The document discusses algorithms for hierarchical clustering of large datasets. It introduces UPGMA clustering and its limitations when dealing with huge datasets. It then proposes two new algorithms called Sparse-UPGMA and Multi-Round MC-UPGMA to overcome these limitations. Multi-Round MC-UPGMA clusters the data in multiple rounds to deal with sparse inputs while requiring less memory. The algorithms are tested on clustering over 1.8 million protein sequences from UniRef90.
Clustal X help to the Bioinformatics candidate to predicts the Multiple Sequence Alignment and Phylogenetic Analysis for given a nuber of Gene Sequences of varrious organism,and find the evolutionary relationship.
1) Introduction to Trees.
2) Basic terminologies
3) Binary tree
4) Binary tree types
5) Binary tree representation
6) Binary search tree
7) Creation of a binary tree
8) Operations on binary search tree Trees
Clustal X help to the Bioinformatics candidate to predicts the Multiple Sequence Alignment and Phylogenetic Analysis for given a nuber of Gene Sequences of varrious organism,and find the evolutionary relationship.
1) Introduction to Trees.
2) Basic terminologies
3) Binary tree
4) Binary tree types
5) Binary tree representation
6) Binary search tree
7) Creation of a binary tree
8) Operations on binary search tree Trees
Presentation for blast algorithm bio-informaticezahid6
Presentation for BLAST algorithm
Publisher Md.Zahid Hasan
Bio-informatics blast is the use of computational tools for the process of acquisition, visualization, analysis and distribution of these datasets obtained by imaging modalities.
BTrees - Great alternative to Red Black, AVL and other BSTsAmrinder Arora
BTrees - designed by Rudolf Bayer and Ed McCreight - fundamental data structure in computer science. Great alternative to BSTs. Very appropriate for disk based access.
introduction to upgma software , its history and origination, basic mening of upgma, the upgma algorithm, steps to perform upgma, and its diagramatic representation of the process along with an example, its application, advantages along with the disadvantages, and its uses.
A review of two alignment-free methods for sequence comparison. In this presentation two alignment-free methods are studied:
- "Similarity analysis of DNA sequences based on LZ complexity and dynamic programming algorithm" by Guo et al.
- "Alignment-free comparison of genome sequences by a new numerical characterization" by Huang et al.
Splay Trees and Self Organizing Data StructuresAmrinder Arora
Self Organizing Data Structures, such as Splay Trees, continue to adjust themselves based on the operation sequence. They are much easier to implement compared to AVL or RB Trees. Amortized time is O(log n), although worst case time may be O(n).
Discussion of article " Bayes Estimators for Phylogenetic Reconstruction", presented by Leo Martins to the Phylogenomics Lab of the University of Vigo
Syst. Biol. 60(4), 528 540, 2011 doi 10.1093/sysbio/syr021
Presentation for blast algorithm bio-informaticezahid6
Presentation for BLAST algorithm
Publisher Md.Zahid Hasan
Bio-informatics blast is the use of computational tools for the process of acquisition, visualization, analysis and distribution of these datasets obtained by imaging modalities.
BTrees - Great alternative to Red Black, AVL and other BSTsAmrinder Arora
BTrees - designed by Rudolf Bayer and Ed McCreight - fundamental data structure in computer science. Great alternative to BSTs. Very appropriate for disk based access.
introduction to upgma software , its history and origination, basic mening of upgma, the upgma algorithm, steps to perform upgma, and its diagramatic representation of the process along with an example, its application, advantages along with the disadvantages, and its uses.
A review of two alignment-free methods for sequence comparison. In this presentation two alignment-free methods are studied:
- "Similarity analysis of DNA sequences based on LZ complexity and dynamic programming algorithm" by Guo et al.
- "Alignment-free comparison of genome sequences by a new numerical characterization" by Huang et al.
Splay Trees and Self Organizing Data StructuresAmrinder Arora
Self Organizing Data Structures, such as Splay Trees, continue to adjust themselves based on the operation sequence. They are much easier to implement compared to AVL or RB Trees. Amortized time is O(log n), although worst case time may be O(n).
Discussion of article " Bayes Estimators for Phylogenetic Reconstruction", presented by Leo Martins to the Phylogenomics Lab of the University of Vigo
Syst. Biol. 60(4), 528 540, 2011 doi 10.1093/sysbio/syr021
A journal club style presentation on a publication about the effect of microRNAs and pseudogenes on tumor gene regulation.
(Note: The animations in the slides do not work on SlideShare, please download the PowerPoint file to view.)
How to manage a case of acute exacerbation of COPD according to GOLD guidelines. Sincere thanks to Dr. Amardeep Toppo who has prepared most of this presentation.
This was powerpoint was requested by an attending physician to be shared with the Psychiatric providers regarding DVT prophylaxis in patients who may have been on the unit. They include recommendations as outlined by the ACCP 2012 Guidelines for prevention of venous thromboembolism
The Positive Effects of Fuzzy C-Means Clustering on Supervised Learning Class...Waqas Tariq
Selection of inputs is one of the most substantial components of classification algorithms for data mining and pattern recognition problems since even the best classifier will perform badly if the inputs are not selected very well. Big data and computational complexity are main cause of bad performance and low accuracy for classical classifiers. In other words, the complexity of classifier method is inversely proportional with its classification efficiency. For this purpose, two hybrid classifiers have been developed by using both type-1 and type-2 fuzzy c-means clustering with cascaded a classifier. In this proposed classifier, a large number of data points are reduced by using fuzzy c-means clustering before applied to a classifier algorithm as inputs. The aim of this study is to investigate the effect of fuzzy clustering on well-known and useful classifiers such as artificial neural networks (ANN) and support vector machines (SVM). Then the role of positive effects of these proposed algorithms were investigated on applied different data sets.
The Positive Effects of Fuzzy C-Means Clustering on Supervised Learning Class...CSCJournals
Selection of inputs is one of the most substantial components of classification algorithms for data mining and pattern recognition problems since even the best classifier will perform badly if the inputs are not selected very well. Big data and computational complexity are main cause of bad performance and low accuracy for classical classifiers. In other words, the complexity of classifier method is inversely proportional with its classification efficiency. For this purpose, two hybrid classifiers have been developed by using both type-1 and type-2 fuzzy c-means clustering with cascaded a classifier. In this proposed classifier, a large number of data points are reduced by using fuzzy c-means clustering before applied to a classifier algorithm as inputs. The aim of this study is to investigate the effect of fuzzy clustering on well-known and useful classifiers such as artificial neural networks (ANN) and support vector machines (SVM). Then the role of positive effects of these proposed algorithms were investigated on applied different data sets.
Clustering and Visualisation using R programmingNixon Mendez
Clustering Analysis is a collection of patterns into clusters based on similarity.
Here we will discuss on the following :
Microarray Data of Yeast Cell Cycle
Clustering Analysis :-
Principal Component Analysis (PCA)
Multidimensional Scaling (MDS)
K-Means
Self-Organizing Maps (SOM)
Hierarchical Clustering
Kernal based speaker specific feature extraction and its applications in iTau...TELKOMNIKA JOURNAL
Extraction and classification algorithms based on kernel nonlinear features are popular in the new direction of research in machine learning. This research paper considers their practical application in the iTaukei automatic speaker recognition system (ASR) for cross-language speech recognition. Second, nonlinear speaker-specific extraction methods such as kernel principal component analysis (KPCA), kernel independent component analysis (KICA), and kernel linear discriminant analysis (KLDA) are summarized. The conversion effects on subsequent classifications were tested in conjunction with Gaussian mixture modeling (GMM) learning algorithms; in most cases, computations were found to have a beneficial effect on classification performance. Additionally, the best results were achieved by the Kernel linear discriminant analysis (KLDA) algorithm. The performance of the ASR system is evaluated for clear speech to a wide range of speech quality using ATR Japanese C language corpus and self-recorded iTaukei corpus. The ASR efficiency of KLDA, KICA, and KLDA technique for 6 sec of ATR Japanese C language corpus 99.7%, 99.6%, and 99.1% and equal error rate (EER) are 1.95%, 2.31%, and 3.41% respectively. The EER improvement of the KLDA technique-based ASR system compared with KICA and KPCA is 4.25% and 8.51% respectively.
Motivation entails the development of a program that automatically performs clustering and outlier detection for a wide variety of numerically represented data.
Microgrids are the solution to the growing demand for energy in the recent times. It has the potential to improve local reliability, reduce cost and increase penetration rates for distributed renewable energy generation. Inclusion of Renewable Energy Systems(RES) which have become the topic of discussion in the recent times due to acute energy crisis, causes the power flow in the microgrid to be bi-directional in nature. The presence of the RES in the microgrid system causes the grid to be reconfigurable. This reconfiguration might also occur due to load or utility grid connection and disconnection. Thus conventional protection strategies are not applicable to micro-grids and is hence challenging for engineers to protect the grid in a fault condition. In this paper various Minimum Spanning Tree(MST) algorithms are applied in microgrids to identify the active nodes of the current topology of the network in a heuristic approach and thereby generating a tree from the given network so that minimum number of nodes have to be disconnected from the network during fault clearance. In the paper we have chosen the IEEE-39 and IEEE-69 bus networks as our sample test systems.
An Efficient Clustering Method for Aggregation on Data FragmentsIJMER
Clustering is an important step in the process of data analysis with applications to numerous fields. Clustering ensembles, has emerged as a powerful technique for combining different clustering results to obtain a quality cluster. Existing clustering aggregation algorithms are applied directly to large number of data points. The algorithms are inefficient if the number of data points is large. This project defines an efficient approach for clustering aggregation based on data fragments. In fragment-based approach, a data fragment is any subset of the data. To increase the efficiency of the proposed approach, the clustering aggregation can be performed directly on data fragments under comparison measure and normalized mutual information measures for clustering aggregation, enhanced clustering aggregation algorithms are described. To show the minimal computational complexity. (Agglomerative, Furthest, and Local Search); nevertheless, which increases the accuracy.
Integrative analysis of transcriptomics and proteomics data with ArrayMining ...Natalio Krasnogor
These slides are part of a presentation I gave on March 2010 at the BioInformatics and Genome Research Open Club at the Weizmann Institute of Science, Israel.
In these slides my student and I describe two web-applications for microarray and gene/protein set analysis,
ArrayMining.net and TopoGSA. These use ensemble and consensus methods as well as the
possibility of modular combinations of different analysis techniques for an integrative view of
(microarray-based) gene sets, interlinking transcriptomics with proteomics data sources. This integrative process uses tools from different fields, e.g. statistics, optimisation and network
topological studies. As an example for these integrative techniques, we use a microarray
consensus-clustering approach based on Simulated Annealing, which is part of the ArrayMining.net
Class Discovery Analysis module, and show how this approach can be combined in a modular
fashion with a prior gene set analysis. The results reveal that improved cluster validity indices can be obtained by merging the two methods, and provide pointers to distinct sub-classes within pre-defined tumour categories for a breast cancer dataset by the Nottingham Queens Medical Centre.
In the second part of the talk, I show how results from a supervised
microarray feature selection analysis on ArrayMining.net can be investigated in further detail with
TopoGSA, a new web-tool for network topological analysis of gene/protein sets mapped on a
comprehensive human protein-protein interaction network. I discuss results from a TopoGSA
analysis of the complete set of genes currently known to be mutated in cancer.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
1. Azhar Ali Shah @ Interdisciplinary Optimization and Decision Making Journal Club (IODMJC) IODMJC, March 20 , 2009
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3. Introduction: authors Azhar A Shah Efficient algorithms for accurate hierarchical clustering of huge datasets: tackling the entire protein space /31
4. Introduction: Hierarchical Clustering Azhar A Shah Efficient algorithms for accurate hierarchical clustering of huge datasets: tackling the entire protein space /31
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6. Introduction: about the topic Azhar A Shah Efficient algorithms for accurate hierarchical clustering of huge datasets: tackling the entire protein space /31 There is no guideline for selecting the best linkage method. In practice, people almost always use average linkage. UPGMA (Unweighted Pair Group Method using arithmetic Averages) Scalable to large datasets as it requires only (O(1)) edges in memory. BUT Highly susceptible to outliers!
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8. Introduction: UPGMA -Sparse input N=11 input singletons ( vertices ): {1,2,3,4,11,12,13,14,21,22,23} and 14 edges in the sparse input. The input is considered sparse since not all pairs are given e.g. there is no edge b/w 1 and 22. Clusters 1,2,3,4 form a clique A. Clusters 11,12,13,14 are missing edge < 11,14 > to form clique B. Clusters 21,22,23 are loosely connected to each other and to the cluster of clique A. In total there are two connected components in the input graph: ({1,2,3,4,21,22,23}) (producing 6 merges for 7 vertices) and {11,12,13,14} (producing 4 merges for 3 nodes), which therefore forms a forest of two disjoint trees , rather than the full tree of N-1=10 merges. UPGMA-input 90 23 1 70 23 22 50 22 21 30 14 13 20 14 12 12 13 12 11 13 11 1e+01 12 11 4e-10 4 3 1e-50 4 2 1e-80 3 2 2e-40 4 1 1e-40 3 1 1e-100 2 1 UPGMA-tree 32 99.167 31 26 31 85 29 23 30 50 28 14 29 50 22 21 28 11.5 27 13 27 10 12 11 26 1.33e-10 25 4 25 5e-41 24 3 24 1e-100 2 1
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10. Methodology: 1) Sparse-UPGMA Azhar A Shah Efficient algorithms for accurate hierarchical clustering of huge datasets: tackling the entire protein space /31 Can’t cope with huge datasets, where an O ( E ) memory requirement is intolerable (e.g. Table 1). UPGMA (mean): New eq: Time and memory improvement:
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14. Methodology: 2) Single-Round MC-UPGMA Azhar A Shah Efficient algorithms for accurate hierarchical clustering of huge datasets: tackling the entire protein space /31 Requires O(n) memory for holding forming tree!