduction to Label-Free Quantification
    SeqAn and OpenMS Integration Workshop Temesgen Dadi, Julianus Pfeuffer, Alexander Fillbrunn The Center for Integrative Bioinformatics (CIBI) Mass-spectrometry data analysis in KNIME Julianus Pfeuffer, Alexander Fillbrunn OpenMS • OpenMS – an open-source C++ framework for computational mass spectrometry • Jointly developed at ETH Zürich, FU Berlin, University of Tübingen • Open source: BSD 3-clause license • Portable: available on Windows, OSX, Linux • Vendor-independent: supports all standard formats and vendor-formats through proteowizard • OpenMS TOPP tools – The OpenMS Proteomics Pipeline tools – Building blocks: One application for each analysis step – All applications share identical user interfaces – Uses PSI standard formats • Can be integrated in various workflow systems – Galaxy – WS-PGRADE/gUSE – KNIME Kohlbacher et al., Bioinformatics (2007), 23:e191 OpenMS Tools in KNIME • Wrapping of OpenMS tools in KNIME via GenericKNIMENodes (GKN) • Every tool writes its CommonToolDescription (CTD) via its command line parser • GKN generates Java source code for nodes to show up in KNIME • Wraps C++ executables and provides file handling nodes Installation of the OpenMS plugin • Community-contributions update site (stable & trunk) – Bioinformatics & NGS • provides > 180 OpenMS TOPP tools as Community nodes – SILAC, iTRAQ, TMT, label-free, SWATH, SIP, … – Search engines: OMSSA, MASCOT, X!TANDEM, MSGFplus, … – Protein inference: FIDO Data Flow in Shotgun Proteomics Sample HPLC/MS Raw Data 100 GB Sig. Proc. Peak 50 MB Maps Data Reduction 1
    [Show full text]
  • Bringing Open Source to Drug Discovery
    Bringing Open Source to Drug Discovery Chris Swain Cambridge MedChem Consulting Standing on the shoulders of giants • There are a huge number of people involved in writing open source software • It is impossible to acknowledge them all individually • The slide deck will be available for download and includes 25 slides of details and download links – Copy on my website www.cambridgemedchemconsulting.com Why us Open Source software? • Allows access to source code – You can customise the code to suit your needs – If developer ceases trading the code can continue to be developed – Outside scrutiny improves stability and security What Resources are available • Toolkits • Databases • Web Services • Workflows • Applications • Scripts Toolkits • OpenBabel (htttp://openbabel.org) is a chemical toolbox – Ready-to-use programs, and complete programmer's toolkit – Read, write and convert over 110 chemical file formats – Filter and search molecular files using SMARTS and other methods, KNIME add-on – Supports molecular modeling, cheminformatics, bioinformatics – Organic chemistry, inorganic chemistry, solid-state materials, nuclear chemistry – Written in C++ but accessible from Python, Ruby, Perl, Shell scripts… Toolkits • OpenBabel • R • CDK • OpenCL • RDkit • SciPy • Indigo • NumPy • ChemmineR • Pandas • Helium • Flot • FROWNS • GNU Octave • Perlmol • OpenMPI Toolkits • RDKit (http://www.rdkit.org) – A collection of cheminformatics and machine-learning software written in C++ and Python. – Knime nodes – The core algorithms and data structures are written in C ++. Wrappers are provided to use the toolkit from either Python or Java. – Additionally, the RDKit distribution includes a PostgreSQL-based cartridge that allows molecules to be stored in relational database and retrieved via substructure and similarity searches.
    [Show full text]
  • Cheat Sheet: Building a KNIME Workflow for Beginners
    ® Cheat Sheet: Building a KNIME Workflow for Beginners Getting started with KNIME Analytics Platform EXPLORE ANALYZE 100.00101.27 ● Read through the installation guide at Scatter Plot: Represents Sunburst Chart: Displays Stacked Area Chart: Plots Decision Tree Decision Tree: The Learner node trains a C4.5 input data rows as points categorical columns through multiple numerical data 90.00 knime.com/installation 80.00 decision tree. The configuration window in a two dimensional plot. a hierarchy of rings. Each columns on top of each other 70.00 includes options for pruning, early stopping, ● Check out the 7 things you should do after installing Input dimensions ring is sliced according to using the previous line as the 60.00 information measures, splitting values, and KNIME Analytics Platform at (columns) on the x-y axis the nominal values in the base reference. The areas in 50.00 more. Both the Learner and the Predictor knime.com/blog/seven-things plot and graphical corresponding column and between lines are colored for 40.00 node provide an interactive view where the properties can be changed to the selected hierarchy. easier comparison. This chart 30.00 decision tree is displayed together with the 20.00 ● Take the E-Learning Course at in the configuration This is a powerful chart for is commonly used to visualize 10.00 input data propagation. knime.com/knime-introductory-course window or interactively in multivariate analysis. trending topics. 0.00 k-Means k-Means: Implements the k-Means clustering the node view. -10.00 -14.72 1.00 5.00 10.00 15.00 20.00 25.00 31.00 algorithm.
    [Show full text]