The accumulation of genomic data is a worldwide phenomenon. Cloud-based platforms such as WuXi NextCODE’s Exchange are essential to address the fundamental big data challenge of genomics.
Hannes Smarason: Progress & Prospects in GenomicsHannes Smárason
The annual American Society of Human Genetics Meeting (ASHG 2016) is an excellent time for the field of genomics to take stock of the past and clarify our perspectives for the future.
Maintaining Momentum Post-ASHG 2014: Maximizing the Value of Large Genomic D...Hannes Smárason
The Haplotype Reference Consortium (HRC) unveiled at the American Society of Human Genetics (ASHG) 2014 aims to become the world’s most comprehensive database of genetic variations. The newly launched NextCODE Exchange will provide a browser-based hub for multi-center sharing and collaboration on collective genomic data from massive whole-genome databases like the HRC to accelerate research worldwide.
Hannes Smarason: 2015 = An Inflection Point in GenomicsHannes Smárason
This document discusses how 2015 marked a turning point for genomics and personalized medicine on a global scale. It highlights that rare diseases are an area where genomics has significantly advanced diagnosis by making testing available worldwide. Many countries now have active programs to diagnose rare diseases using genomic sequencing. The document also notes that governments around the world are increasingly supporting genomics research and initiatives to scale up the use of genomic data to improve healthcare.
Hannes smarason next code-wuxi combined technologiesHannes Smárason
Lower-cost genome sequencing has reached a point of strong commercial viability. The remaining 2 legs of the “3-legged stool” of genomics-enabling technologies —genomic analysis tools database storage—are rapidly evolving to support the use of genomic information in medical care.
Personalized Medicine: The Future is Almost HereHannes Smárason
The document discusses how genome sequencing is becoming integrated into medical care, enabling personalized medicine. It describes how genome sequencing costs have plummeted in the last decade, allowing for widespread application. Major medical centers are now installing DNA sequencers and adopting genome sequencing to define ideal treatments tailored to a patient's genes. The future of medicine is moving towards routinely sequencing every patient's genome to personalize prevention and treatment.
Hannes Smarason: Progress & Prospects in GenomicsHannes Smárason
The annual American Society of Human Genetics Meeting (ASHG 2016) is an excellent time for the field of genomics to take stock of the past and clarify our perspectives for the future.
Maintaining Momentum Post-ASHG 2014: Maximizing the Value of Large Genomic D...Hannes Smárason
The Haplotype Reference Consortium (HRC) unveiled at the American Society of Human Genetics (ASHG) 2014 aims to become the world’s most comprehensive database of genetic variations. The newly launched NextCODE Exchange will provide a browser-based hub for multi-center sharing and collaboration on collective genomic data from massive whole-genome databases like the HRC to accelerate research worldwide.
Hannes Smarason: 2015 = An Inflection Point in GenomicsHannes Smárason
This document discusses how 2015 marked a turning point for genomics and personalized medicine on a global scale. It highlights that rare diseases are an area where genomics has significantly advanced diagnosis by making testing available worldwide. Many countries now have active programs to diagnose rare diseases using genomic sequencing. The document also notes that governments around the world are increasingly supporting genomics research and initiatives to scale up the use of genomic data to improve healthcare.
Hannes smarason next code-wuxi combined technologiesHannes Smárason
Lower-cost genome sequencing has reached a point of strong commercial viability. The remaining 2 legs of the “3-legged stool” of genomics-enabling technologies —genomic analysis tools database storage—are rapidly evolving to support the use of genomic information in medical care.
Personalized Medicine: The Future is Almost HereHannes Smárason
The document discusses how genome sequencing is becoming integrated into medical care, enabling personalized medicine. It describes how genome sequencing costs have plummeted in the last decade, allowing for widespread application. Major medical centers are now installing DNA sequencers and adopting genome sequencing to define ideal treatments tailored to a patient's genes. The future of medicine is moving towards routinely sequencing every patient's genome to personalize prevention and treatment.
Ethical, Legal, and Social Implications of ELSI Learning Health Systems 2017 Conference, University of Michigan. Learning from the experience and outcomes of every cancer patient
The document introduces the All of Us Research Program, which aims to collect health data from one million Americans to advance precision medicine research. It was announced by President Obama in 2015. The program receives funding from the federal government and private partners. It collects various types of health data from participants through surveys, health records, samples, and devices. The data is stored and shared securely while protecting privacy. The goal is to generate new medical discoveries and more personalized healthcare through collaboration between researchers and participants.
Strengthening data sharing for public health: ethical, legal and political is...ExternalEvents
http://www.fao.org/about/meetings/wgs-on-food-safety-management/en/
Strengthening data sharing for public health: ethical, legal and political issues. Presentation from the Technical Meeting on the impact of Whole Genome Sequencing (WGS) on food safety management and GMI-9, 23-25 May 2016, Rome, Italy.
The document summarizes Dr. Matthieu-P. Schapranow's presentation at the Festival of Genomics in Boston on turning big medical data into precision medicine. It describes an in-memory database approach that enables real-time analysis of heterogeneous medical data sources. This allows clinicians and researchers to interactively explore patient data, clinical trials, pathways, and literature to obtain personalized treatment recommendations. The system was designed using a human-centered methodology to ensure usability, effectiveness, and feasibility for precision medicine applications.
Real world data, the National COVID-19 Cohort Consortium, and Oncology 2021Warren Kibbe
The document provides an overview of the National COVID Cohort Collaborative (N3C) presented by Warren Kibbe at a workshop. N3C is a large dataset of de-identified electronic health record data from COVID-19 patients across the United States. It aims to answer clinical research questions through federated analytics while maintaining security and privacy. The dataset contains standardized and harmonized variables from different medical centers and common data models. N3C represents a unique resource for examining the effects of COVID-19 on cancer outcomes and other clinical domains through large-scale analysis of real-world data.
Pace of technology innovation, changes in publication, separating data generation from publishing insights. Given at the 2018 VIVO conference at Duke University.
NCI Cancer Imaging Program - Cancer Research Data EcosystemWarren Kibbe
Given to the NCI Cancer Imaging Program monthly telecon on January 9th, 2017. NCI Genomic Data Commons, Beau Biden Cancer Moonshot Blue Ribbon Panel, Cancer Research Data Ecosystem and the role of imaging in precision medicine
The Human Variome Project (HVP) grew its membership and established initiatives between 2016-2020. It published standards and guidelines and strengthened relationships with international organizations. Its goals were increasing publicly available genomic variant data and supporting clinical genomics worldwide. To achieve this, it aimed to facilitate data sharing partnerships, promote standards, advance research, build genomic capacities, and train the next generation of scientists. Moving forward, it plans for its coordination function to transition to a new not-for-profit called Global Variome Ltd.
Work Package (WP) 12 – PEARL Barriers In search for an inventory and assessme...ExternalEvents
http://www.fao.org/about/meetings/wgs-on-food-safety-management/en/
International challenges regarding the future sharing of sequence data. Presentation from the Technical Meeting on the impact of Whole Genome Sequencing (WGS) on food safety management and GMI-9, 23-25 May 2016, Rome, Italy.
Florence Nightingale used data visualization to communicate public health data and drive policy change in the mid-19th century. She partnered with a statistician to analyze mortality data from the Crimean War, which showed that most soldier deaths were from preventable diseases, not combat wounds. Nightingale created "coxcomb" diagrams to visually depict the data in a clear, compelling way. Her data storytelling had a significant impact, improving sanitary conditions in military hospitals. Today, effective communication of data requires identifying the right audience and tailoring the amount, format and delivery of data to meet their needs.
RADx-UP CDCC presentation for the NIH Disaster Interest GroupWarren Kibbe
Presentation on the RADx-Underserved Populations Coordination and Data Collection Center with an emphasis on how it will help understand and reduce the disparities associated with the COVDI-19 pandemic
Why genomics needs telehealth to succeed - Lisa Alderson, Genome Medical - TFSSVSee
Genomics has the potential to revolutionize the practice of medicine by individualizing health care based on an exact knowledge of one's genetic predispositions. Learn why there is currently no sustainable business model to allow for this and how telehealth could be the first step to making personal genomics a part of everyday health care - from the Telehealth Failures & Secrets To Success Conference: vsee.com/telehealth-failures-conference
Opportunities for computing in cancer researchWarren Kibbe
- Data generation is no longer the bottleneck in oncology research - data management, analysis, and reasoning present greater challenges due to the pace of data and technology growth.
- Computing and data science are enabling researchers to move beyond simple observations to predictive modeling and interventions based on understanding complex patient trajectories over time using diverse real-world data sources.
- Machine learning and data analytics applied at scale can support tasks like tumor board decision making, identifying high-risk patients, and understanding disease at multiple levels, but require significant computing power.
Population health measurement - key takeaways from Global Burden of Disease s...Peter Speyer
Overview of the Global Burden of Disease study along with 8 key insights from turning 50K data sources into comparable measurements of health loss by country, age and sex. Insights range from finding, managing, and wrangling/prepping to analyzing and visualizing the results.
The reality of moving towards precision medicineElia Stupka
How do we move towards precision medicine? How can we deliver on the big data in health promise? Who will be the enablers and players? Pharma, Big Tech, or newcomers?
This document discusses open data in bioinformatics and the infrastructure needed to achieve sustainable development goals. It summarizes the exponential growth in biological data from advances like high-throughput sequencing platforms. The H3Africa initiative aims to apply genomics research to improve African health by supporting projects across 27 countries. The H3Africa Bioinformatics Network is developing capacity to archive and analyze the genomic and phenotypic data being collected from over 75,000 research participants to understand disease susceptibility in African populations.
Will Biomedical Research Fundamentally Change in the Era of Big Data?Philip Bourne
This document discusses how biomedical research may fundamentally change in the era of big data. It notes that biomedical research has always been data-driven, but the scope, variety, complexity and volume of data is now much greater. It also discusses the need for more open data sharing and new tools and methods for large-scale analysis. The document suggests biomedical research may move towards a more collaborative "platform" model, as seen with companies like Airbnb, with the goal of improving data access, reuse and reproducibility of research. However, overcoming challenges like incentives, trust and work practices will be important for any new platform to succeed.
Genomics: Shifting the Paradigm for Rare DiseasesHannes Smárason
As genomics is used more and supported by ever-more robust analysis and interpretation, its potential to offer a solution to rare disease diagnosis is truly game-changing.
Presentation from the "Demystifying Big Data" Technical Conference (Universidad de La Laguna, Spain, June 2014).
Biomedical sciences rely on massive data sets. By using machines capable of generating large amounts of data with low cost, science has entered the 'Big Data' era, making computational infrastructures essential to maintain, transfer and analyze all this information.
Ethical, Legal, and Social Implications of ELSI Learning Health Systems 2017 Conference, University of Michigan. Learning from the experience and outcomes of every cancer patient
The document introduces the All of Us Research Program, which aims to collect health data from one million Americans to advance precision medicine research. It was announced by President Obama in 2015. The program receives funding from the federal government and private partners. It collects various types of health data from participants through surveys, health records, samples, and devices. The data is stored and shared securely while protecting privacy. The goal is to generate new medical discoveries and more personalized healthcare through collaboration between researchers and participants.
Strengthening data sharing for public health: ethical, legal and political is...ExternalEvents
http://www.fao.org/about/meetings/wgs-on-food-safety-management/en/
Strengthening data sharing for public health: ethical, legal and political issues. Presentation from the Technical Meeting on the impact of Whole Genome Sequencing (WGS) on food safety management and GMI-9, 23-25 May 2016, Rome, Italy.
The document summarizes Dr. Matthieu-P. Schapranow's presentation at the Festival of Genomics in Boston on turning big medical data into precision medicine. It describes an in-memory database approach that enables real-time analysis of heterogeneous medical data sources. This allows clinicians and researchers to interactively explore patient data, clinical trials, pathways, and literature to obtain personalized treatment recommendations. The system was designed using a human-centered methodology to ensure usability, effectiveness, and feasibility for precision medicine applications.
Real world data, the National COVID-19 Cohort Consortium, and Oncology 2021Warren Kibbe
The document provides an overview of the National COVID Cohort Collaborative (N3C) presented by Warren Kibbe at a workshop. N3C is a large dataset of de-identified electronic health record data from COVID-19 patients across the United States. It aims to answer clinical research questions through federated analytics while maintaining security and privacy. The dataset contains standardized and harmonized variables from different medical centers and common data models. N3C represents a unique resource for examining the effects of COVID-19 on cancer outcomes and other clinical domains through large-scale analysis of real-world data.
Pace of technology innovation, changes in publication, separating data generation from publishing insights. Given at the 2018 VIVO conference at Duke University.
NCI Cancer Imaging Program - Cancer Research Data EcosystemWarren Kibbe
Given to the NCI Cancer Imaging Program monthly telecon on January 9th, 2017. NCI Genomic Data Commons, Beau Biden Cancer Moonshot Blue Ribbon Panel, Cancer Research Data Ecosystem and the role of imaging in precision medicine
The Human Variome Project (HVP) grew its membership and established initiatives between 2016-2020. It published standards and guidelines and strengthened relationships with international organizations. Its goals were increasing publicly available genomic variant data and supporting clinical genomics worldwide. To achieve this, it aimed to facilitate data sharing partnerships, promote standards, advance research, build genomic capacities, and train the next generation of scientists. Moving forward, it plans for its coordination function to transition to a new not-for-profit called Global Variome Ltd.
Work Package (WP) 12 – PEARL Barriers In search for an inventory and assessme...ExternalEvents
http://www.fao.org/about/meetings/wgs-on-food-safety-management/en/
International challenges regarding the future sharing of sequence data. Presentation from the Technical Meeting on the impact of Whole Genome Sequencing (WGS) on food safety management and GMI-9, 23-25 May 2016, Rome, Italy.
Florence Nightingale used data visualization to communicate public health data and drive policy change in the mid-19th century. She partnered with a statistician to analyze mortality data from the Crimean War, which showed that most soldier deaths were from preventable diseases, not combat wounds. Nightingale created "coxcomb" diagrams to visually depict the data in a clear, compelling way. Her data storytelling had a significant impact, improving sanitary conditions in military hospitals. Today, effective communication of data requires identifying the right audience and tailoring the amount, format and delivery of data to meet their needs.
RADx-UP CDCC presentation for the NIH Disaster Interest GroupWarren Kibbe
Presentation on the RADx-Underserved Populations Coordination and Data Collection Center with an emphasis on how it will help understand and reduce the disparities associated with the COVDI-19 pandemic
Why genomics needs telehealth to succeed - Lisa Alderson, Genome Medical - TFSSVSee
Genomics has the potential to revolutionize the practice of medicine by individualizing health care based on an exact knowledge of one's genetic predispositions. Learn why there is currently no sustainable business model to allow for this and how telehealth could be the first step to making personal genomics a part of everyday health care - from the Telehealth Failures & Secrets To Success Conference: vsee.com/telehealth-failures-conference
Opportunities for computing in cancer researchWarren Kibbe
- Data generation is no longer the bottleneck in oncology research - data management, analysis, and reasoning present greater challenges due to the pace of data and technology growth.
- Computing and data science are enabling researchers to move beyond simple observations to predictive modeling and interventions based on understanding complex patient trajectories over time using diverse real-world data sources.
- Machine learning and data analytics applied at scale can support tasks like tumor board decision making, identifying high-risk patients, and understanding disease at multiple levels, but require significant computing power.
Population health measurement - key takeaways from Global Burden of Disease s...Peter Speyer
Overview of the Global Burden of Disease study along with 8 key insights from turning 50K data sources into comparable measurements of health loss by country, age and sex. Insights range from finding, managing, and wrangling/prepping to analyzing and visualizing the results.
The reality of moving towards precision medicineElia Stupka
How do we move towards precision medicine? How can we deliver on the big data in health promise? Who will be the enablers and players? Pharma, Big Tech, or newcomers?
This document discusses open data in bioinformatics and the infrastructure needed to achieve sustainable development goals. It summarizes the exponential growth in biological data from advances like high-throughput sequencing platforms. The H3Africa initiative aims to apply genomics research to improve African health by supporting projects across 27 countries. The H3Africa Bioinformatics Network is developing capacity to archive and analyze the genomic and phenotypic data being collected from over 75,000 research participants to understand disease susceptibility in African populations.
Will Biomedical Research Fundamentally Change in the Era of Big Data?Philip Bourne
This document discusses how biomedical research may fundamentally change in the era of big data. It notes that biomedical research has always been data-driven, but the scope, variety, complexity and volume of data is now much greater. It also discusses the need for more open data sharing and new tools and methods for large-scale analysis. The document suggests biomedical research may move towards a more collaborative "platform" model, as seen with companies like Airbnb, with the goal of improving data access, reuse and reproducibility of research. However, overcoming challenges like incentives, trust and work practices will be important for any new platform to succeed.
Genomics: Shifting the Paradigm for Rare DiseasesHannes Smárason
As genomics is used more and supported by ever-more robust analysis and interpretation, its potential to offer a solution to rare disease diagnosis is truly game-changing.
Presentation from the "Demystifying Big Data" Technical Conference (Universidad de La Laguna, Spain, June 2014).
Biomedical sciences rely on massive data sets. By using machines capable of generating large amounts of data with low cost, science has entered the 'Big Data' era, making computational infrastructures essential to maintain, transfer and analyze all this information.
Lightning fast genomics with Spark, Adam and ScalaAndy Petrella
This document discusses using Apache Spark and ADAM to perform scalable genomic analysis. It provides an overview of genomics and challenges with existing approaches. ADAM uses Apache Spark and Parquet to efficiently store and query large genomic datasets. The document demonstrates clustering genomic data from the 1000 Genomes Project to predict populations, showing ADAM and Spark can handle large genomic workloads. It concludes these tools provide scalable genomic data processing but future work is needed to implement more advanced algorithms.
Challenges and Opportunities of Big Data GenomicsYasin Memari
The document discusses the challenges and opportunities of big data genomics. It notes that the bottleneck in genomics has shifted from data generation to data handling as sequencing capacity doubles every year. While compression can help address the data deluge, throughput from techniques like metagenomics and single-cell sequencing will continue to outpace storage gains. The document then explores solutions for analyzing and storing large genomic datasets through techniques like cloud computing, distributed file systems, and MapReduce frameworks.
The Plimmer® system is a next generation water treatment system that uses Capacitive Deionization (CDI) technology to reduce total dissolved solids (TDS), hardness, metals, and salts in ground and surface water to deliver drinking water that meets WHO standards. It requires no chemicals, has low power usage, and low water waste. The system treats multiple contaminants in a single pass and has over 400 global installations.
Fabricio Silva: Cloud Computing Technologies for Genomic Big Data AnalysisFlávio Codeço Coelho
This document discusses the use of cloud computing technologies for genomic big data analysis. It begins by defining big data and describing the exponential growth of genomic data. It then discusses how cloud computing provides flexibility, scalability, and accessibility for genomic data processing through virtualization and large computing clusters. Specific technologies enabled for the cloud that help with genomic analysis are described, such as Hadoop, MapReduce, and genomic analysis tools adapted for these frameworks. The document concludes by discussing challenges remaining around data transfer speeds and the need for cloud application expertise, but also describes how platforms like Galaxy Cloudman and Cloudgene allow genomic analysis in the cloud without programming expertise.
Big Data & the networked future of Science (at Ignite Seattle 7)Deepak Singh
The document discusses how big data and networked technologies are transforming science. It notes that the Human Genome Project took 15 years to sequence one genome while now a single genome can be sequenced in weeks. It also notes that the 1000 Genomes Project now generates around 100 terabytes of data per week, over 2 petabytes per year. The document highlights several quotes about how data and networked technologies are allowing more collaboration and new insights in science.
Festival of Genomics 2016 London: Analyze Genomes: Modeling and Executing Gen...Matthieu Schapranow
This presentation covers the "Analyze Genomes: Modeling and Executing Genome Data Processing Pipelines" presentation of the 2016 Festival of Genomics workshop "Big Medical Data in Precision Medicine: Challenges or Opportunities?" on Jan 19, 2016 in London.
Interviewing - why some questions are off limitsAnn Loraine
This document provides guidance on questions that are inappropriate and legally off-limits during a job interview. It notes that questions about age, race, gender, religion, disability, family or marital status are not allowed because they could enable discrimination in hiring decisions. The document advises preparing responses in case inappropriate questions are asked and knowing one's rights under laws like the Civil Rights Act, ADA and ADEA. It suggests politely redirecting to one's qualifications if asked an off-limits question. The document concludes with advising investigation of processes for filing discrimination complaints.
Bioinformatics & Genomics December NewsletterKelly Wickham
The Festival of Genomics will be coming to London in January 2016 for the first time due to high demand from one of the assistant's top clients, Seven Bridges Genomics UK Ltd. The 3-day event will include workshops, speakers and entertainment at ExCel London. The assistant has been working with Seven Bridges Genomics Ltd to help build their new UK office in London and recently placed their Senior Account Manager. The assistant can provide the best candidates for positions in genomics and bioinformatics and has an in-depth understanding of the industries.
Visualizing the genome: Techniques for presenting genome data and annotationsAnn Loraine
This document discusses techniques for visualizing genome data and annotations in genome browsers. It describes three key techniques:
1. Semantic zooming allows biologists to inspect both large genomic structures like introns and exons as well as closer details of sequences.
2. Sorting annotations into adjustable and movable tiers helps organize dense information and make patterns more visible.
3. Displaying protein motifs alongside gene structures allows biologists to quickly assess how alternative splicing impacts protein function.
Genome sequencing technology available today can accurately sequence a whole genome from an individual’s test sample for a surprisingly low cost.
As a result, the adoption of this technology is rapidly expanding as medical centers around the world embrace its utility in informing healthcare decisions—an emerging reality of personalized medicine.
Federal Research & Development for the Florida system Sept 2014 Warren Kibbe
This document discusses challenges in cancer data integration and analysis. It proposes the development of open science models, standardized data elements, and sustainable informatics infrastructure. Emerging technologies like mobile devices, social media, and cloud computing create opportunities to build a national "learning health system" for cancer. The National Cancer Institute is pursuing initiatives like the Cancer Genomics Data Commons and cloud pilots to leverage large genomic and clinical datasets using these technologies and develop predictive models to improve outcomes. The ultimate goal is a system that facilitates data sharing, continuous learning from all cancer patients, and personalized, predictive oncology.
FDA NGS and Big Data Conference September 2014Warren Kibbe
The document discusses the National Cancer Institute's efforts to address challenges in cancer data access and analysis through the development of the NCI Genomics Data Commons and NCI Cloud Pilots. The NCI Genomics Data Commons will provide integrated genomic and clinical cancer data from projects like TCGA to researchers. The NCI Cloud Pilots aim to explore cloud-based models for analyzing large cancer genomics datasets without having to download the full datasets locally, helping to enable more widespread data access and analysis. The goal is to build a national learning health system for cancer clinical genomics through open data sharing and cloud-based approaches.
The document discusses how genomics and blockchain technologies will transform healthcare by making genomic data and insights more accessible and affordable globally. It describes Shivom's vision of creating a genomic data ecosystem where individuals own and can choose to share their genomic data securely via blockchain, and how this could benefit research, precision medicine and personalized healthcare. Key features of the Shivom platform include genome sequencing and storage, a marketplace for healthcare services, and tools to incentivize data sharing and collaboration between individuals, researchers and organizations.
Presentation "The Impact of All Data on Healthcare"
Keith Perry
Associate VP & Deputy CIO
UT MD Anderson Cancer Center
With continuing advancement in both technology and medicine, the drive is on to make all data meaningful to drive medical discovery and create actionable outcomes. With tools and capabilities to capture more data than ever before, the challenge becomes linking existing structured and unstructured clinical data with genomic data to increase the industry’s analytical footprint.
Learning Objectives:
∙ Discuss the need to make all data meaningful in order to speed discovery of new knowledge
∙ Provide examples of an analytical direction that supports evolution in medicine
∙ Expose the challenges facing the industry with respect to ~omits
2016 Data Commons and Data Science Workshop June 7th and June 8th 2016. Genomic Data Commons, FAIR, NCI and making data more findable, publicly accessible, interoperable (machine readable), reusable and support recognition and attribution
Presentation that gives an overview of the impact of IT on radiology, including the growing role of biomarkers and artificial intelligence and deep learning on the (future) radiology profession. The shift to precision medicine and personalized care are explained, the reasons for a re-definition of radiology are addressed.
Beating Bugs with Big Data: Harnessing HPC to Realize the Potential of Genomi...Tom Connor
Introducing the HPC challenges associated with developing a set of clinical microbial genomics services in the NHS in Wales. Demonstrating the potential of these technologies, and the impact it is already having for the patients of the Welsh NHS.
Presentación del nodo Valenciano en Bonn en el comité de Euro-BioImagingmaigva
BIMCV is a medical imaging databank in the Valencia Region of Spain that aims to transform its collection of medical images and associated clinical data into an environment for translational innovation in healthcare. It provides mass storage and high-performance computing capabilities to facilitate large-scale image processing, comparison, and validation. BIMCV houses over 5 million clinical cases per year from various medical imaging modalities. It offers open access to this imaging and clinical data to support population imaging studies and disease signature identification through data-driven projects like analyzing neurological images to improve treatment of diseases like multiple sclerosis.
- National challenges in cancer research include lowering barriers to data access and analysis, and integrating clinical and basic research data to enable improved outcomes.
- Disruptive technologies like high-throughput biology and ubiquitous computing are generating large amounts of molecular and clinical cancer data.
- The NCI is working to build infrastructure like the Genomics Data Commons and Cloud Pilots to make these data widely accessible and support data analysis.
- The goal is to develop a national "learning health system" that applies insights from real-world cancer data to research and clinical practice to continuously improve patient care and outcomes.
NCI Cancer Genomics, Open Science and PMI: FAIR Warren Kibbe
Talk given to the NLM Fellows on July 8, 2016. Touches on Cancer Genomics, Open Science and PMI: FAIR in NCI genomics thinking and projects. Includes discussion of the Genomic Data Commons (GDC), Cancer Data Ecosystem, Data sharing, and the NCI cancer clinical trials open API.
Biomedical Clusters, Clouds and Commons - DePaul Colloquium Oct 24, 2014Robert Grossman
This document discusses how biomedical discovery is being disrupted by big data. Large genomic, phenotype, and environmental datasets are needed to understand complex diseases that result from combinations of many rare variants. However, analyzing large biomedical data is costly and difficult given the standard model of local computing. The document proposes creating large "commons" of community data and computing as an instrument for big data discovery. Examples are given of the Cancer Genome Atlas project, which has petabytes of research data on thousands of cancer patients, and how tumors evolve over time. Overall, the document argues that new models of shared biomedical clouds and commons are needed to enable cost-effective analysis of big biomedical data.
Data supporting precision oncology fda wakibbeWarren Kibbe
This document discusses how data is supporting precision oncology through three main points:
1) Our ability to generate and analyze biomedical data continues to grow in terms of variety and volume from sources like genomics and imaging.
2) Analyzing multi-scale, multi-modal temporal data requires advances in data science like machine learning and artificial intelligence.
3) Standards like FAIR data principles are needed to enable data sharing and the creation of a learning health system for cancer through harmonization and interoperability of data.
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
Muktapishti is a traditional Ayurvedic preparation made from Shoditha Mukta (Purified Pearl), is believed to help regulate thyroid function and reduce symptoms of hyperthyroidism due to its cooling and balancing properties. Clinical evidence on its efficacy remains limited, necessitating further research to validate its therapeutic benefits.
Local Advanced Lung Cancer: Artificial Intelligence, Synergetics, Complex Sys...Oleg Kshivets
Overall life span (LS) was 1671.7±1721.6 days and cumulative 5YS reached 62.4%, 10 years – 50.4%, 20 years – 44.6%. 94 LCP lived more than 5 years without cancer (LS=2958.6±1723.6 days), 22 – more than 10 years (LS=5571±1841.8 days). 67 LCP died because of LC (LS=471.9±344 days). AT significantly improved 5YS (68% vs. 53.7%) (P=0.028 by log-rank test). Cox modeling displayed that 5YS of LCP significantly depended on: N0-N12, T3-4, blood cell circuit, cell ratio factors (ratio between cancer cells-CC and blood cells subpopulations), LC cell dynamics, recalcification time, heparin tolerance, prothrombin index, protein, AT, procedure type (P=0.000-0.031). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and N0-12 (rank=1), thrombocytes/CC (rank=2), segmented neutrophils/CC (3), eosinophils/CC (4), erythrocytes/CC (5), healthy cells/CC (6), lymphocytes/CC (7), stick neutrophils/CC (8), leucocytes/CC (9), monocytes/CC (10). Correct prediction of 5YS was 100% by neural networks computing (error=0.000; area under ROC curve=1.0).
share - Lions, tigers, AI and health misinformation, oh my!.pptxTina Purnat
• Pitfalls and pivots needed to use AI effectively in public health
• Evidence-based strategies to address health misinformation effectively
• Building trust with communities online and offline
• Equipping health professionals to address questions, concerns and health misinformation
• Assessing risk and mitigating harm from adverse health narratives in communities, health workforce and health system
ABDOMINAL TRAUMA in pediatrics part one.drhasanrajab
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Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
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Genomics: Big Data Leading to Big Opportunities
1. Genomics:
Big
Data
Leading
to
Big
Opportunities
Hannes
Smarason
Genome
Sequencing
|Personalized
Medicine
|
Transforming
Health
Care
2. The
Big
Data
of
Genomics
• We
are
in
the
midst
of
an
explosion
of
‘Big
Data’
in
a
variety
of
human
endeavors:
– Roughly
2.5
quintillion
bytes
of
data
are
generated
every
day;
and
– 90%
of
the
world’s
data
was
created
in
the
last
two
years.
• Genomics
has
become
a
major
source
of
the
growth
of
such
big
data,
particularly
as
the
cost
of
sequencing
genomes
has
plummeted
source:
http://www-‐01.ibm.com/software/data/bigdata/what-‐is-‐big-‐data.html
3. Genomics:
Driving
the
Exponential
Growth
of
Big
Data
• The
raw
sequence
data
for
just
one
person’s
whole
genome
use
as
much
as
100GB
–
and
already
hundreds
of
thousands
of
individual
genomes
have
been
sequenced.
– With
more
than
2,500
high-‐throughput
sequencing
instruments
currently
used
in
55
countries
across
the
globe,
more
genomes
are
added
every
daya
– The
aggregate
amount
of
genomic
data
is
growing
explosively,
and
NGS
sequencing
data
are
estimated
to
have
doubled
in
volume
annually
since
2007b
• Impressive
population-‐wide
sequencing
efforts
are
leading
the
way,
from
100,000
genomes
in
England,
Saudi
Arabia,
and
Iceland
to
350,000
in
Qatar
to
a
million
in
both
China
and
the
U.S.
• And
earlier
this
month,
the
CEO
of
the
Cleveland
Clinic
predicted
that
soon
children
will
routinely
have
their
whole
genomes
sequenced
at
birth,
implying
a
near-‐future
in
which
10s
of
millions
of
new
genomes
are
sequenced
annually.c
• sources:
a
http://omicsmaps.com/;
b
http://www.genengnews.com/issue/toc/248/;
c
http://www.cnbc.com/2015/10/30/gene-‐testing-‐set-‐for-‐major-‐breakthrough-‐clinic-‐ceo.html
4. Creating
Big
Data
is
Just
the
Beginning
• Analysis
of
big
data
in
genomics
and
associated
informatics
is
already
generating
significant
progress
in
cancer
care
and
the
diagnosis
of
rare
diseases.
• Yet
there
is
a
broad
consensus
that
a
‘data
bottleneck’
is
hampering
the
collaboration
and
discovery
that
could
continue
to
revolutionize
heathcare.
• In
order
to
use
genetic
information
to
prevent
or
treat
disease,
researchers
and
physicians
need
resources
that:
– Draw
together
useful
data
from
disparate
sources;
– Facilitate
analysis
and
collaboration;
and
– Improve
clinical
practice.
5. Turning
Data
into
Resources
• The
power
of
genomic
analysis
needs
to
expand
outward:
– from
major
research
centers
and
hospitals
– to
the
myriad
clinics
and
community
hospitals
where
many
patients
receive
care.
• To
have
the
greatest
impact
on
the
broadest
population,
clinicians
throughout
the
world’s
health
systems
need
access
to
the
big
data
generated
by
DNA
sequencing,
even
– or
perhaps
especially
– if
they
are
not
affiliated
with
research
institutions.
• And
along
with
access,
they
need
tools
to
analyze
and
interpret
the
data.
6. Answers
in
the
Cloud
• Key
medical
advancements
require
not
only
big
data,
but
also
tools
and
resources
to
generate,
interpret,
and
share
analysis
of
millions
of
genomes.
• Cloud-‐based
platforms
– such
as
WuXi NextCODE’s Exchange
– are
essential
to
address
the
fundamental
big
data
challenge
of
genomics:
leveraging
massive
datasets
to
improve
patient
care
in
the
clinic.
• Collaboration
in
the
cloud
works
to
dismantle
existing
‘data
silos,’
genomic
information
hosted
only
on
local
servers
and
analyzed
on
idiosyncratic,
closed
platforms.
• The
WuXi NextCODE Exchange,
in
contrast,
is
a
browser-‐based
hub
that:
– affords
secure,
seamless
collaboration
with
colleagues
around
the
world;
– provides
access
to
NextCODE’s tools
for
making
the
critical
links
between
variation
in
the
genome
and
disease
and
other
phenotypes;
and
– supports
analysis
with
harmonized
links
to
the
the
most
important
public
reference
data.
7. The
Evolution
of
Genomic
Analysis
• The
big
data
of
genomics
will
continue
to
expand,
and
our
approaches
to
analyzing
genomic
data
need
to
continue
to
evolve
to
meet
the
growing
demands
of
clinicians
and
researchers.
• At
WuXi NextCODE:
– We
are
building
upon
our
heritage
of
conducting
the
largest
analysis
of
genomic
data
(deCODE’s path-‐breaking
Icelandic
analysis)
by
assembling
an
ever-‐growing
database
of
human
genomes.
– We
are
committed
to
driving
the
movement
of
sequence
data
into
patient
diagnosis
and
care
through
user-‐friendly,
leading-‐edge
analysis
and
informatics.
• I
am
confident
that
data
analysis
and
collaboration
in
the
cloud
will
revolutionize
healthcare,
and
exceptionally
proud
that
WuXi NextCODE’s
Exchange
is
at
the
forefront
of
this
exciting
advancement.