This presentation is about the usage of Artificial Intelligence in Bioinformatics. These slides give the basic knowledge about usage of Artificial Intelligence in Bioinformatics.
EG-CompBio presentation about Artificial Intelligence in Bioinformatics covering:
-AI (Types, Development)
-Deep Learning (Architecture)
-Bioinformatics Fields
-Input formats for AI
-AI Challenges in Biology
-Example: (Proteomics, Transcriptomics)
-Metagenomics: @ NU
-Taxonomic Classification
-Phenotype Classification
-How to begin in AI in Bioinformatics
An introduction to bioinformatics practices and aims will be given and contrasted against approaches from other fields. Most importantly, it will be discussed how bioinformatics fits into the discovery cycle for hypothesis driven neuroscience research.
Ab Initio Protein Structure Prediction is a method to determine the tertiary structure of protein in the absence of experimentally solved structure of a similar/homologous protein. This method builds protein structure guided by energy function.
I had prepared this presentation for an internal project during my masters degree course.
Bioinformatics and Artificial Intelligence (AI) the interrelation between the...Swapsg
The relation between the Bioinformatics and the Artificial Intelligence (AI) specified in it as both the fields are new.
credits-https://poweredtemplate.com/03075/0/index.html
Computational Biology and BioinformaticsSharif Shuvo
Computational Biology and Bioinformatics is a rapidly developing multi-disciplinary field. The systematic achievement of data made possible by genomics and proteomics technologies has created a tremendous gap between available data and their biological interpretation.
EG-CompBio presentation about Artificial Intelligence in Bioinformatics covering:
-AI (Types, Development)
-Deep Learning (Architecture)
-Bioinformatics Fields
-Input formats for AI
-AI Challenges in Biology
-Example: (Proteomics, Transcriptomics)
-Metagenomics: @ NU
-Taxonomic Classification
-Phenotype Classification
-How to begin in AI in Bioinformatics
An introduction to bioinformatics practices and aims will be given and contrasted against approaches from other fields. Most importantly, it will be discussed how bioinformatics fits into the discovery cycle for hypothesis driven neuroscience research.
Ab Initio Protein Structure Prediction is a method to determine the tertiary structure of protein in the absence of experimentally solved structure of a similar/homologous protein. This method builds protein structure guided by energy function.
I had prepared this presentation for an internal project during my masters degree course.
Bioinformatics and Artificial Intelligence (AI) the interrelation between the...Swapsg
The relation between the Bioinformatics and the Artificial Intelligence (AI) specified in it as both the fields are new.
credits-https://poweredtemplate.com/03075/0/index.html
Computational Biology and BioinformaticsSharif Shuvo
Computational Biology and Bioinformatics is a rapidly developing multi-disciplinary field. The systematic achievement of data made possible by genomics and proteomics technologies has created a tremendous gap between available data and their biological interpretation.
After sequencing of the genome has been done, the first thing that comes to mind is "Where are the genes?". Genome annotation is the process of attaching information to the biological sequences. It is an active area of research and it would help scientists a lot to undergo with their wet lab projects once they know the coding parts of a genome.
Bioinformatics is a science of extracting knowledge from biological data, сomplexity and amount of which, has increased significantly over the past decades. To meet the challenges ahead, more sophisticated algorithms and assets should be adopted. Thus, Machine Learning has become an everyday tool in Bioinformatics, that helps to solve important biological riddles. In this report, In this presentation I discussed examples of how using well-known Machine Learning methods, bioinformaticians and computer scientists help doctors and biologists diagnose and treat deadly diseases.
it will help you to understand how the protein microarrays are made, what are the different types and what all purposes they are used for. its very useful ppt
An integrated publicly accessible bioinformatics resource to support genomic/proteomic research and scientific discovery.
Established in 1984, by the National Biomedical Research Foundation (NBRF) Georgetown University Medial Center, Washington D.C., USA.
It is the source of annotated protein databases and analysis tools for the researchers.
Serve as primary resource for the exploration of protein information.
Accessible by text search for entry and list retrieval, and also BLAST search and peptide match.
Gene prediction is the process of determining where a coding gene might be in a genomic sequence. Functional proteins must begin with a Start codon (where DNA transcription begins), and end with a Stop codon (where transcription ends).
The Role of Bioinformatics in The Drug Discovery ProcessAdebowale Qazeem
The Role of Bioinformatics in The Drug Discovery Process, is an undergraduate seminar presentation in the department of Biochemistry, Faculty of life Sciences, University of Ilorin, Ilorin.
An Introduction to Bioinformatics
Drexel University INFO648-900-200915
A Presentation of Health Informatics Group 5
Cecilia Vernes
Joel Abueg
Kadodjomon Yeo
Sharon McDowell Hall
Terrence Hughes
After sequencing of the genome has been done, the first thing that comes to mind is "Where are the genes?". Genome annotation is the process of attaching information to the biological sequences. It is an active area of research and it would help scientists a lot to undergo with their wet lab projects once they know the coding parts of a genome.
Bioinformatics is a science of extracting knowledge from biological data, сomplexity and amount of which, has increased significantly over the past decades. To meet the challenges ahead, more sophisticated algorithms and assets should be adopted. Thus, Machine Learning has become an everyday tool in Bioinformatics, that helps to solve important biological riddles. In this report, In this presentation I discussed examples of how using well-known Machine Learning methods, bioinformaticians and computer scientists help doctors and biologists diagnose and treat deadly diseases.
it will help you to understand how the protein microarrays are made, what are the different types and what all purposes they are used for. its very useful ppt
An integrated publicly accessible bioinformatics resource to support genomic/proteomic research and scientific discovery.
Established in 1984, by the National Biomedical Research Foundation (NBRF) Georgetown University Medial Center, Washington D.C., USA.
It is the source of annotated protein databases and analysis tools for the researchers.
Serve as primary resource for the exploration of protein information.
Accessible by text search for entry and list retrieval, and also BLAST search and peptide match.
Gene prediction is the process of determining where a coding gene might be in a genomic sequence. Functional proteins must begin with a Start codon (where DNA transcription begins), and end with a Stop codon (where transcription ends).
The Role of Bioinformatics in The Drug Discovery ProcessAdebowale Qazeem
The Role of Bioinformatics in The Drug Discovery Process, is an undergraduate seminar presentation in the department of Biochemistry, Faculty of life Sciences, University of Ilorin, Ilorin.
An Introduction to Bioinformatics
Drexel University INFO648-900-200915
A Presentation of Health Informatics Group 5
Cecilia Vernes
Joel Abueg
Kadodjomon Yeo
Sharon McDowell Hall
Terrence Hughes
This Presentation will give you an overview about Artificial Intelligence : definition, advantages , disadvantages , benefits , applications .
We hope it to be useful .
Kim Solez Singularity explained and promoted fall 2016Kim Solez ,
Dr. Kim Solez presents "The Singularity Explained and Promoted" September 6, 2016 in the Technology and Future of Medicine course LABMP 590 at the University of Alberta in Edmonton, Alberta, Canada. Copyright (c) 2016, JustMachines Inc.
Smart Data Slides: Emerging Hardware Choices for Modern AI Data ManagementDATAVERSITY
Leading edge AI applications have always been resource-intensive and known for stretching the limits of conventional (von Neumann architecture) computer performance. Specialized hardware, purpose built to optimize AI applications, is not new. In fact, it should be no surprise that the very first .com internet domain was registered to Symbolics - a company that built the Lisp Machine, a dedicated AI workstation - in 1985. In the last three decades, of course, the performance of conventional computers has improved dramatically with advances in chip density (Moore’s Law) leading to faster processor speeds, memory speeds, and massively parallel architectures. And yet, some applications - like machine vision for real time video analysis and deep machine learning - always need more power.
Participants in this webinar will learn the fundamentals of the three hardware approaches that are receiving significant investments and demonstrating significant promise for AI applications.
- neuromorphic/neurosynaptic architectures (brain-inspired hardware)
- GPUs (graphics processing units, optimized for AI algorithms), and
- quantum computers (based on principles and properties of quantum-mechanics rather than binary logic).
Note - This webinar requires no previous knowledge of hardware or computer architectures.
"Bridging the Gap between Bioinformatics and Medical Informatics"INBIOMEDvision
INBIOMEDvision workshop at MIE2012 - XXIV Conference of the European Federation for Medical informatics. August 26–29, 2012. Pisa, Italy. Presentations: M.A. Mayer, V. López Alonso, N.Shublaq.
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Jack D Ryger: World Landmarks Light Up To Honor ParisJack D. Ryger
Jack D Ryger highlights a few of the many landmarks across the globe that have chosen to honor Paris by lighting up in the colors of the French Flag, following the tragic events of 11/13/15.
Kenneth Verlage took up the topic of the transformation of IT through digitization into a product, and of the impact and importance of this change for your organization, technology and innovation work.
Author - Kenneth Verlage
The triump of artificial intelligence in search of intelligence deveshTechXpla
www.techxpla.com/events CSI Mumbai and Artificial Intelligence Mumbai (AIM) Present
Conference : Rise of Artificial Intelligence startups; This session started with introduction to Artificial Intelligence and current status by Devesh Rajadhyax CEO of Cere Labs. Here are the slides
Misdiagnosis or missed diagnosis are common in hospitals. Common measures to curb these include observation or morbidity/mortality conference or autopsy studies. However, these are often expensive and are biased. Clinical Decision Support Systems like ai-med can reduce these misdiagnosis by using artificial intelligence and other internet technologies.
BioinformaticsPurpose Bioinformatics is the combination of comp.docxrichardnorman90310
Bioinformatics
Purpose: Bioinformatics is the combination of computer science and biology which used various methods of storing and retrieving the biological data which have pros and cons, scientists are able to discover new information on various diseases, its mutation, it helps in differentiating one organism from another by analyzing their genetic data, biological development and will stop various crimes, disadvantages and develops the algorithm that helps in measuring the sequence similarity.
1. Introduction: Bioinformatics is a field which include molecular biology, statistics, issues, computer problems, and extensive mathematics complex problem. It has two stages deliberately gather various insights from the natural information and to make a computational model. It can be found in the study area of precision and preventive medicine.
0. Background info on of bioinformaticsComment by R Daniel Creider: A, B, C and D are not a part of the introduction. The outline is not organized correctly
0. How to approach bioinformatics?
1. Goals of Bioinformatics
0. Development of efficient algorithms
0. Extension of experimental data by predictions
1. Advantages of bioinformatics
1. World is getting information on new discovery and crimes are prevented
1. Discover new information on various diseases
1. How organisms mutate
1. How it analyses data to differentiate one organism from another
1. Disadvantages of bioinformatics
2. Data manipulation, complexity, lack of well-trained manpower to use the software
2. Misuse of the information
0. Problems behinds it
0. Data about the genetic information lack proper analyzed
0. Importance of Bioinformatics
3. Genetic research
0. Genomics and proteomics
1.
Solution
of the problem
1. Use software wisely
1. Decrease its complexity
1. Future of the bioinformatics
2. Bioinformatics is the present and future of biotechnology
0. Use for research and exchange information for comparison, storage and analysis
BIOINFORMATICS: A Technical Report
Texas A&M University-Commerce
Bishow KunwarComment by R Daniel Creider: Your name comes before the name of the University.
Abstract
The main aim of Bioinformatics is to improve the various methods of storing, retrieving and organizing the biological data by critically evaluating the data. The effectiveness of bi informatics in the field of genetics and genomics is playing its part in a way that particularly in textual mining of biological development. Bioinformatics is the application which is the mix of two fields (software engineering and science). It is a field that includes different things like sub-atomic science, measurement issues, software engineering issues, and broad arithmetic complex issues.
Keywords; Bioinformatics, Genetic, Genomic, Biological Development
Introduction:
Bioinformatics is the application which is the combination of two fields (computer science and biology). It is a field that involves multiple things like molecular .
BioinformaticsPurpose Bioinformatics is the combination of comp.docxjasoninnes20
Bioinformatics
Purpose: Bioinformatics is the combination of computer science and biology which used various methods of storing and retrieving the biological data which have pros and cons, scientists are able to discover new information on various diseases, its mutation, it helps in differentiating one organism from another by analyzing their genetic data, biological development and will stop various crimes, disadvantages and develops the algorithm that helps in measuring the sequence similarity.
1. Introduction: Bioinformatics is a field which include molecular biology, statistics, issues, computer problems, and extensive mathematics complex problem. It has two stages deliberately gather various insights from the natural information and to make a computational model. It can be found in the study area of precision and preventive medicine.
0. Background info on of bioinformaticsComment by R Daniel Creider: A, B, C and D are not a part of the introduction. The outline is not organized correctly
0. How to approach bioinformatics?
1. Goals of Bioinformatics
0. Development of efficient algorithms
0. Extension of experimental data by predictions
1. Advantages of bioinformatics
1. World is getting information on new discovery and crimes are prevented
1. Discover new information on various diseases
1. How organisms mutate
1. How it analyses data to differentiate one organism from another
1. Disadvantages of bioinformatics
2. Data manipulation, complexity, lack of well-trained manpower to use the software
2. Misuse of the information
0. Problems behinds it
0. Data about the genetic information lack proper analyzed
0. Importance of Bioinformatics
3. Genetic research
0. Genomics and proteomics
1.
Solution
of the problem
1. Use software wisely
1. Decrease its complexity
1. Future of the bioinformatics
2. Bioinformatics is the present and future of biotechnology
0. Use for research and exchange information for comparison, storage and analysis
BIOINFORMATICS: A Technical Report
Texas A&M University-Commerce
Bishow KunwarComment by R Daniel Creider: Your name comes before the name of the University.
Abstract
The main aim of Bioinformatics is to improve the various methods of storing, retrieving and organizing the biological data by critically evaluating the data. The effectiveness of bi informatics in the field of genetics and genomics is playing its part in a way that particularly in textual mining of biological development. Bioinformatics is the application which is the mix of two fields (software engineering and science). It is a field that includes different things like sub-atomic science, measurement issues, software engineering issues, and broad arithmetic complex issues.
Keywords; Bioinformatics, Genetic, Genomic, Biological Development
Introduction:
Bioinformatics is the application which is the combination of two fields (computer science and biology). It is a field that involves multiple things like molecular ...
introduction,history scope and applications of
relation to other fields , bioinformatics,biological databases,computers internet,sequence development, and
introduction to sequence development and alignment
Bioinformatics: Bioinformatics, Healthcare Informatics and Analytics for Improved Healthcare System, Intelligent Monitoring and Control for Improved Healthcare System.
Bioinformatics is a hybrid science that links biological data with techniques for information storage, distribution, and analysis to support multiple areas of scientific research, including biomedicine.
Similar to Uses of Artificial Intelligence in Bioinformatics (20)
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GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
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Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
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3. WHAT IS BIOINFORMATICS?
Bioinformatics is the application of
computer technology to the
management of biological
information.
Bioinformatics is an
interdisciplinary research field that
combines biology, computer
science, mathematics and
statistics into a broad-based field
that will have profound impacts on
all fields of biology.
4. Aim of Bioinformatics:
1) Organizing Data in the correct
manner
1) Proper Analysis of the Data
2) Interpreting the data in a
biologically meaningful manner
6. Artificial intelligence (AI) has
increasingly gained attention in
bioinformatics research and
computational molecular biology.
1) AI Algorithms to be used for keeping
records
2) Choosing a particular method for
analyzing data
3) Helping Interpret Large Amount of
Data quickly by using computer
7. Example:
DNA sequencing with artificial intelligence:
Sequencing of DNA is among the most important tasks in molecular
biology. DNA chips are considered to be a more rapid alternative to more
common gel-based methods of sequencing. DNA chips commonly are
made with the set of all possible probes eight nucleotides in length
(octamers) generating 65,536 unique probes spaced on a 1.6 cm2 array
(Fodor, Read, Pirrung, Stryer, Lu and Solas, 1991). For example, consider
the DNA target sequence ATTGATTCG, with length NZ9 and a DNA chip
with all possible probes of length nZ4. A DNA chip with probe length n will
have 4n positions in the grid on the DNA chip. Thus, for a probe length 4
there exist 256 grid positions, each associated with a unique probe
sequence. All possible 4-nucleotide probes would exist in the set: {AAAA,
AAAT, ., and TTTT}.
8. In SBH, an appropriate length probe must be used to
unambiguously determine a target of length N. When is large (O40
nucleotides), a probe of length 4 cannot be used to reconstruct the
target with a high probability of success (Fogel, Chellapilla, &
Fogel, 1998). As N increases, the probability of redundancy in the
target increases making unambiguously reconstruction difficult
(Noble, 1995). Hence the AI methods are well suited to solve the
DNA sequencing problem unambiguously and obtain a near
optimal solution. A hidden Markov model (HMM) is a statistical
model, which is very well suited for many tasks in molecular
biology (Krogh, 1998). The most popular use of the HMM in
molecular biology is as a ‘probabilistic profile’ of a protein family,
which is called a profile HMM. From a family of proteins (or DNA)
a
9. profile HMM can be made for searching a database for other
members of the family. Boufounos, El-Difrawy , & Ehrlich (2004)
used HMMs in DNA sequencing, where they developed an approach
to the DNA base calling problem. In addition, they also modeled the
state emission densities using artificial neural networks and provided
a modified Baum-Welch re-estimation procedure to perform training.
Fuzzy logic is a mathematical framework, which is compatible with
poorly quantitative yet qualitatively signifi- cant data. Fuzzy logic is a
natural language for linguistic modeling, thus it is consistent with the
qualitative linguistic– graphical methods conventionally used to
describe biological systems (Woolf & Wang, 2000). Fuzzy if-then
rules were also developed to describe the basic molecular properties
and behaviors of DNA inside the living cell.
15. Artificial Intelligence and heuristic methods are extremely important
for the present and future developments of bioinformatics, a very
recent and strategic discipline having the potential for a revolutionary
impact on biotechnology, pharmacology, and medicine. While
computation has already transformed our industrial society, a
comparable biotechnological transformation is on the horizon. In the
last few years it has become clear that these two exponentially
growing areas are actually converging.
Molecular biologists are currently engaged in some of the most
impressive data collection projects. Recent genome-sequencing
projects are generating an enormous amount of data related to the
function and the structure of biological molecules and sequences.
16. AI and heuristic methods (in particular machine
learning and data mining, cluster analysis, pattern
recognition, knowledge representation) can provide
key solutions for the new challenges posed by the
progressive transformation of biology into a data-
massive science.
17. The main objective is to create an environment for
(1) cross-disseminating state-of-the-art knowledge both to
AI researchers and computational biologists
(2) creating a common substrate of knowledge that both
AI people and computational biologists can understand;
(3) stimulating the development of specialized AI
techniques, keeping in mind the application to
computational biology
(4) fostering new collaborations among scientists having
similar or complementary backgrounds.
18. Some Recent Approaches:
Computational analysis of biological data
Artificial intelligence, machine learning, and
heuristic methods, including neural and belief
networks
Prediction of protein structure (secondary
structure, contact maps)
The working draft of the human genome
Genome annotation
Computational tools for gene regulation
Analysis of gene expression data and their
applications
Computer assisted drug discovery
Knowledge discovery in biological domains
20. Bioinformatics is being used in following fields:
1) Microbial genome applications
2) Molecular medicine
3) Personalized medicine
4) Preventative medicine
5) Gene therapy
6) Drug development
7) Antibiotic resistance
8) Evolutionary studies
9) Waste cleanup Biotechnology
10) Climate change Studies
11) Alternative energy sources
12) Crop improvement
13) Forensic analysis
14) Bio-weapon creation
15) Insect resistance
16) Improve nutritional quality
17) Development of Drought resistant varieties
Vetinary Science
21. FUTURE AND CONCLUSION
Bioinformatics in combination with AI
techniques will play an increasingly
important role in streamlining complex
analytical workflows to perform a multi-
step analysis within one analytical
framework.
Such workflows enable processing and
analysis of biological data that are
complex, and are growing at an
exponential rate.
The complexity of biological questions
and thus analytical tasks for answering
22. AI techniques that deploy machine
learning, knowledge discovery, and
reasoning are continuously improving.
Future of bioinformatics lays in large-
scale analysis driven by computational
intelligence that will produce huge
savings in time, effort, and money and
accelerate biological discovery.
23. AI based tools can perform both the
complex tasks based on reasoning, as
well as repetitive menial tasks that can
be performed over a huge
combinatorial space and simulate
millions of wet-laboratory experiments.
These fields experience rapidly
growing knowledge that increased
understanding of both the human
immune system and pathogens.