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How data mining works
 
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In this video we describe data mining, in the context of knowledge discovery in databases. More videos on classification algorithms can be found at https://www.youtube.com/playlist?list=PLXMKI02h3_qjYoX-f8uKrcGqYmaqdAtq5 Please subscribe to my channel, and share this video with your peers!
Views: 234287 Thales Sehn Körting
Bioinformatics part 2 Databases (protein and nucleotide)
 
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For more information, log on to- http://shomusbiology.weebly.com/ Download the study materials here- http://shomusbiology.weebly.com/bio-materials.html This video is about bioinformatics databases like NCBI, ENSEMBL, ClustalW, Swisprot, SIB, DDBJ, EMBL, PDB, CATH, SCOPE etc. Bioinformatics Listeni/ˌbaɪ.oʊˌɪnfərˈmætɪks/ is an interdisciplinary field that develops and improves on methods for storing, retrieving, organizing and analyzing biological data. A major activity in bioinformatics is to develop software tools to generate useful biological knowledge. Bioinformatics uses many areas of computer science, mathematics and engineering to process biological data. Complex machines are used to read in biological data at a much faster rate than before. Databases and information systems are used to store and organize biological data. Analyzing biological data may involve algorithms in artificial intelligence, soft computing, data mining, image processing, and simulation. The algorithms in turn depend on theoretical foundations such as discrete mathematics, control theory, system theory, information theory, and statistics. Commonly used software tools and technologies in the field include Java, C#, XML, Perl, C, C++, Python, R, SQL, CUDA, MATLAB, and spreadsheet applications. In order to study how normal cellular activities are altered in different disease states, the biological data must be combined to form a comprehensive picture of these activities. Therefore, the field of bioinformatics has evolved such that the most pressing task now involves the analysis and interpretation of various types of data. This includes nucleotide and amino acid sequences, protein domains, and protein structures.[9] The actual process of analyzing and interpreting data is referred to as computational biology. Important sub-disciplines within bioinformatics and computational biology include: the development and implementation of tools that enable efficient access to, use and management of, various types of information. the development of new algorithms (mathematical formulas) and statistics with which to assess relationships among members of large data sets. For example, methods to locate a gene within a sequence, predict protein structure and/or function, and cluster protein sequences into families of related sequences. The primary goal of bioinformatics is to increase the understanding of biological processes. What sets it apart from other approaches, however, is its focus on developing and applying computationally intensive techniques to achieve this goal. Examples include: pattern recognition, data mining, machine learning algorithms, and visualization. Major research efforts in the field include sequence alignment, gene finding, genome assembly, drug design, drug discovery, protein structure alignment, protein structure prediction, prediction of gene expression and protein--protein interactions, genome-wide association studies, and the modeling of evolution. Bioinformatics now entails the creation and advancement of databases, algorithms, computational and statistical techniques, and theory to solve formal and practical problems arising from the management and analysis of biological data. Over the past few decades rapid developments in genomic and other molecular research technologies and developments in information technologies have combined to produce a tremendous amount of information related to molecular biology. Bioinformatics is the name given to these mathematical and computing approaches used to glean understanding of biological processes. Source of the article published in description is Wikipedia. I am sharing their material. Copyright by original content developers of Wikipedia. Link- http://en.wikipedia.org/wiki/Main_Page
Views: 99367 Shomu's Biology
Bioinformatics part 1 What is Bioinformatics
 
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For more information, log on to- http://shomusbiology.weebly.com/ Download the study materials here- http://shomusbiology.weebly.com/bio-materials.html Bioinformatics Listeni/ˌbaɪ.oʊˌɪnfərˈmætɪks/ is an interdisciplinary field that develops and improves on methods for storing, retrieving, organizing and analyzing biological data. A major activity in bioinformatics is to develop software tools to generate useful biological knowledge. Bioinformatics uses many areas of computer science, mathematics and engineering to process biological data. Complex machines are used to read in biological data at a much faster rate than before. Databases and information systems are used to store and organize biological data. Analyzing biological data may involve algorithms in artificial intelligence, soft computing, data mining, image processing, and simulation. The algorithms in turn depend on theoretical foundations such as discrete mathematics, control theory, system theory, information theory, and statistics. Commonly used software tools and technologies in the field include Java, C#, XML, Perl, C, C++, Python, R, SQL, CUDA, MATLAB, and spreadsheet applications.[1][2][3] Source of the article published in description is Wikipedia. I am sharing their material. Copyright by original content developers of Wikipedia. Link- http://en.wikipedia.org/wiki/Main_Page
Views: 235592 Shomu's Biology
Single Cell Orange 01: Dive into scOrange
 
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Introduction to Orange Single Cell software. scOrange is a specialized tool for the analysis of single cell RNA expression data. Design: Agnieszka Rovšnik Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana
Views: 1165 Orange Data Mining
Datamining in Science: Mining Patterns in Protein StructuresΓÇöAlgorithms and Applications
 
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With the data explosion occurring in sciences, utilizing tools to help analyze the data efficiently is becoming increasingly important. This session will describe tools included with SQL Server (Yukon), and Wei Wang will describe the MotifSpace projectΓÇöa comprehensive database of candidate spatial protein motifs based on recently developed data mining algorithms. One of the next great frontiers in molecular biology is to understand and predict protein function. Proteins are simple linear chains of polymerized amino acids (residues) whose biological functions are determined by the three-dimensional shapes that they fold into. A popular approach to understanding proteins is to break them down into structural sub-components called motifs. Motifs are recurring structural and spatial units that are frequently correlated with specific protein functions. Traditionally, the discovery of motifs has been a laborious task of scientific exploration. In this talk, I will discuss recent data-mining algorithms that we have developed for automatically identifying potential spatial motifs. Our methods automatically find frequently occurring substructures within graph-based representations of proteins. The complexity of protein structures and corresponding graphs poses significant computational challenges. The kernel of our approach is an efficient subgraph-mining algorithm that detects all (maximal) frequent subgraphs from a graph database with a user-specified minimal frequency.
Views: 116 Microsoft Research
Bioinformatics part 3 Sequence alignment introduction
 
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This Bioinformatics lecture explains the details about the sequence alignment. The mechanism and protocols of sequence alignment is explained in this video lecture on Bioinformatics. For more information, log on to- http://shomusbiology.weebly.com/ Download the study materials here- http://shomusbiology.weebly.com/bio-materials.html In bioinformatics, a sequence alignment is a way of arranging the sequences of DNA, RNA, or protein to identify regions of similarity that may be a consequence of functional, structural, or evolutionary relationships between the sequences.[1] Aligned sequences of nucleotide or amino acid residues are typically represented as rows within a matrix. Gaps are inserted between the residues so that identical or similar characters are aligned in successive columns. Sequence alignments are also used for non-biological sequences, such as those present in natural language or in financial data. Very short or very similar sequences can be aligned by hand. However, most interesting problems require the alignment of lengthy, highly variable or extremely numerous sequences that cannot be aligned solely by human effort. Instead, human knowledge is applied in constructing algorithms to produce high-quality sequence alignments, and occasionally in adjusting the final results to reflect patterns that are difficult to represent algorithmically (especially in the case of nucleotide sequences). Computational approaches to sequence alignment generally fall into two categories: global alignments and local alignments. Calculating a global alignment is a form of global optimization that "forces" the alignment to span the entire length of all query sequences. By contrast, local alignments identify regions of similarity within long sequences that are often widely divergent overall. Local alignments are often preferable, but can be more difficult to calculate because of the additional challenge of identifying the regions of similarity. A variety of computational algorithms have been applied to the sequence alignment problem. These include slow but formally correct methods like dynamic programming. These also include efficient, heuristic algorithms or probabilistic methods designed for large-scale database search, that do not guarantee to find best matches. Global alignments, which attempt to align every residue in every sequence, are most useful when the sequences in the query set are similar and of roughly equal size. (This does not mean global alignments cannot end in gaps.) A general global alignment technique is the Needleman--Wunsch algorithm, which is based on dynamic programming. Local alignments are more useful for dissimilar sequences that are suspected to contain regions of similarity or similar sequence motifs within their larger sequence context. The Smith--Waterman algorithm is a general local alignment method also based on dynamic programming. Source of the article published in description is Wikipedia. I am sharing their material. Copyright by original content developers of Wikipedia. Link- http://en.wikipedia.org/wiki/Main_Page
Views: 172661 Shomu's Biology
Introduction to bash for data analysis
 
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For absolute beginners. Using the command-line/shell/terminal for basic data analysis. This video covers how to find the terminal, navigating around the file system, looking at files, editing files, and even using piping to string together different commands and unlock the power of bash. The code is at http://omgenomics.com/bash-intro
Views: 9539 OMGenomics
Building data mining Taverna workflows
 
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Tutorial on building data mining Taverna workflows the e-LICO project tools
Views: 2044 mygridorguk
Advanced Data Mining with Weka (2.3: The MOA interface)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 3: The MOA interface http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/4vZhuc https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 3791 WekaMOOC
MSBI - SSAS - Data Mining - SEQUENCE CLUSTERING
 
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MSBI - SSAS - Data Mining - SEQUENCE CLUSTERING
Views: 400 M R Dhandhukia
Efficient Data Mining for Personalized Medicine
 
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Increasingly, patient care will rely on integration of highly complex and multifaceted clinical and molecular data, such as clinical genomics and other OMICS data. The enormous volume of accumulated data has challenged software developers, scientists, and clinicians because investigators and their work are separated by therapeutic area and or developmental stage, location of data in large internal siloes, and the need to efficiently interpret and analyze data in clinically useful ways. Integration of internal knowledge with external data from open and commercial sources has also proved challenging. The tranSMART Knowledge Management Platform, an open-source knowledge-management platform, has allowed medical and life science researchers to advance translational science and clinical research through information sharing and collaboration. By combining a data repository with intuitive search capabilities and analysis tools, the tranSMART system provides researchers a single self-service web portal with access to phenotypic, omics, and unstructured text-based data from multiple internal and external sources. tranSMART can, for example, help scientists develop and refine research hypotheses by investigating correlations between genetic and phenotypic data, and assessing their analytical results in the context of published literature and other work. Optimal use of tranSMART may require substantial expertise in life sciences IT, bioinformatics, semantics, and biology. During this webinar, experts from tranSMART and Thomson Reuters will present features of the tranSMART platform, and describe services offered by Thomson Reuters that can facilitate full and efficient use of the platform through consulting services in installation and IT support, data annotation and data mining as well as application plug-ins. The integration of tranSMART into a data exchange portal at a major public private partnerships will also be described.
Views: 892 GENNews
Data Mining Tool: sending genes to Pubmed
 
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Do a query and then see what genes and terms are co-cited in the literature.
Views: 38 QMRIBioinf
Data Repositories and Web Tools for Data Mining
 
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2015 Network Analysis Short Course - Systems Biology Analysis Methods for Genomic Data Speaker: Giovanni Coppola, UCLA The goal of the network analysis workshop is to familiarize researchers with network methods and software for integrating genomic data sets with complex phenotype data. Students will learn how to integrate disparate data sets (genetic variation, gene expression, epigenetic, protein interaction networks, complex phenotypes, gene ontology information) and use networks for identifying disease genes, pathways and key regulators.
The Sentieon Genomic Tools - Enabling Precision Data for Precision Medicine
 
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The Sentieon Genomics Tools provide identical results to the GATK pipelines with a 10x reduction in runtime, a robust software implementation, and deterministic data processing. This webcast will explore the benefits of the Sentieon Genomics Tools including a discussion of the results of the PrecisionFDA Truth and Consistency challenges and the ICGC-TCGA DREAM Mutation Calling Challenge for somatic SNV, indel, and structural variants. Golden Helix has partnered with Sentieon to integrate its secondary analysis tools with Golden Helix software to provide users with a comprehensive solution for genomic data analysis. In this webinar, Dr. Andreas delves into the new partnership, followed by an overview of the Sentieon software by Dr. Donald Freed.
Views: 378 Golden Helix Inc.
Getting Started with Orange 04: Loading Your Data
 
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Loading your data in Orange from Google sheets or Excel. License: GNU GPL + CC Music by: http://www.bensound.com/ Website: http://orange.biolab.si/ Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana
Views: 64947 Orange Data Mining
Getting Started with Orange 15: Image Analytics - Classification
 
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How to use embeddings for image classification and what can misclassifications tell us. Images kindly provided by: The Bouq at https://bouqs.com/ License: GNU GPL + CC Music by: http://www.bensound.com/ Website: https://orange.biolab.si/ Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana
Views: 19158 Orange Data Mining
Database Mining
 
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Views: 173 Sean Patrick Altea
Machine Learning with Orange - Tutorial
 
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As a joke amongst friends, I made this video on Machine Learning using this amazing data mining tool known as Orange. It makes the process of understanding machine learning a lot easier. For more information check out : https://www.youtube.com/channel/UClKKWBe2SCAEyv7ZNGhIe4g
Views: 571 G C KEERTHI Vasan
T-BioInfo - an intuitive interface for complex bioinformatics tasks
 
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T-BioInfo Platform offers a wide array of tools designed to process various omics data types, integrate them and analyze using supervised and unsupervised data mining methods to extract meaningful insights. Learn more at http://t-bio.info
Views: 453 Pine Biotech
"How machine learning helps cancer research" by Evelina Gabasova
 
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Machine learning methods are being applied in many different areas - from analyzing financial stock markets to movie recommender engines. But the same methods can be applied to other areas that also deal with big messy data. In bioinformatics I use similar machine learning, only this time to help find the underlying mechanisms of cancer. The problems in bioinformatics might seem opaque and confusing - sequencing, DNA, methylation, ChIP-seq, motifs etc. But underneath, the same algorithms that are used to find groups of customers based on their buying behavior can be used to find subtypes of cancer that respond differently to treatments. Algorithms for text analysis can be used to find important patterns in DNA strands. And software verification tools can help analyze biological systems. In this talk, I'll show you the exciting world of machine learning applications in bioinformatics. No knowledge of biology is required, the talk will be mostly in developer-speak. Evelina Gabasova UNIVERSITY OF CAMBRIDGE @evelgab Evelina is a machine learning researcher working in bioinformatics, trying to reverse-engineer cancer at University of Cambridge. Her background is mainly in computer science, statistics and machine learning. Evelina is a big fan of F# and uses it frequently for data manipulation and exploratory analysis in her research. Outside of academia, she also speaks at developer conferences and user groups about using F# for data science. She writes a blog at http://www.evelinag.com.
Views: 10307 Strange Loop
Getting Started with Orange 01: Welcome to Orange
 
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Introduction to Orange data mining software. Learn about the development of Orange workflows, data loading, basic machine learning algorithms and interactive visualizations. Download Orange from: https://orange.biolab.si/download/ License: GNU GPL + CC Music by: http://www.bensound.com/ Website: http://orange.biolab.si/ Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana
Views: 184497 Orange Data Mining
Decision Tree with Solved Example in English | DWM | ML | BDA
 
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Take the Full Course of Artificial Intelligence What we Provide 1) 28 Videos (Index is given down) 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in Artificial Intelligence Sample Notes : https://goo.gl/aZtqjh To buy the course click https://goo.gl/H5QdDU if you have any query related to buying the course feel free to email us : [email protected] Other free Courses Available : Python : https://goo.gl/2gftZ3 SQL : https://goo.gl/VXR5GX Arduino : https://goo.gl/fG5eqk Raspberry pie : https://goo.gl/1XMPxt Artificial Intelligence Index 1)Agent and Peas Description 2)Types of agent 3)Learning Agent 4)Breadth first search 5)Depth first search 6)Iterative depth first search 7)Hill climbing 8)Min max 9)Alpha beta pruning 10)A* sums 11)Genetic Algorithm 12)Genetic Algorithm MAXONE Example 13)Propsotional Logic 14)PL to CNF basics 15) First order logic solved Example 16)Resolution tree sum part 1 17)Resolution tree Sum part 2 18)Decision tree( ID3) 19)Expert system 20) WUMPUS World 21)Natural Language Processing 22) Bayesian belief Network toothache and Cavity sum 23) Supervised and Unsupervised Learning 24) Hill Climbing Algorithm 26) Heuristic Function (Block world + 8 puzzle ) 27) Partial Order Planing 28) GBFS Solved Example
Views: 252058 Last moment tuitions
Advanced Data Mining with Weka (2.6: Application to Bioinformatics – Signal peptide prediction)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 6: Application to Bioinformatics – Signal peptide prediction http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/4vZhuc https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 2983 WekaMOOC
Decision Tree Induction (in Hindi)
 
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This Video is about Decision Tree Classification in Data Mining.
Views: 22335 Red Apple Tutorials
Spectral Orange: Preprocess
 
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How to construct a preprocessing pipeline for spectroscopy in Orange and how to visually observe the effect of different preprocessing methods. Get Orange: https://orange.biolab.si/ See Spectroscopy add-on: https://github.com/Quasars/orange-spectroscopy License: GNU GPL + CC Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana In collaboration with: Soleil Synchrotron, Elettra Sincrotrone Trieste, BioSpec Norway and Canadian Light Source. Design: Agnieszka Rovšnik Music: THE HAPPY SONG by Nicolai Heidlas Music https://soundcloud.com/nicolai-heidlas Creative Commons — Attribution 3.0 Unported— CC BY 3.0 http://creativecommons.org/licenses/b... Music promoted by Audio Library https://youtu.be/cGuaRsXLScQ
Views: 1547 Orange Data Mining
Getting Started with Orange 14: Image Analytics - Clustering
 
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How to work with images in Orange, what are image embeddings and how do perform clustering with embedded data. For more information on image clustering, read the blog: [Image Analytics: Clustering] https://blog.biolab.si/2017/04/03/image-analytics-clustering/ License: GNU GPL + CC Music by: http://www.bensound.com/ Website: https://orange.biolab.si/ Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana
Views: 19771 Orange Data Mining
Data Preparation vs. Data Wrangling Comparison in Machine Learning / Deep Learning
 
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Data Preparation: Comparison of Programming Languages, Frameworks and Tools for Data Preprocessing and (Inline) Data Wrangling in Machine Learning / Deep Learning Projects. A key task to create appropriate analytic models in machine learning or deep learning is the integration and preparation of data sets from various sources like files, databases, big data storages, sensors or social networks. This step can take up to 80% of the whole project. This session compares different alternative techniques to prepare data, including extract-transform-load (ETL) batch processing (like Talend, Pentaho), streaming analytics ingestion (like Apache Storm, Flink, Apex, TIBCO StreamBase, IBM Streams, Software AG Apama), and data wrangling (DataWrangler, Trifacta) within visual analytics. Various options and their trade-offs are shown in live demos using different advanced analytics technologies and open source frameworks such as R, Python, Apache Hadoop, Spark, KNIME or RapidMiner. The session also discusses how this is related to visual analytics tools (like TIBCO Spotfire), and best practices for how the data scientist and business user should work together to build good analytic models. Key takeaways for the audience: - Learn various options for preparing data sets to build analytic models - Understand the pros and cons and the targeted persona for each option - See different technologies and open source frameworks for data preparation - Understand the relation to visual analytics and streaming analytics, and how these concepts are actually leveraged to build the analytic model after data preparation Slide Deck: http://www.slideshare.net/KaiWaehner/data-preparation-vs-inline-data-wrangling-in-data-science-and-machine-learning
Views: 2753 Kai Wähner
INTRODUCTION TO TEXT MINING IN HINDI
 
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find relevant notes at-https://viden.io/
Views: 9297 LearnEveryone
Introduction to Clustering Techniques | Mahout Clustering techniques | Mahout Clustering Tutorial
 
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Watch Sample Class Recording: http://www.edureka.co/mahout?utm_source=youtube&utm_medium=referral&utm_campaign=clustering-tech Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. Know More about various clustering techniques through this video. Following are the topics covered in the video: 1.Difference between various clustering techniques. 2. K- means Clustering 3.Fuzzy K- means Clustering 4.Fuzzy K- means Clustering MapReduce flow. 5.Various clustering algorithms. Related Blogs http://www.edureka.co/blog/introduction-to-clustering-in-mahout/?utm_source=youtube&utm_medium=referral&utm_campaign=clustering-tech http://www.edureka.co/blog/k-means-clustering/?utm_source=youtube&utm_medium=referral&utm_campaign=clustering-tech Edureka is a New Age e-learning platform that provides Instructor-Led Live, Online classes for learners who would prefer a hassle free and self paced learning environment, accessible from any part of the world. The topics related to ‘Clustering Techniques’ have extensively been covered in our course ‘Machine Learning with Mahout’. For more information, please write back to us at [email protected] Call us at US: 1800 275 9730 (toll free) or India: +91-8880862004
Views: 2546 edureka!
Single Cell Orange 02: Loading Single Cell Data from 10x Matrix
 
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In the video, we explain how to load your own data from the 10x Genomics matrix data file and how to merge data from different experiments. Download the data from: https://support.10xgenomics.com/single-cell-gene-expression/datasets, namely AML027 Post-transplant BMMCs and AML027 Pre-transplant BMMCs. Design: Agnieszka Rovšnik Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana
Views: 563 Orange Data Mining
Getting Started with Orange 18: Text Classification
 
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How to visualize logistic regression model, build classification workflow for text and predict tale type of unclassified tales. License: GNU GPL + CC Music by: http://www.bensound.com/ Website: https://orange.biolab.si/ Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana
Views: 18031 Orange Data Mining
Getting Started With Orange 05: Hierarchical Clustering
 
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Explanation of distance measurement between data points and a simple use of hierarchical clustering in the Orange workflow. License: GNU GPL + CC Music by: http://www.bensound.com/ Website: http://orange.biolab.si/ Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana
Views: 56608 Orange Data Mining
Getting Started with Orange 06: Making Predictions
 
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Making predictions with classification tree and logistic regression. Train data set: http://tinyurl.com/fruits-and-vegetables-train Test data set: http://tinyurl.com/test-fruits-and-vegetables License: GNU GPL + CC Music by: http://www.bensound.com/ Website: http://orange.biolab.si/ Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana
Views: 68349 Orange Data Mining
Big Data to Knowledge: Integrated Bioinformatics towards Systems Biology and Precision Medicine
 
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With the advent of next-generation sequencing (NGS) and other high-throughput omics technologies, systems integration is becoming the driving force for 21st century biology and medicine. To fully realize the value of such genome-scale data for knowledge discovery and disease understanding requires advanced bioinformatics for integration, mining, comparative analysis, and functional interpretation. We have developed a bioinformatics research infrastructure that integrates disparate databases and text mining tools in an ontological framework for automatic construction of knowledge networks and visual analysis of omics data. Our natural language processing (NLP) framework supports full-scale literature mining and generalizable relation extraction to connect gene/protein, mutation, miRNA to drug, disease and phenotype in personalized medicine context. This talk will highlight our collaborative projects with large-scale national initiatives, including the NIH LINCS-BD2K (Big Data to Knowledge) and TCGA/CPTAC cancer consortium projects to understand the impact of kinase inhibitor drugs on signaling pathways in cancer therapy. Dr. Cathy Wu is the Edward G. Jefferson Chair and Director of the Center for Bioinformatics & Computational Biology (CBCB) at University of Delaware. She has conducted bioinformatics research for 25 years and has led/co-led several large multi-institutional Consortium grants, including the Delaware INBRE. She directs the Protein Information Resource (PIR), a member of the UniProt Consortium with 5 million pageviews per month from 500,000 sites worldwide. She has published 250 peer-reviewed papers and is recognized as a “Highly Cited Researcher” (top 1%). The CBCB provides cutting-edge bioinformatics infrastructure, including Big Data and clinical genomics analytics capabilities for precision medicine.
Views: 201 DE-CTR ACCEL
Getting Started with Orange 20: Multivariate Projection - Freeviz
 
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How to visualize multiple variables in Orange and what how to interpret the Freeviz projection. For more information read the blogs on: [Visualizing Multiple Variables with Freeviz] https://blog.biolab.si/2018/01/26/visualizing-multiple-variables-freeviz/ [Scatter Plot Projection Rank] https://blog.biolab.si/2015/08/28/scatter-plot-projection-rank/ License: GNU GPL + CC Music by: http://www.bensound.com/ Website: http://orange.biolab.si/ Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana
Views: 8254 Orange Data Mining
Preprocessing Data using Orange Data Mining
 
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Orange is a component-based data mining and machine learning software suite, featuring a visual programming front-end for explorative data analysis and visualization, and Python bindings and libraries for scripting. It includes a set of components for data preprocessing, feature scoring and filtering, modeling, model evaluation, and exploration techniques. It is implemented in C++ and Python. Its graphical user interface builds upon the cross-platform Qt framework. Orange is distributed free under the GPL. It is maintained and developed at the Bioinformatics Laboratory of the Faculty of Computer and Information Science, University of Ljubljana, Slovenia.
Views: 13917 Andi Ariffin
Bootstrap Sampling
 
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An explanation of bootstrap sampling (i.e. sampling with replacement) using animations to illustrate. Bootstrap sampling is a primary component of the RandomForests® algorithm and is useful for running simulations. http://www.salford-systems.com
Views: 18501 Salford Systems
BioInformatics 2019   10th International Conferenc
 
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BioInformatics 2019 - 10th International Conference on BioInformatics Models, Methods and Algorithms (ins) AS At Prague Prague, Czechia For more information: https://www.eventbrite.com/e/bioinformatics-2019-10th-international-conference-on-bioinformatics-models-methods-and-algorithms-tickets-46369302833?aff=RMV DESCRIPTION ============== BIOINFORMATICS is part of BIOSTEC, the 12th International Joint Conference on Biomedical Engineering Systems and Technologies. Registration to BIOINFORMATICS allows free access to all other BIOSTEC conferences. BIOSTEC 2019 will be held in conjunction with MODELSWARD 2019 and ICISSP 2019. Registration to BIOSTEC allows free access to the MODELSWARD and ICISSP conferences (as a non-speaker). The purpose of the International Conference on Bioinformatics Models, Methods and Algorithms is to bring together researchers and practitioners interested in the design and application of modelling frameworks, algorithmic concepts, computational methods, and information technologies to address challenging problems in Bioinformatics and Biomedical research. There is a tremendous need to explore how mathematical, statistical and computational techniques can be used to better understand biological processes and systems, while developing new methodologies and tools to analyze the massive currently-available biological data. Areas of interest to this community include systems biology and biological networks (regulatory, neuronal, predator-prey, ecological ones, etc.), sequence analysis, biostatistics, graph models, image analysis, scientific data management and data mining, machine learning, pattern recognition, computational evolutionary biology, computational genomics and proteomics, and related areas. Conference Co-chairs ========================== Ana Fred, Instituto de Telecomunicações / IST, Portugal Hugo Gamboa, LIBPHYS-UNL / FCT - New University of Lisbon, Portugal PROGRAM CHAIR =============== Elisabetta De Maria, Université de Nice Sophia-Antipolis, France Keynote Speakers Hossam Haick, Israel Institute of Technology, Israel Andres Diaz Lantada, Universidad Politecnica de Madrid, Spain Henrique Martins, Universidade da Beira Interior, Portugal SCOPE =========== There is a tremendous need to explore how mathematical, statistical and computational techniques can be used to better understand biological processes and systems, while developing new methodologies and tools to analyze the massive currently-available biological data. Areas of interest to this community include systems biology and biological networks (regulatory, neuronal, predator-prey, ecological ones, etc.), sequence analysis, biostatistics, graph models, image analysis, scientific data management and data mining, machine learning, pattern recognition, computational evolutionary biology, computational genomics and proteomics, and related areas. CONFERENCE TOPICS ======================= Simulation and Modeling Immuno- and Chemo-informatics Pattern Recognition, Clustering and Classification Computational Intelligence Model Design and Evaluation Transcriptomics Next Generation Sequencing Genomics and Proteomics Sequence Analysis Structural Bioinformatics Structure Prediction Databases and Data Management Structural Variations Image Analysis Visualization Formal Verification of Biological Systems Computational Neuroscience Data mining and Machine Learning Biostatistics and Stochastic Models Pharmaceutical Applications Systems Biology Algorithms and Software Tools Web Services in Bioinformatics Computational Molecular Systems Please contact the event manager Marilyn below for the following: - Discounts for registering 5 or more participants. - If your company requires a price quotation. Event Manager Contact: marilyn.b.turner(at)nyeventslist.com You can also contact us if you require a visa invitation letter, after ticket purchase. We can also provide a certificate of completion for this event if required. ---------------------------------- For more information: https://www.eventbrite.com/e/bioinformatics-2019-10th-international-conference-on-bioinformatics-models-methods-and-algorithms-tickets-46369302833?aff=RMV Eventbrite: https://www.eventbrite.com/o/event-promotions-by-new-york-events-list-11118815675 Twitter: https://twitter.com/nyeventslist Facebook: https://www.facebook.com/NewYorkEventsList New York Events List: http://nyeventslist.com/
Discovering Cellular Mechanisms and Biomarkers in Anti-PD1 Non-Responders..
 
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Presented At: LabRoots - Drug Discovery Virtual Event 2019 Presented By: Devendra Mistry, PhD - Senior Field Application Scientist, QIAGEN Advanced Genomics Speaker Biography: Devendra (Dev) received his PhD from University of California San Diego(UCSD) Biomedical Sciences graduate program and did postdoctoral studies under both academic and pharmaceutical settings. During his post-doctoral studies, he focused on cellular mechanisms regulating stem cells and cancer stem cells through next-gen sequencing data generation, analysis, interpretation and mining. Dev joined QIAGEN as a field application scientist in 2015. In the past 3 years, he has provided trainings to many pharma, biotech, academic and government investigators for different QIAGEN bioinformatics software. In addition, he has helped many of these users troubleshoot problems with their existing workflows and design new workflows. Webinar: Discovering Cellular Mechanisms and Biomarkers in Anti-PD1 Non-Responders Using Ingenuity Pathway Analysis Software and OmicSoft OncoLand Datasets from QIAGEN Webinar Abstract: The last two decades have seen an explosion in the volume of oncology data generated using next-generation sequencing (NGS) and multi-omics techniques. As a result, there is a growing need for computational tools that are powerful enough to generate meaningful hypotheses about the biological mechanisms underlying cancer. In this presentation, we demonstrate how two of QIAGEN’s bioinformatics solutions – Ingenuity Pathway Analysis (IPA) software and the OmicSoft OncoLand database – can be combined to generate testable hypotheses in an immuno-oncology case study (GSE67501). This study sought to understand the mechanism underlying the failure of anti-PD-1 therapy in advanced renal cell carcinoma patients. The study data, along with tens of thousands of other public-domain datasets (including TCGA, SRA, GEO, GTEx and others), have been reprocessed using OmicSoft’s bioinformatics pipeline, and are readily available in OmicSoft’s OncoLand platform. Learning Objectives: 1. Introduction to databases backing Ingenuity Pathway Analysis and Oncoland 2. Studying potential biomarkers and targets through Oncoland’s data mining and comparison tools 3. Generation of peer-reviewed literature backed hypotheses through Ingenuity Pathway Analysis Sponsored By: QIAGEN Earn PACE/CME Credits: 1. Make sure you’re a registered member of LabRoots (https://www.labroots.com/virtual-event/drug-discovery-2019) 2. Watch the webinar on YouTube above or on the LabRoots Website (https://www.labroots.com/virtual-event/drug-discovery-2019) 3. Click Here to get your PACE (Expiration date – February 27, 2021 06:00 AM) – https://www.labroots.com/credit/pace-credits/3306/third-party LabRoots on Social: Facebook: https://www.facebook.com/LabRootsInc Twitter: https://twitter.com/LabRoots LinkedIn: https://www.linkedin.com/company/labroots Instagram: https://www.instagram.com/labrootsinc Pinterest: https://www.pinterest.com/labroots/ SnapChat: labroots_inc
Views: 30 LabRoots
Advanced Data Mining with Weka (2.2: Weka’s MOA package)
 
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Advanced Data Mining with Weka: online course from the University of Waikato Class 2 - Lesson 2: Weka’s MOA package http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/4vZhuc https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 3064 WekaMOOC
Single Cell Orange 03: Loading Single Cell Data from Tab-Delimited Files
 
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In the video, we explain how to load your own data from Excel and other tab-delimited files and how to use labels from different data sets. Download the first data from: https://www.sciencedirect.com/science/article/pii/S1534580718307834#app2 Download the second data from: https://portals.broadinstitute.org/single_cell/study/div-seq-single-nucleus-rna-seq-of-dividing-cells-in-the-adult-mouse-brain-and-spinal-cord#study-download, file name is Div-Seq.DG.Running_Average_logTPMs_normalized.txt.gz. Design: Agnieszka Rovšnik Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana
Views: 567 Orange Data Mining
Databases and Visualization Tools
 
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This is the second module in the 2016 Bioinformatics for Cancer Genomics workshop hosted by the Canadian Bioinformatics Workshops. This lecture is by Michelle Brazas from the Ontario Institute for Cancer Research and Florence Cavalli from the Hospital for Sick Children. How it Begins by Kevin MacLeod is licensed under a Creative Commons Attribution license (https://creativecommons.org/licenses/by/4.0/) Source: http://incompetech.com/music/royalty-free/index.html?isrc=USUAN1100200 Artist: http://incompetech.com/
Views: 1121 Bioinformatics DotCa
Getting Started with Orange 17: Text Clustering
 
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How to transform text into numerical representation (vectors) and how to find interesting groups of documents using hierarchical clustering. License: GNU GPL + CC Music by: http://www.bensound.com/ Website: https://orange.biolab.si/ Created by: Laboratory for Bioinformatics, Faculty of Computer and Information Science, University of Ljubljana
Views: 18104 Orange Data Mining

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