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Sequence analysis of nucleic acids and proteins: part 2

Sequence analysis of nucleic acids and proteins: part 2

Sequence analysis of nucleic acids and proteins: part 2. Prediction of structure and function. Based on Chapter 3 of Post-genome bioinformatics by Minoru Kanehisa Oxford University Press, 2000. Search and learning problems in sequence analysis. Thermodynamic principle.

By geri
(159 views)

Introduction to Neural Networks

Introduction to Neural Networks

Introduction to Neural Networks. Neural Networks in the Brain. Human brain “computes” in an entirely different way from conventional digital computers. The brain is highly complex, nonlinear, and parallel.

By afric
(142 views)

Classification / Regression Neural Networks 2

Classification / Regression Neural Networks 2

Classification / Regression Neural Networks 2. Neural networks. Topics Perceptrons structure training expressiveness Multilayer networks possible structures activation functions training with gradient descent and backpropagation expressiveness. Neural network application.

By liz
(79 views)

Chapter 5 Unsupervised learning

Chapter 5 Unsupervised learning

Chapter 5 Unsupervised learning. Introduction. Unsupervised learning Training samples contain only input patterns No desired output is given (teacher-less) Learn to form classes/clusters of sample patterns according to similarities among them

By lonna
(363 views)

Neural Networks

Neural Networks

Neural Networks. A neural network is a network of simulated neurons that can be used to recognize instances of patterns. NNs learn by searching through a space of network weights http://www.cs.unr.edu/~sushil/class/ai/classnotes/glickman/1.pgm.txt.

By yoland
(173 views)

Protein Secondary Structure Prediction

Protein Secondary Structure Prediction

Protein Secondary Structure Prediction. G P S Raghava. Protein Structure Prediction. Importance What is secondary structure Assignment of secondary structure (SS) Type of SS prediction methods Description of various methods Role of multiple sequence alignment/profiles How to use.

By paul
(280 views)

Lectures 7&8: Non-linear Classification and Regression using Layered Perceptrons

Lectures 7&8: Non-linear Classification and Regression using Layered Perceptrons

. m ( x , q ) = 0. +. +. . +. +. +. x 2. +. +. . . +. +. +. . +. . +. +. . +. +. +. x 1. Lectures 7&8: Non-linear Classification and Regression using Layered Perceptrons. Dr Martin Brown Room: E1k Email: martin.brown@manchester.ac.uk Telephone: 0161 306 4672

By mya
(262 views)

Simple Cyclic Movements as a Distinct Autism Feature: Computational Approach

Simple Cyclic Movements as a Distinct Autism Feature: Computational Approach

Department of Informatics, Nicolaus Copernicus University, Toruń. Institute of Computer Science, Maria Curie- Skłodowska University, Lublin. Simple Cyclic Movements as a Distinct Autism Feature: Computational Approach. Krzysztof Dobosz Dariusz Mikołajewski Grzegorz M. Wójcik

By montana
(120 views)

Dimension reduction (2)

Dimension reduction (2)

Dimension reduction (2). Projection pursuit ICA NCA Partial Least Squares. Blais . “The role of the en v ironment in synaptic plasticity…..” (1998) Liao et al. PNAS. (2003) http://www.cis.hut.fi/aapo/papers/NCS99web/node11.html Barker & Raynes . J. Chemometrics 2013.

By bryce
(104 views)

Punctuation Generation Inspired Linguistic Features For Mandarin Prosodic Boundary Prediction

Punctuation Generation Inspired Linguistic Features For Mandarin Prosodic Boundary Prediction

Punctuation Generation Inspired Linguistic Features For Mandarin Prosodic Boundary Prediction. Chen- yu chiang , yih-ru wang and sin- horng chen 2012 icassp Reporter: Huang- wei chen. Introduction(1/2).

By illias
(142 views)

Artificial Neural Network (Back-Propagation Neural Network)

Artificial Neural Network (Back-Propagation Neural Network)

Artificial Neural Network (Back-Propagation Neural Network). Yusuf Hendrawan , STP., M.App.Life Sc., Ph.D. http://research.yale.edu/ysm/images/78.2/articles-neural-neuron.jpg. http://faculty.washington.edu/chudler/color/pic1an.gif. Neurons. Biologica l. Artificial. A typical AI agent.

By uta
(505 views)

Neural Networks

Neural Networks

Neural Networks. Dr. Thompson March 19, 2013. Artificial Intelligence. Robotics Computer Vision & Speech Recognition Expert Systems Pattern Recognition Machine Learning Natural Language Processing Prognostics & Diagnostics. Neural Network Applications. Character Recognition

By clea
(162 views)

Outline

Outline

Artificiel Neural Networks 2 Morten Nielsen Department of Systems Biology , DTU IIB-INTECH, UNSAM, Argentina. Outline. Optimization procedures Gradient decent (this you already know) Network training back propagation cross-validation Over-fitting examples.

By amal
(141 views)

Notes on Backpropagation

Notes on Backpropagation

Notes on Backpropagation. Alex Churchill. Feed Forward. Node C = sigmoid(A * weight ca + B * weight cb ). C. D. Feed Forward. Node C = sigmoid(0.1 * 0.1+ 0.7*0.5). C. D. Feed Forward. Node C = sigmoid(0.01+0.35) = 0.59. C. 0.59. D. Feed Forward.

By kinsey
(85 views)

Artificial Neural Networks: An Alternative Approach to Risk – Based Design

Artificial Neural Networks: An Alternative Approach to Risk – Based Design

Artificial Neural Networks: An Alternative Approach to Risk – Based Design. By George Mermiris. Introduction. Inspiration from the study of the human brain and physical neurons Response speed for physical neurons is 10 -3 s compared to electrical circuits with 10 -9 s

By shayla
(160 views)

Hebbian Coincidence Learning

Hebbian Coincidence Learning

When one neuron contributes to the firing of another neuron the pathway between them is strengthened. That is, if the output of i is the input to j, then the weight is adjusted by a quantity proportional to c * (o i * o j). Hebbian Coincidence Learning. The rule is Δ W = c * f(X,W) * X

By lei
(151 views)

Neural Networks in ECG classification

Neural Networks in ECG classification

Neural Networks in ECG classification. Under the guidance of Prof. P. Bhattacharya Nishant Chandra Mrigen Negi Meru A Patil. Layout. History of Neural networks in medical Need for accurate processing Applications of ANN in medical What is ECG?

By long
(184 views)

Advances in WP2

Advances in WP2

Torino Meeting – 9-10 March 2006. Advances in WP2. www.loquendo.com. Activities on WP2 since last meeting. Study of innovative NN adaptation methods Models: Linear Hidden Networks Test on project adaptation corpora: WSJ0 Adaptation component WSJ1 Spoke-3 component

By guido
(76 views)

Using Neural Networks for remote OS Identification

Using Neural Networks for remote OS Identification

Using Neural Networks for remote OS Identification. Javier Burroni - Carlos Sarraute Core Security Technologies PacSec/core05 conference. OUTLINE. 1. Introduction 2. DCE-RPC Endpoint mapper 3. OS Detection based on Nmap signatures 4. Dimension reduction and training.

By sally
(84 views)

Hvordan få oversikten?

Hvordan få oversikten?

Hvordan få oversikten?. Annotering av sekvensen. Kromosom 16: et av de minste. Finding genes. What are we looking for? Proteins encoded in mRNA Non-coding RNA (ncRNA) genes Where are we looking? Prokaryotes Eukaryotes (often introns). Classes of RNA.

By adina
(97 views)

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