schedule Aug 9th 01:45 - 03:15 PM place Neptune people 76 Interested

The event disease happens when there is a slip in the finely orchestrated dance between physiology, environment and genes. Treatment with chemicals (natural, synthetic or combination) solved some diseases but others persisted and got propagated along the generations. Molecular basis of disease became prime center of studies to understand and to analyze root cause. Cancer also showed a way that origin of disease, detection, prognosis and treatment along with cure was not so uncomplicated process. Treatment of diseases had to be done case by case basis (no one size fits).

With the advent of next generation sequencing, high through put analysis, enhanced computing power and new aspirations with neural network to address this conundrum of complicated genetic elements (structure and function of various genes in our systems). This requires the genomic material extraction, their sequencing (automated system) and analysis to map the strings of As, Ts, Gs, and Cs which yields genomic dataset. These datasets are too large for traditional and applied statistical techniques. Consequently, the important signals are often incredibly small along with blaring technical noise. This further requires far more sophisticated analysis techniques. Artificial intelligence and deep learning gives us the power to draw clinically useful information from the genetic datasets obtained by sequencing.

Precision of these analyses have become vital and way forward for disease detection, its predisposition, empowers medical authorities to make fair and situationally decision about patient treatment strategies. This kind of genomic profiling, prediction and mode of disease management is useful to tailoring FDA approved treatment strategies based on these molecular disease drivers and patient’s molecular makeup.

Now, the present scenario encourages designing, developing, testing of medicine based on existing genetic insights and models. Deep learning models are helping to analyze and interpreting tiny genetic variations ( like SNPs – Single Nucleotide Polymorphisms) which result in unraveling of crucial cellular process like metabolism, DNA wear and tear. These models are also responsible in identifying disease like cancer risk signatures from various body fluids. They have the immense potential to revolutionize healthcare ecosystem. Clinical data collection is not streamlined and done in a haphazard manner and the requirement of data to be amenable to a uniform fetchable and possibility to be combined with genetic information would power the value, interpretation and decisive patient treatment modalities and their outcomes.

There is hugh inflow of medical data from emerging human wearable technologies, along with other health data integrated with ability to do quickly carry out complex analyses on rich genomic databases over the cloud technologies … would revitalize disease fighting capability of humans. Last but still upcoming area of application in direct to consumer genomics (success of 23andMe).

This road map promises an end-to-end system to face disease in its all forms and nature. Medical research, and its applications like gene therapies, gene editing technologies like CRISPR, molecular diagnostics and precision medicine could be revolutionized by tailoring a high-throughput computing method and its application to enhanced genomic datasets.

 
 

Outline/Structure of the Workshop

Basics of Genetics(10 minutes)

A. Types of Nucleic Acid (DNA & RNA)

B. Structure of Nucleic Acid

C. What is gene?

D. Basic gene regulation

Basics of genetic mutation(10 minutes)

A. What is genetic mutation?

B. Types of mutations

C. Why mutation occurs

D. Origin of cancer and it’s progression

Basics of genetic testing (10 minutes)

A. Sequencing technique

B. Next generation sequencing

Hands On:(CNN )- 1 hour

A. Problem 1: Detecting actively coding regions (the discovery of transcription-factor binding sites in DNA.)- 30 minutes(Handson)

B. Problem 2: Deep convolutional neural networks for accurate somatic mutation detection -30 minutes( Handson )

Learning Outcome

Participants will

  1. Gain insight into human genomics and healthcare
  2. Develop an intuitive understanding of sequence models.
  3. Exciting knowledge about emerging area of research

Target Audience

Anyone who is interested in healthcare and emerging trends in human genetics, Data Scientists, Data Analysts, Machine Learning engineers , Life Sciences / Genomics Researchers, Deep Learning Engineers

Prerequisites for Attendees

This is an advanced workshop targeted towards data scientists who have good knowledge in Deep Learning. There will be an introduction to Genomics required for the workshop , hence data scientists without prior knowledge in Genomics will be able to comprehend the concepts involved in the workshop.

schedule Submitted 5 months ago

Public Feedback

comment Suggest improvements to the Speaker
  • Dr. Vikas Agrawal
    By Dr. Vikas Agrawal  ~  4 months ago
    reply Reply

    Dear Usha: Thanks for the proposal.  As we discussed, please mention the time break down of each topic. In addition, we could summarize the genomics background and place additional emphasis on the technical aspects. You can assume some knowledge of CNNs. Warm Regards, Vikas

    • Usha Rengaraju
      By Usha Rengaraju  ~  4 months ago
      reply Reply

      Dear Dr.Vikas,

      The time breakdown for the workshop will be below.

      Presenters : Dr. Vijayalakshmi and Dr. Jyothirmayeee

      Basics of Genetics(15 min)

      Basics of genetic mutation(15 min)

      Basics of genetic testing(15 min)

      Presenter: Usha Rengaraju

      Hands on workshop:(45 min)

      The processed data sets and the colab notebooks with detailed explanation will be provided in advance .

      https://drive.google.com/file/d/11QqkUds3rImh2VRtDrvLXkelVnNzouwp/view?usp=sharing

      The above ppt contains the overview of all the problem statements which will be covered in the workshop.The first slide contains the concepts which are crucial to understand the problem statement.

      DNA Accessibility
      DNA Regulation by methylation
      DNA Transcription 
      Detection of Mutants/Variants
       
      The remaining slides contain detailed description of the problem statements .

      Refresher to CNN (5 min)

      Problem Statement 1(25 min) :

      Description of Problem statement (5 minutes)

      Notebook walkthrough (20 minutes)

      Problem Statement 2(15 min) :

      Description of Problem statement (5 minutes)

      Notebook walkthrough (10 minutes)

      Bonus Notebook: Additional Notebook covering problem statement 3 and 4 with detailed explanation will be provided for the audience to try post the session.

      Bonus Video: 20 minute recorded video of the recent research in Cancer Genomics by Dr.Vijayalakshmi Mahadevan will also be uploaded to watch post the session.

      Supplementary Materials :

      In order to accomodate audience who do not have deep learning knowledge but have interest towards this topic , we will provide the following supplementary materials in advance .

      1) Detailed Notebook covering basics of CNN

      2) A document containing Genetics basics and terminologies required for the session.

      We are open to restructuring the session format according to suggestions. Kindly let us know your thoughts on the same.

      Thanks and Regards,

      Usha Rengaraju

      • Dr. Vikas Agrawal
        By Dr. Vikas Agrawal  ~  4 months ago
        reply Reply

        Thanks, Usha. Given the expertise of our presenters, could we please compress the genomics and genetics part of the tutorial to 30 minutes and expand the hands-on workshop to an hour to allow for the physical reality of people accessing notebooks and following along. Warm Regards, Vikas

        • Usha Rengaraju
          By Usha Rengaraju  ~  4 months ago
          reply Reply

          Dear Dr.Vikas,

          Thank you for the suggestions.I have updated the proposal as 30 minutes for Genomics, Genetics part and 1 hour for Handson.

          Thanks and Regards,

          Usha Rengaraju

           

           


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    Workshop
    Advanced

    { This is a handson workshop in pgmpy package. The creator of pgmpy package Abinash Panda will do the code demonstration }

    Crude oil plays an important role in the macroeconomic stability and it heavily influences the performance of the global financial markets. Unexpected fluctuations in the real price of crude oil are detrimental to the welfare of both oil-importing and oil-exporting economies.Global macro hedge-funds view forecast the price of oil as one of the key variables in generating macroeconomic projections and it also plays an important role for policy makers in predicting recessions.

    Probabilistic Graphical Models can help in improving the accuracy of existing quantitative models for crude oil price prediction as it takes in to account many different macroeconomic and geopolitical variables .

    Hidden Markov Models are used to detect underlying regimes of the time-series data by discretising the continuous time-series data. In this workshop we use Baum-Welch algorithm for learning the HMMs, and Viterbi Algorithm to find the sequence of hidden states (i.e. the regimes) given the observed states (i.e. monthly differences) of the time-series.

    Belief Networks are used to analyse the probability of a regime in the Crude Oil given the evidence as a set of different regimes in the macroeconomic factors . Greedy Hill Climbing algorithm is used to learn the Belief Network, and the parameters are then learned using Bayesian Estimation using a K2 prior. Inference is then performed on the Belief Networks to obtain a forecast of the crude oil markets, and the forecast is tested on real data.

  • Liked Saikat Sarkar
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    Saikat Sarkar / Dhanya Parameshwaran / Dr Sweta Choudhary / Raunak Bhandari / Srikanth Ramaswamy / Usha Rengaraju - AI meets Neuroscience

    480 Mins
    Workshop
    Advanced

    This is a mixer workshop with lot of clinicians , medical experts , Neuroimaging experts ,Neuroscientists, data scientists and statisticians will come under one roof to bring together this revolutionary workshop.

    The theme will be updated soon .

    Our celebrity and distinguished presenter Srikanth Ramaswamy who is an advisor at Mysuru Consulting Group and also works Blue Brain Project at the EPFL will be delivering an expert talk in the workshop.

    https://www.linkedin.com/in/ramaswamysrikanth/

    { This workshop will be a combination of panel discussions , expert talk and neuroimaging data science workshop ( applying machine learning and deep learning algorithms to Neuroimaging data sets}

    { We are currently onboarding several experts from Neuroscience domain --Neurosurgeons , Neuroscientists and Computational Neuroscientists .Details of the speakers will be released soon }

    Abstract for the Neuroimaging Data Science Part of the workshop:

    The study of the human brain with neuroimaging technologies is at the cusp of an exciting era of Big Data. Many data collection projects, such as the NIH-funded Human Connectome Project, have made large, high- quality datasets of human neuroimaging data freely available to researchers. These large data sets promise to provide important new insights about human brain structure and function, and to provide us the clues needed to address a variety of neurological and psychiatric disorders. However, neuroscience researchers still face substantial challenges in capitalizing on these data, because these Big Data require a different set of technical and theoretical tools than those that are required for analyzing traditional experimental data. These skills and ideas, collectively referred to as Data Science, include knowledge in computer science and software engineering, databases, machine learning and statistics, and data visualization.

    The workshop covers Data analysis, statistics and data visualization and applying cutting-edge analytics to complex and multimodal neuroimaging datasets . Topics which will be covered in this workshop are statistics, associative techniques, graph theoretical analysis, causal models, nonparametric inference, and meta-analytical synthesis.

  • Liked Raunak Bhandari
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    Raunak Bhandari / Ankit Desai / Usha Rengaraju - Knowledge Graph from Natural Language: Incorporating order from textual chaos

    90 Mins
    Workshop
    Advanced

    Intro

    What If I told you that instead of the age-old saying that "a picture is worth a thousand words", it could be that "a word is worth a thousand pictures"?

    Language evolved as an abstraction of distilled information observed and collected from the environment for sophisticated and efficient interpersonal communication and is responsible for humanity's ability to collaborate by storing and sharing experiences. Words represent evocative abstractions over information encoded in our memory and are a composition of many primitive information types.

    That is why language processing is a much more challenging domain and witnessed a delayed 'imagenet' moment.

    One of the cornerstone applications of natural language processing is to leverage the language's inherent structural properties to build a knowledge graph of the world.

    Knowledge Graphs

    Knowledge graph is a form of a rich knowledge base which represents information as an interconnected web of entities and their interactions with each other. This naturally manifests as a graph data structure, where nodes represent entities and the relationship between them are the edges.

    Automatically constructing and leveraging it in an intelligent system is an AI-hard problem, and an amalgamation of a wide variety of fields like natural language processing, information extraction and retrieval, graph algorithms, deep learning, etc.

    It represents a paradigm shift for artificial intelligence systems by going beyond deep learning driven pattern recognition and towards more sophisticated forms of intelligence rooted in reasoning to solve much more complicated tasks.

    To elucidate the differences between reasoning and pattern recognition: consider the problem of computer vision: the vision stack processes an image to detect shapes and patterns in order to identify objects - this is pattern recognition, whereas reasoning is much more complex - to associate detected objects with each other in order to meaningfully describe a scene. For this to be accomplished, a system needs to have a rich understanding of the entities within the scene and their relationships with each other.

    To understand a scene where a person is drinking a can of cola, a system needs to understand concepts like people, that they drink certain liquids via their mouths, liquids can be placed into metallic containers which can be held within a palm to be consumed, and the generational phenomenon that is cola, among others. A sophisticated vision system can then use this rich understanding to fetch details about cola in-order to alert the user of their calorie intake, or to update preferences for a customer. A Knowledge Graph's 'awareness' of the world phenomenons can thus be used to augment a vision system to facilitate such higher order semantic reasoning.

    In production systems though, reasoning may be cast into a pattern recognition problem by limiting the scope of the system for feasibility, but this may be insufficient as the complexity of the system scales or we try to solve general intelligence.

    Challenges in building a Knowledge Graph

    There are two primary challenges towards integrating knowledge graphs in systems: acquisition of knowledge and construction of the graph and effectively leveraging it with robust algorithms to solve reasoning tasks. Creation of the knowledge graph can vary widely depending on the breadth and complexity of the domain - from just manual curation to automatically constructing it by leveraging unstructured/semi-structured sources of knowledge, like books and Wikipedia.

    Many natural language processing tasks are precursors towards building knowledge graphs from unstructured text, like syntactic parsing, information extraction, entity linking, named entity recognition, relationship extraction, semantic parsing, semantic role labeling, entity disambiguation, etc. Open information extraction is an active area of research on extracting semantic triplets of object ('John'), predicate ('eats'), subject ('burger') from plain text, which are used to build the knowledge graph automatically.

    A very interesting approach to this problem is the extraction of frame semantics. Frame semantics relates linguistic semantics to encyclopedic knowledge and the basic idea is that the meaning of a word is linked to all essential knowledge that relates to it, for eg. to understand the word "sell", it's necessary to also know about commercial transactions, which involve a seller, buyer, goods, payment, and the relations between these, which can be represented in a knowledge graph.

    This workshop will focus on building such a knowledge graph from unstructured text.

    Learn good research practices like organizing code and modularizing output for productive data wrangling to improve algorithm performance.

    Knowledge Graph at Embibe

    We will showcase how Embibe's proprietary Knowledge Graph manifests and how it's leveraged across a multitude of projects in our Data Science Lab.

  • Liked Shrutika Poyrekar
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    Shrutika Poyrekar / kiran karkera / Usha Rengaraju - Introduction to Bayesian Networks

    90 Mins
    Workshop
    Advanced

    { This is a handson workshop . The use case is Traffic analysis . }

    Most machine learning models assume independent and identically distributed (i.i.d) data. Graphical models can capture almost arbitrarily rich dependency structures between variables. They encode conditional independence structure with graphs. Bayesian network, a type of graphical model describes a probability distribution among all variables by putting edges between the variable nodes, wherein edges represent the conditional probability factor in the factorized probability distribution. Thus Bayesian Networks provide a compact representation for dealing with uncertainty using an underlying graphical structure and the probability theory. These models have a variety of applications such as medical diagnosis, biomonitoring, image processing, turbo codes, information retrieval, document classification, gene regulatory networks, etc. amongst many others. These models are interpretable as they are able to capture the causal relationships between different features .They can work efficiently with small data and also deal with missing data which gives it more power than conventional machine learning and deep learning models.

    In this session, we will discuss concepts of conditional independence, d- separation , Hammersley Clifford theorem , Bayes theorem, Expectation Maximization and Variable Elimination. There will be a code walk through of simple case study.