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: 223379 Thales Sehn Körting
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. 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: 220390 Shomu's Biology
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. 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: 92647 Shomu's Biology
Please join us for the fifth course in the Bioinformatics Specialization! http://coursera.org/specializations/bioinformatics
Data Analysis for Genomics Data Analysis for Genomics will teach students how to harness the wealth of genomics data arising from new technologies, such as microarrays and next generation sequencing, in order to answer biological questions, both for basic cell biology and clinical applications. Register to Data Analysis for Genomics from HarvardX at http://www.edx.org/courses About this Course The purpose of this course is to enable students to analyze and interpret data generated by modern genomics technology, specifically microarray data and next generation sequencing data. We will focus on applications common in public health and biomedical research: measuring gene expression differences between populations, associated genomic variants to disease, measuring epigenetic marks such as DNA methylation, and transcription factor binding sites. The course covers the necessary statistical concepts needed to properly design experiments and analyze the high dimensional data produced by these technologies. These include estimation, hypothesis testing, multiple comparison corrections, modeling, linear models, principle component analysis, clustering, nonparametric and Bayesian techniques. Along the way, students will learn to analyze data using the R programming language and several packages from the Bioconductor project. Currently, biomedical research groups around the world are producing more data than they can handle. The training and skills acquired by taking this course will be of significant practical use for these groups. The learning that will take place in this course will allow for greater success in making biological discoveries and improving individual and population health.
Views: 6040 edX
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. 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: 160899 Shomu's Biology
Data Mining Advanced Bioinformatics Advanced Bio Informatics
Views: 57 Online Lectures In Hindi - Urdu
A brief tour of selected on-line tools to analyze TCGA data. The primary portal for these tools is found at Analytical Tools for The Cancer Genome Atlas https://tcga-data.nci.nih.gov/tcga/tcgaAnalyticalTools.jsp Examples are presented for tools at cBioPortal for Cancer Genomics; Integrative Genomics Viewer; Broad GDAC Firehose; MD Anderson GDAC MBatch; as well as tools still in development.
Views: 6701 nmsuaces
Presenter: Tunca Dogan KanSiL, Department of Health Informatics, Graduate School of Informatics, ODTU European Molecular Biology Laboratory, European Bioinformatics Institute * This version doesn't have the annotations made by the presenter. To watch the original version, you can register for free and watch it here: https://www.bigmarker.com/bioinfonet/TuncaDogan Abstract: Machine learning and data mining techniques are frequently employed to make sense of large-scale and noisy biological/biomedical data accumulated in public servers. A key subject in this endeavour is the prediction of the properties of proteins such as their functions and interactions. Recently, deep learning (DL) based methods have outperformed the conventional machine learning algorithms in the fields of computer vision, natural language processing and artificial intelligence; which brought attention to their application to the biological data. In this talk, I'm going to explain the DL-based probabilistic computational methods we have recently developed in our research center (KanSiL, Graduate School of Informatics, ODTU); first, to predict the functions of the uncharacterised proteins (i.e., DEEPred); and second, to identify novel interacting drug candidate molecules for all potential targets in the human proteome (i.e., DEEPscreen) to serve the purposes of drug discovery and repositioning, together with the aim of biomedical data integration. Apart from the benefits of employing novel DL approaches, I'll also mention the limitations of DL-based techniques when applied on the biological data, to explain why deep learning alone cannot solve every problem related to bioinformatics.
Views: 184 RSG-Turkey
I highlight the top 4 best laptops for data analysts looking to enter the data science field. Data analytics is a hot topic, what do you need to become a successful data analysts? ►►► Full List of Laptops Below - Don't Miss It! ► You May Also Like - Data Analyst Job Description ( http://bit.ly/2DySEjP ) ► Or Check out the Full Playlist for Data Analysts ( http://bit.ly/2mB4G0N ) Top 4 Best Laptops for Data Analysts ► Dell XPS 15 ( http://amzn.to/2CWz8cr ) ► Razer Blade Stealth ( http://amzn.to/2EHGZ2M ) ► Macbook Pro 15 ( http://amzn.to/2sFIEQu ) ► MSI GS63VR Stealth Pro ( http://amzn.to/2FxJLnI ) In a world of technology and options what is the right choice when choosing a computer for your new career in the data science industry. I hope this video helps you decide how to pick the best pc (personal computer) for your work as a data analyst. I receive a lot of questions on what are the best tools and resources. Here are some of the top questions: - Mac vs PC for Data analysts? - Mac vs windows for Data analysts? - What is the best laptop for data analysts? - What are the top 4 laptops for data analysts? - What is the best laptop for Data Science? - How to choose the right laptop for data analytics? - Best Laptops for Data Analytics 2018 Here at Jobs in the Future we have spent a great deal of time researching and informing you about the Data Science industry, and we will continue to do so with practical, informative content that doesn't weigh you down with all the jargon. Today I want to touch on what gear you need to become a well outfitted Data Analyst. 1 ) Dell XPS 15 (Top Recommendation) Talk about Fast! Check out the specs on this beast. full 32GB of RAM, 1TB SSD, and a NVIDIA GeForce GTX 1050 - 15.6-inch 4K Ultra HD (3840 x 2160) InfinityEdge touch display - 32 GB DDR4-2400MHz, No Optical Drive - 1TB PCIe Solid State Drive - 7th Generation Intel Core i7-7700HQ Quad Core Processor (6M cache, up to 3.8 GHz), NVIDIA GeForce GTX 1050 4GB DDR5 Graphics 2 ) Razer Blade Stealth (not for Data Science) Still very fast, but do be warned. You cannot swap out the 16GB RAM. It is soldered to the mother board. Major kill-joy. - 4K Display (3840x2160) - 12.5” IGZO 16:9 Touchscreen - 512GB ultra-fast PCIe SSD - 7th Gen Intel Core i7-7500U Dual-Core Processor with Hyper - Threading 2.7GHz / 3.5GHz (Base/Turbo) - 16GB dual-channel onboard memory (LPDDR3-1866MHz) - Thunderbolt 3 (USB-C) - Intel HD Graphics 620 (Makes this not the best Data Science computer, but still a great choice for data analyst) 3 ) Macbook Pro 15 (For the Apple Gurus) I know, you were getting worried. You thought I was going to leave out the trusty go to, the Macbook Pro. Well here it is! But be warned. Just like the Razer, the Macbook Pro has its 16GB RAM Soldered to the motherboard. This is why I placed the Dell XPS 15 and the MSI GS63VR on this list of machines. - 2.9GHz quad-core 7th-generation Intel Core i7 processor - Turbo Boost up to 3.9GHz - 16GB 2133MHz LPDDR3 memory - 512GB SSD storage - Radeon Pro 560 with 4GB memory (GPU) - Four Thunderbolt 3 ports 4 ) MSI GS63VR Stealth Pro (The Top Performer) This computer is simply beyond reason. Pack with power. Ready to handle whatever data job you can throw at it. - Display: 15.6" Full HD - Anti-Glare Wide View Angle 1920x1080 - Processor: Intel Core i7-7700HQ (2.8-3.8GHz) - Graphics Card: NVIDIA's GTX 1070 8G GDDR5 Max Q - RAM: 32GB (16GB x2) DDR4 2400MHz - Hard Drive: 512GB SSD + 1TB (SATA) 5400rpm ------- SOCIAL Twitter ► @jobsinthefuture Facebook ►/jobsinthefuture Instagram ►@Jobsinthefuture WHERE I LEARN: (affiliate links) Lynda.com ► http://bit.ly/2rQB2u4 Udemy ► http://fxo.co/52oG Envato ► http://bit.ly/2CSoROx edX.org ► http://fxo.co/4y00 MY FAVORITE GEAR: (affiliate links) Camera ► http://amzn.to/2BWvE9o CamStand ► http://amzn.to/2BWsv9M Compute ► http://amzn.to/2zPeLvs Mouse ► http://amzn.to/2C0T9hq TubeBuddy ► https://www.tubebuddy.com/bengkaiser Host Gator ► http://bit.ly/2CBonPO ( Get 60% off Website Hosting with the link ) ► Download the Ultimate Guide Now! ( https://www.getdrip.com/forms/883303253/submissions/new ) Thanks for Supporting Our Channel! DISCLAIMER: This video and description contains affiliate links, which means that if you click on one of the product links, I’ll receive a small commission. This help support the channel and allows us to continue to make videos like this. Thank you for the support! Disclaimer - Links in this description are affiliate links. Which means I receive a small commission when you purchase a product at NO extra cost to you.
Views: 26988 Ben G Kaiser
Full lecture: http://bit.ly/K-means The K-means algorithm starts by placing K points (centroids) at random locations in space. We then perform the following steps iteratively: (1) for each instance, we assign it to a cluster with the nearest centroid, and (2) we move each centroid to the mean of the instances assigned to it. The algorithm continues until no instances change cluster membership.
Views: 493690 Victor Lavrenko
Presentation based on Zaremba et al, Text-mining of PubMed abstracts by natural language processing to create a public knowledge base on molecular mechanisms of bacterial enteropathogens. BMC Bioinformatics 2009 10:177 http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-10-177
Views: 804 Jeff Shaul
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: 3586 WekaMOOC
Views: 19 Ali Soofastaei
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: 58599 Orange Data Mining
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: 208553 Last moment tuitions
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: 792 Orange Data Mining
3DMax is a tool that can be used to construct the 3D structure of a chromosome from Hi-C data. 3DMax employs an efficient maximum likelihood algorithm to construct the 3D structures for a chromosome. The Java and MATLAB versions of this tool was created. Video Created by: Oluwatosin Oluwadare Dr. Jianlin Cheng's Bioinformatics, Data Mining, and Machine Learning Lab Computer Science Department University of Missouri, Columbia, MO 65211-2060
I highlight the top 4 best affordable laptops for data analysts looking to enter the data analytics field. Data analytics is a hot topic, what do you need to become a successful data analysts? ►►► Full List of Laptops Below - Don't Miss It! ► Or Check out the Full Playlist for Data Analysts ( http://bit.ly/2mB4G0N ) The Two Basic Models ► Dell Inspiron i7559 5012 ( http://amzn.to/2G1lwBz ) ► MSI GL72M ( http://amzn.to/2tWAYu2 ) Top 4 Best Affordable Laptops for Data Analysts ► Dell Inspiron 7000 Flagship ( http://amzn.to/2DB5l8C ) ► Lenovo Legion Y520 ( http://amzn.to/2GB35S5 ) ► Acer Predator Helios 300 ( http://amzn.to/2FHEkXl ) ► MSI GL62MVR ( http://amzn.to/2HHWWTF ) In a world of technology and options what is the right choice when choosing a laptop for your new career in the data analytics industry. I hope this video helps you decide how to pick the best pc (personal computer) for your work as a data analyst. I receive a lot of questions on what are the best tools and resources. Here are some of the top questions: - Affordable Laptops for Data Analytics? - Budget Laptops for Data Analytics - What is the best budget laptop for data analysts? - What are the top 4 affordable laptops for data analysts? - What is the best cheap laptop for Data analytics? - How to choose a budget laptop for data analytics? - Best budget Laptops for Data Analytics 2018 Here at Jobs in the Future we have spent a great deal of time researching and informing you about the Data analytics industry, and we will continue to do so with practical, informative content that doesn't weigh you down with all the jargon. Today I want to touch on what gear you need to become a well outfitted Data Analyst. 1 ) Dell Inspiron 7000 Flagship This computer edged its way into the top 4 best affordable computers because it comes standard with 16GB of RAM, 12GB of Solid State Hard Drive + 1TB of SATA Hard Drive, but it slides in as the entry level to the top four best because of the Nvidia GTX 960 Graphics card. 16GB DDR3 RAM - Full HD 1920 x 1080 - 128 GB SSD + 1 TB HDD; 15.6" - NVIDIA GeForce GTX 960M 4 GB GDDR5 - Intel Core i7-6700HQ Quad-Core processor, 2.6GHz can boost up to 3.5GHz 2 ) Lenovo Legion Y520 Now we are starting to develop some real power in this line up. The Legion comes with all that you need to dive in at an affordable price. RAM, 2GB of SATA Hard Drive, and the GTX 1050i. This computer is really starting to give us some hope on the affordable line up. - 15.6" FHD Anti-Glare LED Backlight - Intel Core i7-7700HQ Processor 2.8GHz (boosts up to 3.80GHz) - NVIDIA GeForce GTX 1050Ti 4GB GDDR5 - 16GB DDR4 - 256GB SSD + 2TB 3 ) Acer Predator Helios 300 The Acer Predator is a well suited computer. Lots of power and with an excellent design. The only issue I have with this computer is the small Solid State Hard Drive that is possess. I would highly recommend upgrading to a larger 512GB or 1TB Drive if you're going to pick up this computer. Click here for Samsung! They make a great drive, or go portable external. - 15.6" Full HD (1920 x 1080) widescreen IPS display - 7th Generation Intel Core i7-7700HQ Processor (Up to 3.8GHz) - NVIDIA GeForce GTX 1060 with 6 GB of dedicated GDDR5 VRAM - 16GB DDR4 Memory - 256GB SSD 4 ) MSI GL62MVR The MSI GL62 is stacked with options for the price. It has all the tops specs to get you going on a budget into the data industry. MSI held back no punches getting this computer maxed out for the price. Check the specs below. - Display: 15.6" Full HD - Processor: Core i7-7700HQ 2.8 (Boosts up to 3.8 GHz) - NVIDIA GeForce GTX 1060 6G GDDR5 - RAM: 16GB (8G*2) DDR4 2400MHz (Upgrade-able to a max memory of 32GB - 128GB NVMe SSD + 1TB HDD (5400RPM) ------- SOCIAL Twitter ► @jobsinthefuture Facebook ►/jobsinthefuture Instagram ►@Jobsinthefuture WHERE I LEARN: (affiliate links) Lynda.com ► http://bit.ly/2rQB2u4 Udemy ► http://fxo.co/52oG Envato ► http://bit.ly/2CSoROx edX.org ► http://fxo.co/4y00 MY FAVORITE GEAR: (affiliate links) Camera ► http://amzn.to/2BWvE9o CamStand ► http://amzn.to/2BWsv9M Compute ► http://amzn.to/2zPeLvs Mouse ► http://amzn.to/2C0T9hq TubeBuddy ► https://www.tubebuddy.com/bengkaiser Host Gator ► http://bit.ly/2CBonPO ( Get 60% off Website Hosting with the link ) ► Download the Ultimate Guide Now! ( https://www.getdrip.com/forms/883303253/submissions/new ) Thanks for Supporting Our Channel! DISCLAIMER: This video and description contains affiliate links, which means that if you click on one of the product links, I’ll receive a small commission. This help support the channel and allows us to continue to make videos like this. Thank you for the support!
Views: 4649 Ben G Kaiser
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: 111 Microsoft Research
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: 2880 WekaMOOC
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: 2538 Kai Wähner
Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://goo.gl/to1yMH or Fill the form we will contact you https://goo.gl/forms/2SO5NAhqFnjOiWvi2 if you have any query email us at [email protected] or [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 199443 Last moment tuitions
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, bioinformatics, data compression, and computer graphics. Data Science Certification Training - R Programming: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=Clustering-Data-Science-a3It88zzbiA&utm_medium=SC&utm_source=youtube #datascience #datasciencetutorial #datascienceforbeginners #datasciencewithr #datasciencetutorialforbeginners #datasciencecourse What are the course objectives? This course will enable you to: 1. Gain a foundational understanding of business analytics 2. Install R, R-studio, and workspace setup. You will also learn about the various R packages 3. Master the R programming and understand how various statements are executed in R 4. Gain an in-depth understanding of data structure used in R and learn to import/export data in R 5. Define, understand and use the various apply functions and DPLYP functions 6. Understand and use the various graphics in R for data visualization 7. Gain a basic understanding of the various statistical concepts 8. Understand and use hypothesis testing method to drive business decisions 9. Understand and use linear, non-linear regression models, and classification techniques for data analysis 10. Learn and use the various association rules and Apriori algorithm 11. Learn and use clustering methods including K-means, DBSCAN, and hierarchical clustering Who should take this course? There is an increasing demand for skilled data scientists across all industries which makes this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals: IT professionals looking for a career switch into data science and analytics Software developers looking for a career switch into data science and analytics Professionals working in data and business analytics Graduates looking to build a career in analytics and data science Anyone with a genuine interest in the data science field Experienced professionals who would like to harness data science in their fields Who should take this course? There is an increasing demand for skilled data scientists across all industries which makes this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals: 1. IT professionals looking for a career switch into data science and analytics 2. Software developers looking for a career switch into data science and analytics 3. Professionals working in data and business analytics 4. Graduates looking to build a career in analytics and data science 5. Anyone with a genuine interest in the data science field 6. Experienced professionals who would like to harness data science in their fields For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 4548 Simplilearn
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: 15898 Orange Data Mining
Advanced Data Mining with Weka: online course from the University of Waikato Class 1 - Lesson 4: Looking at forecasts http://weka.waikato.ac.nz/ Slides (PDF): https://goo.gl/JyCK84 https://twitter.com/WekaMOOC http://wekamooc.blogspot.co.nz/ Department of Computer Science University of Waikato New Zealand http://cs.waikato.ac.nz/
Views: 4942 WekaMOOC
For more information, log on to- http://shomusbiology.weebly.com/ Download the study materials here- http://shomusbiology.weebly.com/bio-materials.html FASTA is a DNA and protein sequence alignment software package first described (as FASTP) by David J. Lipman and William R. Pearson in 1985. Its legacy is the FASTA format which is now ubiquitous in bioinformatics. FASTA takes a given nucleotide or amino-acid sequence and searches a corresponding sequence database by using local sequence alignment to find matches of similar database sequences. The FASTA program follows a largely heuristic method which contributes to the high speed of its execution. It initially observes the pattern of word hits, word-to-word matches of a given length, and marks potential matches before performing a more time-consuming optimized search using a Smith-Waterman type of algorithm. The size taken for a word, given by the parameter ktup, controls the sensitivity and speed of the program. Increasing the ktup value decreases number of background hits that are found. From the word hits that are returned the program looks for segments that contain a cluster of nearby hits. It then investigates these segments for a possible match. There are some differences between fastn and fastp relating to the type of sequences used but both use four steps and calculate three scores to describe and format the sequence similarity results. These are: Identify regions of highest density in each sequence comparison. Taking a ktup to equal 1 or 2. In this step all or a group of the identities between two sequences are found using a look up table. The ktup value determines how many consecutive identities are required for a match to be declared. Thus the lesser the ktup value: the more sensitive the search. ktup=2 is frequently taken by users for protein sequences and ktup=4 or 6 for nucleotide sequences. Short oligonucleotides are usually run with ktup = 1. The program then finds all similar local regions, represented as diagonals of a certain length in a dot plot, between the two sequences by counting ktup matches and penalizing for intervening mismatches. This way, local regions of highest density matches in a diagonal are isolated from background hits. For protein sequences BLOSUM50 values are used for scoring ktup matches. This ensures that groups of identities with high similarity scores contribute more to the local diagonal score than to identities with low similarity scores. Nucleotide sequences use the identity matrix for the same purpose. The best 10 local regions selected from all the diagonals put together are then saved. Rescan the regions taken using the scoring matrices. trimming the ends of the region to include only those contributing to the highest score. Rescan the 10 regions taken. This time use the relevant scoring matrix while rescoring to allow runs of identities shorter than the ktup value. Also while rescoring conservative replacements that contribute to the similarity score are taken. Though protein sequences use the BLOSUM50 matrix, scoring matrices based on the minimum number of base changes required for a specific replacement, on identities alone, or on an alternative measure of similarity such as PAM, can also be used with the program. For each of the diagonal regions rescanned this way, a subregion with the maximum score is identified. The initial scores found in step1 are used to rank the library sequences. The highest score is referred to as init1 score. In an alignment if several initial regions with scores greater than a CUTOFF value are found, check whether the trimmed initial regions can be joined to form an approximate alignment with gaps. Calculate a similarity score that is the sum of the joined regions penalising for each gap 20 points. This initial similarity score (initn) is used to rank the library sequences. The score of the single best initial region found in step 2 is reported (init1). Here the program calculates an optimal alignment of initial regions as a combination of compatible regions with maximal score. This optimal alignment of initial regions can be rapidly calculated using a dynamic programming algorithm. The resulting score initn is used to rank the library sequences.This joining process increases sensitivity but decreases selectivity. A carefully calculated cut-off value is thus used to control where this step is implemented, a value that is approximately one standard deviation above the average score expected from unrelated sequences in the library. A 200-residue query sequence with ktup2 uses a value 28. Source of the article published in description is Wikipedia. I am sharing their material. Copyright by original content developers. Link- http://en.wikipedia.org/wiki/Main_Page
Views: 59381 Shomu's Biology
A brief introduction to epigenetics and DNA methylation, followed by a detailed description of how to determine if specific genes are differentially methylated in biospecimen samples. Instructions are provided to learn how to download large datasets from the TCGA web portal.
Views: 2648 nmsuaces
Explanation of k-means clustering, and silhouette score and the use of k-means on a real data in Orange. For more information read the blogs on: [Learning with Paint Data] http://blog.biolab.si/2015/07/10/learn-with-paint-data/ [Interactive k-Means] http://blog.biolab.si/2016/08/12/interactive-k-means/ [Silhouette Scoring] http://blog.biolab.si/2016/03/23/all-i-see-is-silhouette/ 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: 23240 Orange Data Mining
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: 61635 Orange Data Mining
This session took place in February 2016. Part 2 of 7 - Speaker: John Overington, Director Bioinformatics, Stratified Medical Data mining, machine learning and artificial intelligence are becoming the most talk-about topics in digital health. With vast volumes of medical data available, exploiting these techniques to derive valuable insights may both challenge and reshape certain elements of our healthcare system. These new approaches are leading to redefining drug discovery, assisting and automating diagnoses and helping predict and prevent diseases using health record data – or even our digital footprint. Artificially intelligent algorithms will permeate both the lives of the doctor and patient. But there remains much hype, confusion and misunderstanding in the field. What is possible and what are the limitations? How will medicine adapt and what will be the impact on patient’s health and autonomy?
Views: 628 Imperial College Business School
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.
This video describes the fundamental principles of the hash table data structure which allows for very fast insertion and retrieval of data. It covers commonly used hash algorithms for numeric and alphanumeric keys and summarises the objectives of a good hash function. Collision resolution is described, including open addressing techniques such as linear and quadratic probing, and closed addressing techniques such as chaining with a linked list.
Views: 153574 Kevin Drumm
A confused archivist has misplaced Monet's masterpiece in the archive. We will use Orange to find the missing painting based on the nearest neighbors approach. In the video, we explain how to use Painters embedder and how to find nearest neighbor(s) to a given reference sample. Data set download: http://file.biolab.si/images/Paintings.zip Prototypes add-on: https://github.com/biolab/orange3-prototypes About Painters embedder: http://blog.kaggle.com/2016/11/17/painter-by-numbers-competition-1st-place-winners-interview-nejc-ilenic/ 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: 1521 Orange Data Mining
Read our blog post analysing the European Commission's (EC) text and data mining (TDM) exception and providing recommendations on how to improve it: http://bit.ly/2cE60sp Copy (short for Copyright) explains what text and data mining (TDM) is all about, and what hurdles researchers are currently facing. We also have a blog post on the TDM bits in the EC's Impact Assessment accompanying the proposal: http://bit.ly/2du9sYe Read more about the EC's copyright reform proposals in general: http://bit.ly/2cvAh0a
Views: 3265 FixCopyright
Jee-Hyub Kim and Senay Kafkas from the Literature Services team at EMBL-EBI present this talk on an introduction to text mining and its applications in service provision. The 1st part of this talk focuses on what text mining is and some of the methods and available tools. The 2nd part looks at how to find articles on Europe PMC - a free literature resource for biomedical and health researchers - and how to build your own text mining pipeline (starts at 20:30 mins). The final part gives a nice case study showing how Europe PMC's pipeline was integrated into a new drug target validation platform called Open Targets (previously CTTV) (starts at 38:20 mins). This video is best viewed in full screen mode using Google Chrome.
Views: 3115 European Bioinformatics Institute - EMBL-EBI
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: 13398 Andi Ariffin
Video of Ben Keller's presentation from our Genomics Data and Bioinformatics Meet-up: http://www.meetup.com/seattlesigkdd/events/222388885/ Example Code: https://gist.github.com/bjkeller/87d0b334463bec9f8fb5
Views: 131 DataClub Seattle SIGKDD
Presented At: Cancer Research & Oncology 2018 Presented By: Devendra Mistry, PhD - Senior Field Application Scientist, Bioinformatics, QIAGEN 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 Markers in Anti-PD1 Non-Responders Through Ingenuity Pathway Analysis and Oncoland Webinar Abstract: In the last two decades, large amount of next-generation sequencing (NGS) and -omics data has been generated in the field of immuno-oncology. Generating hypotheses by analyzing hundreds if not thousands of differentially expressed genes from expression studies and mining information from large amount of publicly available NGS and -omics data can be a very daunting task. QIAGEN’s Oncoland/Arraystudio (from Omicsoft) and Ingenuity Pathway Analysis (IPA) software provide with a set of tools and functionalities to do analysis and interpretation of NGS data to generate meaningful hypotheses and the ability to mine and compare information across a very large number of datasets curated from publicly available from sources such as GEO, SRA, TCGA, GTEX and others. In this webinar, we use gene expression data from a clinical study (GSE67501) focused on understanding the mechanism underlying anti-PD-1 therapy failure in advanced renal cell carcinoma patients. Using this data and the data curated from TCGA and other sources, it will be demonstrated how Arraystudio and Ingenuity Pathway Analysis can be used to generate hypotheses for mechanism of action and to discover potential targets and biomarkers. 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 Earn PACE/CME Credits: 1. Make sure you’re a registered member of LabRoots (https://www.labroots.com/virtual-event/cancer-research-oncology-2018) 2. Watch the webinar on YouTube above or on the LabRoots Website (https://www.labroots.com/virtual-event/cancer-research-oncology-2018) 3. Click Here to get your PACE (Expiration date – October 11, 2020 09:00 AM)– https://www.labroots.com/credit/pace-credits/3096/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: 45 LabRoots
Slides for this lecture can be found here: https://drive.google.com/open?id=1ZVLSxAHPF80jHMO1W5Pjt6KmZRfy2__J This is the eighth lecture of eight for the B.Sc. Honours bioinformatics module at Stellenbosch University Faculty of Medicine and Health Sciences, Department of Biomedical Sciences. The lecture covers topics in biological pathways and networks. We started with a quick assessment of the experimental sources that lead to our understanding of gene-gene, protein-protein, and gene-protein relationships. We contrasted approaches that enumerate a set of genes as associated with each activity (such as the genes in a given GO category) with those that infer collections of tightly connected components of genes in point-to-point networks. Using collective descriptions of genes improves our sensitivity for changes, our robustness against missing real differences, and improves the interpretability of our findings. We particularly examined Gene Ontology (GO), explaining that it marries a controlled vocabulary with defined relationships to create categories that have a hierarchical relationship described by annotated evidence. We looked at KEGG for another categorical grouping of genes, noting that the reactions leading from metabolite to metabolite were frequently marked by Enzyme Commission numbers. We used the WebGestalt interface for over-representation analysis: which pathways explain disproportionate numbers of our genes of interest? We discussed the use of the hypergeoemtric distribution, a la Fisher Exact Test, for computing the probability of a particular overlap between lists. We also ventured into Gene Set Enrichment Analysis as a way to find the pathways of greatest representation in a list of genes ordered by fold change / p-value. We spent a bit of time defining the graph theory terms that we frequently borrow for biological networks, and we defined the scale-free, small-world, and hierarchical modular properties of biological networks. We mentioned several tools for network visualization but emphasized the powerful Cytoscape environment and NetGestalt framework for both visualization and integration of OMICs data.
Views: 68 David Tabb
Creating a data analysis workflow in Orange data mining software. 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: 84347 Orange Data Mining