In this session we will explore how Google's Cloud services (CloudML, Vision, Genomics API) can be used to process genomic and phenotypic data and solve problems in healthcare and agriculture.
Deep learning in medicine: An introduction and applications to next-generatio...Allen Day, PhD
Deep learning has enabled dramatic advances in image recognition performance. In this talk I will discuss using a deep convolutional neural network to detect genetic variation in aligned next-generation sequencing human read data. Our method, called DeepVariant, both outperforms existing genotyping tools and generalizes across genome builds and even to other species. DeepVariant represents a significant step from expert-driven statistical modeling towards more automatic deep learning approaches for developing software to interpret biological instrumentation data.
Adversarial Analytics - 2013 Strata & Hadoop World TalkRobert Grossman
This is a talk I gave at the Strata Conference and Hadoop World in New York City on October 28, 2013. It describes predictive modeling in the context of modeling an adversary's behavior.
In this session we will explore how Google's Cloud services (CloudML, Vision, Genomics API) can be used to process genomic and phenotypic data and solve problems in healthcare and agriculture.
Deep learning in medicine: An introduction and applications to next-generatio...Allen Day, PhD
Deep learning has enabled dramatic advances in image recognition performance. In this talk I will discuss using a deep convolutional neural network to detect genetic variation in aligned next-generation sequencing human read data. Our method, called DeepVariant, both outperforms existing genotyping tools and generalizes across genome builds and even to other species. DeepVariant represents a significant step from expert-driven statistical modeling towards more automatic deep learning approaches for developing software to interpret biological instrumentation data.
Adversarial Analytics - 2013 Strata & Hadoop World TalkRobert Grossman
This is a talk I gave at the Strata Conference and Hadoop World in New York City on October 28, 2013. It describes predictive modeling in the context of modeling an adversary's behavior.
Talking Data is the largest independent big data service company in China. Their network covers 70% of the mobile services nationwide with 3 billion ad clicks per day. Amongst those clicks, 90% are potentially fraudulent. Click fraud is happening at an overwhelming volume leading to misusage of data and wasting money. Hence, Kaggle (a platform for predictive modeling and analytics competitions from the U.S.) has partnered up with TalkingData to help resolve this issue.
This paper is to build predictive analysis models using traditional and Big Data methods to determine whether a smartphone app will be downloaded after clicking an advertisement. We have used data named “TalkingData AdTracking Fraud Detection Challenge”, which is of 7GB and given by a Kaggle competition. Four classification models are implemented with this massive data set in order to predict fraud in both traditional and Big Data methods. We define it fraud when the user clicked on an advertisement without downloading. The traditional platform has a resource limitation to build models with data set over a giga-byte so that we generate a sample data for the traditional models and adopt the full data set for the models in the Big Data Spark ML systems. We also present the accuracy and performance of the models implemented in both traditional and Big Data systems.
Talk @ ACM SF Bayarea Chapter on Deep Learning for medical imaging space.
The talk covers use cases, special challenges and solutions for Deep Learning for Medical Image Analysis using Tensorflow+Keras. You will learn about:
- Use cases for Deep Learning in Medical Image Analysis
- Different DNN architectures used for Medical Image Analysis
- Special purpose compute / accelerators for Deep Learning (in the Cloud / On-prem)
- How to parallelize your models for faster training of models and serving for inferenceing.
- Optimization techniques to get the best performance from your cluster (like Kubernetes/ Apache Mesos / Spark)
- How to build an efficient Data Pipeline for Medical Image Analysis using Deep Learning
- Resources to jump start your journey - like public data sets, common models used in Medical Image Analysis
Edge-based Discovery of Training Data for Machine LearningZiqiang Feng
(Accepted and presented in Symposium on Edge Computing, Seattle, Oct 2018)
We show how edge-based early discard of data can greatly improve the productivity of a human expert in assembling a large training set for machine learning. This task may span multiple data sources that are live (e.g., video cameras) or archival (data sets dispersed over the Internet). The critical resource here is the attention of the expert. We describe Eureka, an interactive system that leverages edge computing to greatly improve the productivity of experts in this task. Our experimental results show that Eureka reduces the labeling effort needed to construct a training set by two orders of magnitude relative to a brute-force approach.
Intro to Deep Learning for Medical Image Analysis, with Dan Lee from Dentuit AISeth Grimes
Dan Lee from Dentuit AI presented an Intro to Deep Learning for Medical Image Analysis at the Maryland AI meetup (https://www.meetup.com/Maryland-AI), May 27, 2020. Visit https://www.youtube.com/watch?v=xl8i7CGDQi0 for video.
Using the Open Science Data Cloud for Data Science ResearchRobert Grossman
The Open Science Data Cloud is a petabyte scale science cloud for managing, analyzing, and sharing large datasets. We give an overview of the Open Science Data Cloud and how it can be used for data science research.
ICIC 2017: The Next Era: Deep Learning for Biomedical ResearchDr. Haxel Consult
Srinivasan Parthiban (VINGYANI, India)
Deep learning is hot, making waves, delivering results, and is somewhat of a buzzword today. There is a desire to apply deep learning to anything that is digital. Unlike the brain, these artificial neural networks have a very strict predefined structure. The brain is made up of neurons that talk to each other via electrical and chemical signals. We do not differentiate between these two types of signals in artificial neural networks. They are essentially a series of advanced statistics based exercises that review the past to indicate the likely future. Another buzzword that was used for the last few years across all industries is “big data”. In biomedical and health sciences, both unstructured and structured information constitute "big data". On the one hand deep learning needs lot of data whereas “big data" has value only when it generates actionable insight. Given this, these two areas are destined to be married. The couple is made for each other. The time is ripe now for a synergistic association that will benefit the pharmaceutical companies. It may be only a short time before we have vice presidents of machine learning or deep learning in pharmaceutical and biotechnology companies. This presentation will review the prominent deep learning methods and discuss these techniques for their usefulness in biomedical and health informatics.
Multipleregression covidmobility and Covid-19 policy recommendationKan Yuenyong
Multiple Regression Analysis and Covid-19 policy is the contemporary agenda. It demonstrates how to use Python to do data wrangler, to use R to do statistical analysis, and is enable to publish in standard academic journal. The model will explain whether lockdown policy is relevant to control Covid-19 outbreak? It cinc
iMicrobe and iVirus: Extending the iPlant cyberinfrastructure from plants to ...Bonnie Hurwitz
iMicrobe and iVirus: Extending the iPlant cyberinfrastructure from plants to microbes. Overview of work underway to add applications and computational analysis pipelines to iPlant for metagenomics and microbial ecology.
Ultra Fast Deep Learning in Hybrid Cloud Using Intel Analytics Zoo & AlluxioAlluxio, Inc.
Alluxio Global Online Meetup
Apr 23, 2020
For more Alluxio events: https://www.alluxio.io/events/
Speakers:
Jiao (Jennie) Wang, Intel
Tsai Louie, Intel
Bin Fan, Alluxio
Today, many people run deep learning applications with training data from separate storage such as object storage or remote data centers. This presentation will demo the Intel Analytics Zoo + Alluxio stack, an architecture that enables high performance while keeping cost and resource efficiency balanced without network being I/O bottlenecked.
Intel Analytics Zoo is a unified data analytics and AI platform open-sourced by Intel. It seamlessly unites TensorFlow, Keras, PyTorch, Spark, Flink, and Ray programs into an integrated pipeline, which can transparently scale from a laptop to large clusters to process production big data. Alluxio, as an open-source data orchestration layer, accelerates data loading and processing in Analytics Zoo deep learning applications.
This talk, we will go over:
- What is Analytics Zoo and how it works
- How to run Analytics Zoo with Alluxio in deep learning applications
- Initial performance benchmark results using the Analytics Zoo + Alluxio stack
Talking Data is the largest independent big data service company in China. Their network covers 70% of the mobile services nationwide with 3 billion ad clicks per day. Amongst those clicks, 90% are potentially fraudulent. Click fraud is happening at an overwhelming volume leading to misusage of data and wasting money. Hence, Kaggle (a platform for predictive modeling and analytics competitions from the U.S.) has partnered up with TalkingData to help resolve this issue.
This paper is to build predictive analysis models using traditional and Big Data methods to determine whether a smartphone app will be downloaded after clicking an advertisement. We have used data named “TalkingData AdTracking Fraud Detection Challenge”, which is of 7GB and given by a Kaggle competition. Four classification models are implemented with this massive data set in order to predict fraud in both traditional and Big Data methods. We define it fraud when the user clicked on an advertisement without downloading. The traditional platform has a resource limitation to build models with data set over a giga-byte so that we generate a sample data for the traditional models and adopt the full data set for the models in the Big Data Spark ML systems. We also present the accuracy and performance of the models implemented in both traditional and Big Data systems.
Talk @ ACM SF Bayarea Chapter on Deep Learning for medical imaging space.
The talk covers use cases, special challenges and solutions for Deep Learning for Medical Image Analysis using Tensorflow+Keras. You will learn about:
- Use cases for Deep Learning in Medical Image Analysis
- Different DNN architectures used for Medical Image Analysis
- Special purpose compute / accelerators for Deep Learning (in the Cloud / On-prem)
- How to parallelize your models for faster training of models and serving for inferenceing.
- Optimization techniques to get the best performance from your cluster (like Kubernetes/ Apache Mesos / Spark)
- How to build an efficient Data Pipeline for Medical Image Analysis using Deep Learning
- Resources to jump start your journey - like public data sets, common models used in Medical Image Analysis
Edge-based Discovery of Training Data for Machine LearningZiqiang Feng
(Accepted and presented in Symposium on Edge Computing, Seattle, Oct 2018)
We show how edge-based early discard of data can greatly improve the productivity of a human expert in assembling a large training set for machine learning. This task may span multiple data sources that are live (e.g., video cameras) or archival (data sets dispersed over the Internet). The critical resource here is the attention of the expert. We describe Eureka, an interactive system that leverages edge computing to greatly improve the productivity of experts in this task. Our experimental results show that Eureka reduces the labeling effort needed to construct a training set by two orders of magnitude relative to a brute-force approach.
Intro to Deep Learning for Medical Image Analysis, with Dan Lee from Dentuit AISeth Grimes
Dan Lee from Dentuit AI presented an Intro to Deep Learning for Medical Image Analysis at the Maryland AI meetup (https://www.meetup.com/Maryland-AI), May 27, 2020. Visit https://www.youtube.com/watch?v=xl8i7CGDQi0 for video.
Using the Open Science Data Cloud for Data Science ResearchRobert Grossman
The Open Science Data Cloud is a petabyte scale science cloud for managing, analyzing, and sharing large datasets. We give an overview of the Open Science Data Cloud and how it can be used for data science research.
ICIC 2017: The Next Era: Deep Learning for Biomedical ResearchDr. Haxel Consult
Srinivasan Parthiban (VINGYANI, India)
Deep learning is hot, making waves, delivering results, and is somewhat of a buzzword today. There is a desire to apply deep learning to anything that is digital. Unlike the brain, these artificial neural networks have a very strict predefined structure. The brain is made up of neurons that talk to each other via electrical and chemical signals. We do not differentiate between these two types of signals in artificial neural networks. They are essentially a series of advanced statistics based exercises that review the past to indicate the likely future. Another buzzword that was used for the last few years across all industries is “big data”. In biomedical and health sciences, both unstructured and structured information constitute "big data". On the one hand deep learning needs lot of data whereas “big data" has value only when it generates actionable insight. Given this, these two areas are destined to be married. The couple is made for each other. The time is ripe now for a synergistic association that will benefit the pharmaceutical companies. It may be only a short time before we have vice presidents of machine learning or deep learning in pharmaceutical and biotechnology companies. This presentation will review the prominent deep learning methods and discuss these techniques for their usefulness in biomedical and health informatics.
Multipleregression covidmobility and Covid-19 policy recommendationKan Yuenyong
Multiple Regression Analysis and Covid-19 policy is the contemporary agenda. It demonstrates how to use Python to do data wrangler, to use R to do statistical analysis, and is enable to publish in standard academic journal. The model will explain whether lockdown policy is relevant to control Covid-19 outbreak? It cinc
iMicrobe and iVirus: Extending the iPlant cyberinfrastructure from plants to ...Bonnie Hurwitz
iMicrobe and iVirus: Extending the iPlant cyberinfrastructure from plants to microbes. Overview of work underway to add applications and computational analysis pipelines to iPlant for metagenomics and microbial ecology.
Ultra Fast Deep Learning in Hybrid Cloud Using Intel Analytics Zoo & AlluxioAlluxio, Inc.
Alluxio Global Online Meetup
Apr 23, 2020
For more Alluxio events: https://www.alluxio.io/events/
Speakers:
Jiao (Jennie) Wang, Intel
Tsai Louie, Intel
Bin Fan, Alluxio
Today, many people run deep learning applications with training data from separate storage such as object storage or remote data centers. This presentation will demo the Intel Analytics Zoo + Alluxio stack, an architecture that enables high performance while keeping cost and resource efficiency balanced without network being I/O bottlenecked.
Intel Analytics Zoo is a unified data analytics and AI platform open-sourced by Intel. It seamlessly unites TensorFlow, Keras, PyTorch, Spark, Flink, and Ray programs into an integrated pipeline, which can transparently scale from a laptop to large clusters to process production big data. Alluxio, as an open-source data orchestration layer, accelerates data loading and processing in Analytics Zoo deep learning applications.
This talk, we will go over:
- What is Analytics Zoo and how it works
- How to run Analytics Zoo with Alluxio in deep learning applications
- Initial performance benchmark results using the Analytics Zoo + Alluxio stack
This is a talk I gave at a Northwestern University - Complete Genomics Workshop on April 21, 2011 about using clouds to support research in genomics and related areas.
Presentation from the 2013 Bio-IT World conference. It describes the design and implementation of data and compute infrastructure for the New York Genome Center.
Deep Machine Learning for Making Sense of Biotech Data - From Clean Energy to...Wesley De Neve
Deep Machine Learning for Making Sense of Biotech Data - From Clean Energy to Smart Farming. Presentation given at the Korea-Europe International Conference on the 4th Industry Revolution.
Presented at OECD Workshop on Systematic Reviews in the Scope of the Endocrine Disrupter Testing and Assessment (EDTA) Conceptual Framework Level 1 in Paris, France
Cloud Accelerated Genomics by Allen Day of GoogleData Con LA
Abstract:
In this session we will explore how Google's Cloud services (CloudML, Vision, Genomics API) can be used to process genomic and phenotypic data and solve problems in healthcare and agriculture.
Bio:
Allen Day is a Science Advocate with Google Cloud. He's a professional software developer and storyteller with expertise in computational biology, statistics, and distributed computing. Prior to joining Google in Seattle, Allen was based in Singapore as Chief Scientist at MapR, a Silicon Valley BigData platform company.
MobiDE’2012, Phoenix, AZ, United States, 20 May, 2012Charith Perera
Charith Perera, Arkady Zaslavsky, Peter Christen, Ali Salehi, Dimitrios Georgakopoulos, Connecting Mobile Things to Global Sensor Network Middleware using System-generated Wrappers, Proceedings of the 11th ACM International Workshop on Data Engineering for Wireless and Mobile Access (ACM SIGMOD/PODS-Workshop-MobiDE), Scottsdale, Arizona, USA, May, 2012
Big Data and Advanced Data Intensive ComputingJongwook Woo
MapReduce is not working well at real time processing and iterative algorithm, which are mostly for machine learning and graph algorithms. This slide shows Spark, Giraph and Hadoop use cases in Science not in Business.
Google Cloud Platform: Prototype ->Production-> Planet scaleIdan Tohami
As one of Big Data’s Founding Fathers, Google explored the technological changes we faced over the past 10 years and present their solutions to the new data challenges within the Google Cloud ecosystem
IDB-Cloud Providing Bioinformatics Services on Cloudstratuslab
A presentation of IDB (Infrastructure Distributed for Biology) using StratusLab technology by Christophe Blanchet and Clément Gauthey at Lille, France, May 2013.
Presentation given by Appistry's Vice President of Product Strategy, Sultan Meghi at the World Genome Data Analysis Summit. Meghi presented about the big data challenges facing labs as they strive to manage the flow of genetic data from sequencer to the clinic.
Keynote on software sustainability given at the 2nd Annual Netherlands eScience Symposium, November 2014.
Based on the article
Carole Goble ,
Better Software, Better Research
Issue No.05 - Sept.-Oct. (2014 vol.18)
pp: 4-8
IEEE Computer Society
http://www.computer.org/csdl/mags/ic/2014/05/mic2014050004.pdf
http://doi.ieeecomputersociety.org/10.1109/MIC.2014.88
http://www.software.ac.uk/resources/publications/better-software-better-research
Similar to 20170406 Genomics@Google - KeyGene - Wageningen (20)
Genome Analysis Pipelines with Spark and ADAMAllen Day, PhD
Spark is a powerful new tool for processing large volumes of data quickly across a cluster of networked computers.
Typical bioinformatics workflow requirements are well-matched to Spark’s capabilities. However, Spark is not commonly used because many legacy bioinformatics applications make assumptions about their computing environment. These assumptions present a barrier to integrating the tools into more modern computing environments.
These barriers are quickly coming down. ADAM is a software library and set of tools built on top of Spark that make it easy work with file formats commonly used for genome analysis like FastQ, BAM, and VCF.
In this presentation, we’ll explore how a step that is common to many bioinformatics workflows, sequence alignment, can done with Bowtie and ADAM inside a Spark environment to quickly align short reads to a reference genome. A complete code example is demonstrated and provided at https://github.com/allenday/spark-genome-alignment-demo
Hadoop as a Platform for Genomics - Strata 2015, San JoseAllen Day, PhD
Personalized medicine holds much promise to improve the quality of human life.
However, personalizing medicine depends on genome analysis software that does not scale well. Given the potential impact on society, genomics takes first place among fields of science that can benefit from Hadoop.
A single human genome contains about 3 billion base pairs. This is less than 1 gigabyte of data but the intermediate data produced by a DNA sequencer, required to produce a sequenced human genome, is many hundreds of times larger. Beyond the huge storage requirement, deep genomic analysis across large populations of humans requires enormous computational capacity as well.
Interestingly enough, while genome scientists have adopted the concept of MapReduce for parallelizing I/O, they have not embraced the Hadoop ecosystem. For example, the popular Genome Analysis Toolkit (GATK) uses a proprietary MapReduce implementation that can scale vertically but not horizontally.
The science driving genomic analyses is rapidly changing, but the operational problems of processing data from DNA sequencers quickly and reliably are not new.
I present an analysis of the parallels in the fundamental limiting components of the '90s internet boom and the DNA sequencing boom that is currently underway, and illustrate how Hadoop, a proven application architecture used widely in BigData and commercial internet applications can be reused in the genomics sector.
Renaissance in Medicine - Strata - NoSQL and GenomicsAllen Day, PhD
Renaissance in Medicine: Next-Generation Big Data Workloads
Instead of using 1s and 0s (base2), biological software is encoded as A, T, C, and G (base4). DNA sequencers are simply devices for converting information encoded in base4 to base2. Improvements in DNA sequencing technology are happening at a rate that outstrips even Moore’s Law of Computing. As a result, the number of human genomes converted to base2 and uploaded for analysis is rapidly increasing.
Medicine is undergoing a renaissance made possible by analyzing and creating insights from this huge and growing number of genomes. Personalized medicine is simply the practical application of these insights.
In this session, I will show how ETL and MapReduce can be applied in a clinical setting. I will also show how NoSQL and advanced analytics can be used to “reverse engineer” the genetic causes of disease. Such information can be used to predict and prevent individual suffering, as well as to increase the overall health of a society.
2014.06.30 - Renaissance in Medicine - Singapore Management University - Data...Allen Day, PhD
First draft for upcoming Hadoop World presentation "Renaissance in Medicine" that gives an overview of the upcoming changes in medical practice that are enabled by BigData technologies. Specific algorithmic techniques are detailed that enable this use case.
R + Storm Moneyball - Realtime Advanced Statistics - Hadoop Summit - San JoseAllen Day, PhD
Architecting R into the Storm Application Development Process
~~~~~
The business need for real-time analytics at large scale has focused attention on the use of Apache Storm, but an approach that is sometimes overlooked is the use of Storm and R together. This novel combination of real-time processing with Storm and the practical but powerful statistical analysis offered by R substantially extends the usefulness of Storm as a solution to a variety of business critical problems. By architecting R into the Storm application development process, Storm developers can be much more effective. The aim of this design is not necessarily to deploy faster code but rather to deploy code faster. Just a few lines of R code can be used in place of lengthy Storm code for the purpose of early exploration – you can easily evaluate alternative approaches and quickly make a working prototype.
In this presentation, Allen will build a bridge from basic real-time business goals to the technical design of solutions. We will take an example of a real-world use case, compose an implementation of the use case as Storm components (spouts, bolts, etc.) and highlight how R can be an effective tool in prototyping a solution.
Q: Can I simply hire one rockstar data scientist to cover all this kind of work?
A: No, interdisciplinary work requires teams
A: Hire leads who can speak the lingo of each required discipline
A: Hire individual contributors who cover 2+ roles, when possible
Statistical Thinking – Solve the Whole Problem
BONUS: Meta Organization – Integration with Adjacent Teams
Co-authors Allen Day @allenday and Paco Nathan @pacoid
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine.
Bio
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
4. Google confidential │ Do not distribute
Google is good at handling massive volumes of data
uploads per minute
users
search index
query response time
400hrs
500M+
100PB+
0.25s
5. Google confidential │ Do not distribute
Google can handle large amounts of genomic data
uploads per minute
users
search index
query response time
400hrs
500M+
100PB+
0.25s
~8WGS
>100x US PhDs
~1M WGS
0.25s
6. Google confidential │ Do not distribute
BioQuery Analysis Engine
Medical Records Genomics Devices Imaging Patient Reports
Baseline Study Data Private Data
Pharma Health Providers …
Google’s vision to tackle complex health data
Public Data
7. Google confidential │ Do not distribute
Google Genomics is more than infrastructure
General-purpose
cloud infrastructure
Genomics-specific
featuresGenomics API
Virtual Machines & Storage
Data Services & Tools
8. Google confidential │ Do not distribute
Information: principal coordinates analysis (1000 genomes)
14. Verily
Observation: programming a computer to be clever is harder than
programming a computer to learn to be clever.
Intro to machine learning and deep learning
16. Verily
● Modern reincarnation of neural networks
● Collection of simple trainable mathematical
units, organized in layers, that collaborate to
compute a complicated function
● Learns features from raw, heterogeneous data
● Loosely inspired by what (little) we know
about the brain
The deep learning revolution
21. Public Datasets Project
https://cloud.google.com/bigquery/public-data/
A public dataset is any dataset that is stored in BigQuery and made available to the general public. This URL lists a
special group of public datasets that Google BigQuery hosts for you to access and integrate into your applications.
Google pays for the storage of these data sets and provides public access to the data via BigQuery. You pay only for the
queries that you perform on the data (the first 1TB per month is free)
22. GraphConnect SF 2015 / Graphs Are Feeding The World, Tim Williamson, Data Scientist, Monsanto
https://www.youtube.com/watch?v=6KEvLURBenM
23. Verily | Confidential & Proprietary
Motivation
● Variant calling in next-generation sequencing:
○ Well-understood, hard inference problem in genomics.
○ Significant statistical modeling component.
○ Lots of opportunity for improvements
● DeepVariant:
○ Teach deep learning to call variants using aligned NGS reads
25. Verily | Confidential & Proprietary
... but lots of places in the genome are difficult
26. Creating a universal SNP and small indel
variant caller with deep neural networks
Ryan Poplin, Cory McLean, Dan Newburger, Jojo Dijamco, Nam Nguyen, Dion Loy,
Sam Gross, Madeleine Cule, Peyton Greenside, Justin Zook, Marc Salit, Mark
DePristo, Verily Life Sciences, October 2016
27. DNN (Inception V3) Predicts True Genotype from Pileup Images
{ 0.001, 0.994, 0.005 }
{ 0.001, 0.990, 0.009 }
{ 0.000, 0.001, 0.999 }
{ 0.600, 0.399, 0.001 }
Output:
Probability of diploid
genotype states
{ HOM_REF, HET, HOM_VAR }
Raw pixels
Input:
Millions of labeled pileup
images from gold standard
samples
28. Verily | Confidential & Proprietary
Using deep learning for ultra-accurate mutation detection
Input:
Millions of labeled
pileup image
stacks from gold
standard sample
Raw pixels
{ 0.001, 0.994, 0.005 }
{ 0.001, 0.990, 0.009 }
{ 0.000, 0.001, 0.999 }
{ 0.600, 0.399, 0.001 }
Output:
Probability distribution
over the three diploid
genotype states
{ HOM_REF, HET, HOM_VAR }
28
29. Verily | Confidential & Proprietary
Example DNA read pileup “images”
true snps true indels false variants
red = {A,C,G,T}. green = {quality score}. blue = {read strand}.
alpha = {matches ref genome}.
30. Verily | Confidential & Proprietary
PrecisionFDA: unique opportunity with blinded truth sample
NA12878
31. Verily | Confidential & Proprietary
DeepVariant won an award at PrecisionFDA competition
99.85
99.70
98.91
● Overall F-measure
combines SNP and
indel performance
● Blinded sample
shows no
overfitting to
NA12878 with
Verily’s pipelines
31
32. Verily | Confidential & Proprietary
DeepVariant has the best site discovery accuracy
● Verily’s internal
assessment of
precisionFDA
submissions
focusing on
variant
discovery
accuracy in
blinded truth
sample