Bioinformatics involves the analysis of biological information using computers and statistical techniques,
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.
The sequence alignment is made between a known sequence and unknown sequence or between two unknown sequences. The known sequence is called reference sequence. The unknown sequence is called query sequence .
BLAST stands for Basic Local Alignment Search Tool. It addresses a fundamental problem in bioinformatics research. BLAST tool is used to compare a query sequence with a library or database of sequences.
In Bioinformatics, is an algorithm and program for comparing primary biological sequence information, such as the amino-acid sequences of proteins or the nucleotides of DNA and/or RNA sequences.
BLAST was developed by stochastic model of Samuel Karlin and Stephen Altschul in 1990. They proposed “a method for estimating similarities between the known DNA sequence of one organism with that of another”.
A BLAST search enables a researcher to compare a subject protein or nucleotide sequence (called a query sequence) with a library or database of sequences and identify database sequences that resemble the query sequence above a certain threshold.
Dynamic Programming is a method for solving a complex problem by breaking it down into a collection of simpler subproblems, solving each of those subproblems just once, and storing their solutions using a memory-based data structure (array, map,etc).
Bioinformatics involves the analysis of biological information using computers and statistical techniques,
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.
The sequence alignment is made between a known sequence and unknown sequence or between two unknown sequences. The known sequence is called reference sequence. The unknown sequence is called query sequence .
BLAST stands for Basic Local Alignment Search Tool. It addresses a fundamental problem in bioinformatics research. BLAST tool is used to compare a query sequence with a library or database of sequences.
In Bioinformatics, is an algorithm and program for comparing primary biological sequence information, such as the amino-acid sequences of proteins or the nucleotides of DNA and/or RNA sequences.
BLAST was developed by stochastic model of Samuel Karlin and Stephen Altschul in 1990. They proposed “a method for estimating similarities between the known DNA sequence of one organism with that of another”.
A BLAST search enables a researcher to compare a subject protein or nucleotide sequence (called a query sequence) with a library or database of sequences and identify database sequences that resemble the query sequence above a certain threshold.
Dynamic Programming is a method for solving a complex problem by breaking it down into a collection of simpler subproblems, solving each of those subproblems just once, and storing their solutions using a memory-based data structure (array, map,etc).
Methods of Track Circuit Fault Diagnosis Based on HmmIJRESJOURNAL
ABSTRACT: A fault diagnosis method of track circuit based on HMM (Hidden Markov Model) was proposed. On the basis of division of failure mechanism of the track circuit, a training mechanism of multi - sample HMM model was established, and a track circuit fault diagnosis system was composed by multiple fault classifiers. Because of the universality of this system, taking the hump section of railway and a section of ZPW- 2000A non-insulated track circuit as examples, the correctness and effectiveness of the system were verified. The result shows that the fault diagnosis method of track circuit which is based on HMM can effectively achieve six kinds of track circuit fault diagnosis. And compared with BP Neural Network fault diagnosis, it has a higher accuracy rate and has a faster computing speed, which can be used as a new solution for fault diagnosis of track circuits.
Hidden Markov model technique for dynamic spectrum accessTELKOMNIKA JOURNAL
Dynamic spectrum access is a paradigm used to access the spectrum dynamically. A hidden Markov model (HMM) is one in which you observe a sequence of emissions, but do not know the sequence of states the model went through to generate the emissions. Analysis of hidden Markov models seeks to recover the sequence of states from the observed data. In this paper, we estimate the occupancy state of channels using hidden Markov process. Using Viterbi algorithm, we generate the most likely states and compare it with the channel states. We generated two HMMs, one slowly changing and another more dynamic and compare their performance. Using the Baum-Welch algorithm and maximum likelihood algorithm we calculated the estimated transition and emission matrix, and then we compare the estimated states prediction performance of both the methods using stationary distribution of average estimated transition matrix calculated by both the methods.
Probabilistic Models of Time Series and SequencesZitao Liu
Tutorial on Probabilistic Models of Time Series and Sequences. Hidden Markov Models. Linear Dynamical Systems. Forward/backward algorithm. Kalman Filtering. Kalman Smoothing. Viterbi algorithm. Baum-Welch algorithm. Learning HMM. Learning LDS.
🔍 Unlocking the Secrets of Hidden Markov Models (HMM) - Your Guide to Probabilistic Modeling! 🤖
Welcome to another exciting episode of Machine Learning ! In this video, we dive deep into the fascinating world of Hidden Markov Models, demystifying their key concepts, applications, and inference techniques.
📚 In this video, we'll cover:
🔹 Introduction to HMM: Understanding the basics of what HMM is and how it works.
🔹 Key Concepts of the Model: Unraveling the inner workings of states, observations, and transitions in an HMM.
🔹 Applications of HMM: Discover how HMM is used in various real-world scenarios, from speech recognition to bioinformatics.
🔹 Inference in HMM: We'll walk you through both the Forward Algorithm and Viterbi Algorithm, which are essential for making predictions using HMMs.
🌦️ Plus, we've got a hands-on example demonstrating the prediction of weather for the next day based on the current day's conditions using Python and HMM. 🌤️
Whether you're a data science enthusiast or just curious about probabilistic modeling, this video will provide you with a solid foundation in Hidden Markov Models.
Don't forget to like, subscribe, and hit the notification bell to stay updated with our latest content on data science, machine learning, and more!
#HiddenMarkovModel #HMM #ProbabilisticModeling #DataScience #MachineLearning #Python #AI #WeatherPrediction #Tutorial
Enjoy the video and happy learning! 📊📈📉
Online aptitude test management system project report.pdfKamal Acharya
The purpose of on-line aptitude test system is to take online test in an efficient manner and no time wasting for checking the paper. The main objective of on-line aptitude test system is to efficiently evaluate the candidate thoroughly through a fully automated system that not only saves lot of time but also gives fast results. For students they give papers according to their convenience and time and there is no need of using extra thing like paper, pen etc. This can be used in educational institutions as well as in corporate world. Can be used anywhere any time as it is a web based application (user Location doesn’t matter). No restriction that examiner has to be present when the candidate takes the test.
Every time when lecturers/professors need to conduct examinations they have to sit down think about the questions and then create a whole new set of questions for each and every exam. In some cases the professor may want to give an open book online exam that is the student can take the exam any time anywhere, but the student might have to answer the questions in a limited time period. The professor may want to change the sequence of questions for every student. The problem that a student has is whenever a date for the exam is declared the student has to take it and there is no way he can take it at some other time. This project will create an interface for the examiner to create and store questions in a repository. It will also create an interface for the student to take examinations at his convenience and the questions and/or exams may be timed. Thereby creating an application which can be used by examiners and examinee’s simultaneously.
Examination System is very useful for Teachers/Professors. As in the teaching profession, you are responsible for writing question papers. In the conventional method, you write the question paper on paper, keep question papers separate from answers and all this information you have to keep in a locker to avoid unauthorized access. Using the Examination System you can create a question paper and everything will be written to a single exam file in encrypted format. You can set the General and Administrator password to avoid unauthorized access to your question paper. Every time you start the examination, the program shuffles all the questions and selects them randomly from the database, which reduces the chances of memorizing the questions.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Water billing management system project report.pdfKamal Acharya
Our project entitled “Water Billing Management System” aims is to generate Water bill with all the charges and penalty. Manual system that is employed is extremely laborious and quite inadequate. It only makes the process more difficult and hard.
The aim of our project is to develop a system that is meant to partially computerize the work performed in the Water Board like generating monthly Water bill, record of consuming unit of water, store record of the customer and previous unpaid record.
We used HTML/PHP as front end and MYSQL as back end for developing our project. HTML is primarily a visual design environment. We can create a android application by designing the form and that make up the user interface. Adding android application code to the form and the objects such as buttons and text boxes on them and adding any required support code in additional modular.
MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software. It is a stable ,reliable and the powerful solution with the advanced features and advantages which are as follows: Data Security.MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software.
2. • POSITION-SPECIFIC SCORING MATRICES
• A PSSM is defined as a table that contains probability information of amino acids
or nucleotides at each position of an ungapped multiple sequence alignment.
• rows represent residue positions.
• columns represent the names of residues.
• The values in the table represent log odds scores of the
residues.
• The probability values in a PSSM depend on the
number of sequences used to compile the matrix.
3. • Example of construction of a PSSM from a multiple
alignment of nucleotide sequences
4.
5. PSI-BLAST
• Profiles can be used in database searching to find
remote sequence homologs
• Part of NCBI.
• Position-specific iterated BLAST.
• builds profiles and performs database searches in an
iterative fashion.
• single query protein sequence to perform a normal
BLASTP search to generate initial similarity hits.
• The high-scoring hits are used to build a multiple
sequence alignment.
• Than profile is created.
• This method uses MORKOV MODEL foe score calculation.
6. representation of a Markov chain
Drawbacks of PSI-BLAST :-
• the high sensitivity of PSI-BLAST is also its pitfall; it is associated with low selectivity
caused by the false-positives generated in the automated profile construction process.
• If unrelated sequences are erroneously included profiles become biased.
• This problem is known as profile drift.
7. Markov Model
• describes a sequence of events that occur one after another in a chain.
• Each event determines the probability of the next event.
• Unidirectional in nature.
• Move from one position to other with certain probability known as
TRANSTION PROBILITY.
• A good example of a Markov model is the signal change of traffic lights in which the
state of the current signal depends on the state of the previous signal.(e.g., green light
switches on after red light, which switches on after yellow light).
• Biological sequences written as strings of letters can be described by Markov chains.
8. • each letter representing a state is linked together with transitional probability
values.
• allows the calculation of probability values for a given residue according to the
unique distribution frequencies of nucleotides or amino acids.
TYPES OF MARKOV MODEL :-
1) Zero order markov model.
2) First order markov model.
3) Second order markov model.
4) Higher order morkov model.
9. Hidden Markov Model
• A machine learning technique
• A discrete hill climb technique.
• some non observed factors influence state transition calculations.
• An HMM combines two or more Markov chains with only one chain consisting of
observed states and the other chains made up of unobserved (or “hidden”) states that
influence the outcome of the observed states.
10. • the probability going from one state to another state is the transition
probability.
• The probability value associated with each symbol in each state is called
emission probability.
• To calculate the total probability of a particular path of the model, both transition and
emission probabilities linking all the “hidden” as well as observed states need to be
taken into account.
• Example to use two states of a partial HMM to represent (or generate) a sequence.
11. HMM involving two interconnected Markov chains with observed and unobserved state.
12. • a character in the alignment can be in one of three types :
1) match.
2) insertion.
3) deletion.
• Match are observed state.
• Insertion and deletion are hidden state.
13. illustration of a simplified partial HMM for DNA
sequences with emission and transition probability
values. Both probability values are used to
calculate the total probability of a particular path of
the model. For example, to generate the
sequence AG, the model has to progress from A
from STATE 1 to G in STATE 2, the probability of
this path is 0.80 × 0.40 × 0.32 = 0.102. Obviously,
there are 4 × 4 = 16 different sequences this
simple model can generate. The one that has
the highest probability is AT.
14.
15. architecture of a hidden Markov model representing a multiple sequence alignment
17. • works in a similar fashion as in dynamic programming
for sequence alignment.
Viterbi Algorithm
• constructs a matrix with the maximum emission probability values of all the symbols in a
state multiplied by the transition probability for that state.
• It then uses a trace-back procedure going from the lower right corner to the upper left
corner to find the path with the highest values in the matrix.
19. Forward Algorithm
• which constructs a matrix using the sum of multiple emission
states instead of the maximum, and calculates the most likely path
from the upper left corner of the matrix to the lower right corner.
Issues with HMM :
• Limited sampling size
which causes overrepresentation of observed characters while ignoring
the unobserved characters.
• This problem is known as overfitting
20. Tools used to build HMM profile
• HMMer.
• hmmbuild.
• Hmmcalibrate.
• Hmmemit.
• Hmmsearch.
Tool to find MSA which is based on HMM.
Packages are:
Located at Washington university in USA.
21. Applications
• Human identification using Gait.
• Human action recognition from Time Sequential
Images.
• Facial expression identification from videos.
• Speech recognition.