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2022 Academic Internship in

Medical Text Analysis (MTA)

with

Natural Language Processing (MTA-NLP)

9/2021 – 9/2022

INTERNSHIPS in LIFE SCIENCES, AI, Machine Learning, Natural Language Processing

on LPBI’s English Text in Medicine, Biological Sciences and Drug Discovery 

Intended for STEM Students at ALL education level

tasks adjusted per academics

12th Grade CODING Students

College Students in the Life Sciences

PostDocs in Biological Sciences

CONTACT:

 avivalev-ari@alum.berkeley.edu

Internship Description

Medical Text Analysis (Deep Learning NLP)

Research Internship

Medical Text Analysis (MTA) with Deep Learning Natural Language Processing (DL-NLP)

 

Employer Partner: Leaders in Pharmaceutical Business Intelligence (LPBI) Group. Certification is Tuition FREE. Internship is on voluntary Basis. Mentorship done by scientists. Certifications are Tuition FREE and on voluntary basis.

Career(s)

Life Science Research, Bioinformatics, AI, Machine Learning, Statistical NLP, semantic Text Analysis, big Text Data management, Data Science – Visualization

Overview

LPBI Internships offer the following:

  • Affiliation with and mentorship by esteemed scientists and having as peers other research graduate students.
  • Skills development in NLP, ML, AI applications to Medical Text Analysis and potential implications for Drug Discovery and medical claims adjudication
  • References and Letter of Recommendation (LOR)
  • Description of accomplishments and goals achieved during the internship for CV and Resume URL on our website
  • Opportunity to contribute to publications at PharmaceuticalIntelligence.com
  • Advice on exploration of opportunities in life sciences in the US
  • Opportunity to collaborate with professionals from various fields such as medicine and Natural Language Processing as well as with other interns.

Mentors

Aviva Lev-Ari, Ph.D., RN – Director & Founder

Stephen J. Williams, Ph.D. – Chief Scientific Officer

Internship Description

This research internship introduces students to medical text analysis using AI, Machine Learning and (NLP).  Students are introduced to an opportunity to learn about curating cutting edge medical articles, learn about methodologies of data curation and data annotation while using software applications of Natural Language Processing for Medical Text Analysis.

Check Out for Updates to WORKFLOW for a Ten-Steps Medical Text Analysis

WORKFLOW for a Ten-Steps Medical Text Analysis Operation using NLP on LPBI Medical and Life Sciences Content

https://pharmaceuticalintelligence.com/2021/07/15/workflow-for-a-ten-steps-medical-text-analysis-operation-using-nlp-on-lpbi-medical-and-life-sciences-content/

STEP 1:  Domain Knowledge Expert Specifies the selection criteria for a collection of articles:

1.1       Curated & authored articles vs scientific reports

1.2       All articles in a chapter in a book, [N = 1,2,3, ..,18] – Total number of Books = 18

1.3       Selection of articles within a research category [N = 1,2,3, ..,730] Journal ontology has 730 domains named, research categories

1.4       Selection of articles within several research categories

STEP 2:  Create .TXT file for each article in the collection – while working on One assigned Book, an article collection is One Chapter in the Book.

STEP 3:  Create one MERGED .TXT File for all the articles in the collection

 

STEP 4:  Use WordItOut.com and .TXT file per article to generate one WordCloud per article

4.1       Edit Graph – remove connective words, i.e., more, such, or, and, some, the, a,

4.2       Upload WordClouds to the Journal’s media gallery and record article title as legend and source for the graph, add your name as image producer and date

4.3       Insert WordCloud in the article below the author/curator’s name

4.4       Place WordCloud in a one PowerPoint presentation for the entire article collection. Each Chapter in a book will have one PowerPoint file with all the visuals generated in all the steps

STEP 5:  Use .TXT file per article to create a Bar Diagram for the keyword frequencies in the article

5.1       Edit bar diagram and remove connective words, see 4.1, above

5.2       Place each bar diagram in the PowerPoint presentation for the article collection

STEP 6:   Use the one MERGED .TXT file to create ONE Hyper-graph Version 1 plot for the entire article collection (all articles in one chapter) – See Instruction on using Wolfram Hypergraph – Links to DropBox will be given following Onboarding

6.1       Edit hyper-graph

6.2       Place hyper-graph in the PowerPoint presentation

STEP 7:   Use the one MERGED .TXT file to create ONE Tree Diagram plot for the entire article collection

6.1       Edit tree diagram and split into three pages

6.2       Place tree diagram in the PowerPoint presentation

STEP 8:   Transfer all visualization in PowerPoint into a Folder named: Domain Knowledge Expert Interpretation

STEP 9:   The highest value-added step: Domain Knowledge Expert generates a .DOCX file with his expert interpretation of all the Insights drawn from the visualization artifacts generated by NLP, ML, AI when all the insights are put together for analysis and synthesis.

9.1       What are the clinical implications for patient treatment?

9.2       What are the clinical insights for drug discovery for Big Pharma?

9.4       Are there clues for risk adjustment and policy writing tips for health care insurers?

9.5       Store the Expert interpretation into the Interpretation Folder

STEP 10:  Transfer copy of interpretations files for translation into foreign languages: Spanish, Japanese, Russian into folders with language name

STEP 11:  Under Construction: Enrichment of the original content with external repositories

 

Types of Students Desired

STEM students, 12th grade coding students; college students majoring in life sciences, computer science majors

 

Structure/Schedule

  • Cohort/Individual
  • Summer and academic school year internships
  • 16 weeks – (flexible schedule)
  • Meet 1 time per week with supervisor
  • Meet 1 time during internship with group [if the engagement is for one year, then meet once a quarter]
  • Some additional group meetings related to code review, new code instructions, etc.

 

Internship Project Work Examples

  • Sample project: Review articles from various medical fields and extract specific information from articles on cancer. * The new article curation of the content is done by Expert, Author, Writers [EAWs]
  • Sample project: Curated Natural Language Processing resources [See steps 1-7, above] by production of visual products, i.e., WordClouds graphs, Bar Diagrams figures, Hypergraphs plots, Tree Diagram plots for use in shaping a proof-of-concept pilot project to be used in LPBI Group’s business plan.

Skills Used/Gained

  • Machine Learning (ML)
  • Artificial Intelligence (AI)
  • Applications to medical text analysis
  • Article Classification
  • Creation of Text File Format
  • Merging Text File Formats
  • Creation of WordClouds graphs
  • Embedding WordClouds in original articles – master WordPress.com Editor’s DashBoard
  • Creation of bar diagrams figures
  • Generate hyper-graph plots from merged files
  • Create tree diagram plots from merged files
  • PowerPoint presentation skills
  • File management in Dropbox

* Other articles on genomics, cardiovascular, immunology, infectious diseases, metabolomics, precision medicine and reproductive genomics are available

Certification Example

Yash_Choudhary_LPBI-MTA-DL-NLP-Certificate

UPDATED on 9/22/2021

INSTRUCTIONS TO ALL INTERNS

on Deep Learning NLP for Steps 4,5,6,7, in WORKFLOW for a Ten-Steps Medical Text Analysis Operation using NLP on LPBI Medical and Life Sciences Content 

Leaders in Pharmaceutical Business Intelligence (LPBI) Group

https://pharmaceuticalintelligence.com/2021/07/15/workflow-for-a-ten-steps-medical-text-analysis-operation-using-nlp-on-lpbi-medical-and-life-sciences-content/

DEEP LEARNING NLP @ LPBI GROUP

MEDICAL TEXT ANALYSIS

Madison had completed the INSTRUCTIONS on 9/12 at 5PM

THIS IS AN EXAMPLE FOR WHAT EACH INTERN on the Medical Text Analysis with NLP

  • Needs to produce for each Chapter in the Book they are assigned

The following powerpoint linked here (https://docs.google.com/presentation/d/14qbyKh-HFBTAHvgwOaJTIOfDc59rarQB149ICRIgitk/edit#slide=id.gea73ad4e78_0_31) contains the 21 articles in Cancer.  I completely agree with you that the interns need to be able to change the names of each diagram to match what work they are doing (I believe you mentioned Immunology as an example), and that is addressed below.

THESE ARE the INSTRUCTIONS – How to generate the GRAPHICAL OUTPUT of 6 algorithms

  • Please all review and send us feedback

The link here https://docs.google.com/document/d/10U_O36zeHQgq6MGU_Nsbz4FYXjKkbSWdCXkJALp-Ymk/edit#heading=h.k9zii87j8i61contains the finalized Instructions for the Interns.  It covers six algorithms: 25 KeyWord Extraction, Hypergraphs, Tree Diagrams (all 3 of which are Yash’s code), Bar Diagrams, and Word Clouds.  Some of the algorithms require you to have a title in the diagram, and I highlighted the parts of the code where the Interns need to change the text so that the title reflects the work/textbook that they are doing.

UPDATED on 8/23/2021

IMPORT AI

Import AI 263: Foundation models; Amazon improves Alexa; My Little Pony GPT.

Amazon makes Alexa sound more convincing:
…A grab bag of techniques to make synthetic voices sound more realistic…
Amazon has published a research paper about some of the techniques it’s using to make more convincing text-to-speech systems. By using a variety of tools, the company was able to improve the quality of its synthetic voices by 39% relative to a baseline system.

What they did: They use a variety of techniques, ranging from a state-of-the-art sequence-to-sequence model to encode the acoustics, to using a parallel-Wavenert implementation for the ‘neural vocoder’ which fits the text to speech.
Adversarial training – they also use a GAN approach to further improve quality, training a generator network via the acoustic model, then using a discriminator to force the generation of more real-sounding samples.
Read moreEnhancing audio quality for expressive Neural Text-to-Speech (arXiv).

####################################################

Stanford University: Now that big models are here, what do we do about them?
…New research paper and workshop tries to lay out the issues of GPT-3, BERT, and so on…
In recent years, a new class of highly capable, broad utility AI model has emerged. These models vary in modalities and purposes, and include things like GPT-3 (text analysis and generation), BERT (a fundamental input into new search engines), CLIP (combined text and image model), and more. These models are typified by being trained on very large datasets, then being used for a broad range of purposes, many of which aren’t anticipated by their developers.
Now, researchers with Stanford University have published a large research paper on the challenges posed by these models – it’s worth skimming the 100+ paper, and it does a good job of summarizing the different impacts of these models in different areas, ranging from healthcare to robotics. It also tackles core issues, like dataset creation, environmental efficiency, compute usage, and more. Stanford is also hosting a workshop on these models this week, and I’ll be giving a talk where I try to lay out some of the issues, particularly those relating to the centralization of resources and power.

Why this matters: I mostly think ‘foundation models’ matter insofar as they’re bound up with the broader industrialization of AI – foundation models are what you get when you’ve built a bunch of systems that can dump a large amount of resources into the development of your model (where resource = compute, data, training time, human engineering time, etc). Some people dislike foundation models because of how they interact with existing power structures. I think foundation models tell us that there are very significant power asymmetries in AI development and we should pay attention to them and try to increase the number of actors that can work on them. I’ll be giving a keynote about these ideas at the workshop – comments welcome!
Read more about the workshop here:Workshop on Foundation Models (Stanford).
Read the paper hereOn the Opportunities and Risks of Foundation Models (arXiv).

SOURCE

https://jack-clark.net/

UPDATED on 8/16/2021

Proof-of-Concept yielded LPBI’s SOPs on Natural Language Processing – How we will use Deep Learning NOT Statistical NLP !!!!!!!!

  • Yash’s code.nb
  • Deep learning methods
  • 25 keywords
  • Code file: code.nb
  • Hypergraph1.pdf
  • Treeplot.pdf

UPDATED on 8/1/2021

PERSONAL PAGES OF INTERNS

All milestones are recorded on the Personal Pages

Madison Davis, Research Assistant 1 – PERSONAL PAGE on 2021 Medical Text Analysis

Danielle Smolyar, Research Assistant 3 – PERSONAL PAGE on 2021 Medical Text Analysis

Inactive since 2Q 2021

Adina Hazan, PhD, PostDoc in Pharmaceutical Sciences, Medical Text Analysis with Machine Learning 

Inactive since 3Q 2021

Amandeep Kaur, BSc., MSc. (exp. 5/2021) – Research Associate 2, Medical Text Analysis and IT Infrastructure Python Coder

Inactive since 3Q 2021

Premalata Pati, PhD, PostDoc in Pharmaceutical Sciences, Medical Text Analysis with Machine Learning

Yash Choudhary, Research Assistant 3 – PERSONAL PAGE on 2021 Medical Text Analysis

Vaishnavee R. Joshi, BSc, MSc, Research Associate 3, Medical Text Analysis using NLP and Synthetic Biology – PERSONAL PAGE

Komal Ingle, BSc, MSc, Research Associate 3, Medical Text Analysis using NLP and Synthetic Biology – PERSONAL PAGE

Satwik Sunnam, Research Assistant 3 – This is your PERSONAL PAGE

LPBI Group will run FOUR MAJOR INTERNSHIPS: Volunteer base ALL Certificates are Tuition FREE

  • We Offer esteemed Affiliation, Mentorship by Scientists
  • NEW skills development in NLP, ML, AI applications to Medical Text and Drug Discovery
  • References and Resume paragraph on accomplishments and goals during the INTERNSHIP
  • Opportunity to contribute to Publications
  • Explorations of opportunities in Life Sciences in the US

LPBI Group runs FOUR ACADEMIC INTERNSHIPS: Volunteer base

Certificate One Year

  • TWO INTERNSHIPS in LIFE SCIENCES, Medicine, Biological Sciences, Drug Discovery

#1: Text Analysis [Medical] with Wolfram: Natural Language Processing with a NEW cohort of INTERNS and our Summer Interns that will choose to stay

#2: Synthetic Biology Software for Drug Design in Glycobiology for our Joint Venture with ABI-LAB, with Dr. Raphael Nir, PhD, President & CSO

#3: ONE INTERNSHIP is in  Journalism in Pharmaceutical MEDIA & Web Design and Marketing Communications 

#4: ONE INTERNSHIPS is in IS, IT, CS and DATA SCIENCE

IT Project and Administration of IP Asset Classes: Journal, Books, e-Proceedings & Tweet Collections and GALLERY of +6,200 Biological Images

BLOCKCHAIN Architecture design: Interface for NLP. The powerful aspect is hydrating a knowledge graph with NLP inferences so that they can be used for DISCOVERY. Storing those inferences on a blockchain have the value of committing the NLP inferences to a blockchain for purposes of provenance. However, the very important part is The INFRASTRUCTURE behind BurstIQ which is NOT JUST A BLOCKCHAIN [the voice of Erich G.]

UPDATED on 7/28/2021

This is an EXAMPLE of an LPBI Book in the new Book Architecture format

Genomics Volume 2 will be RE-Published in a new Format having the following three parts:

Part A:        Bi-Lingual electronic Table of Contents (eTOCs)

Spanish Translation by Montero Language Services, Madrid, Spain

https://www.montero-ls.com/en/

  • Under consideration: Spanish Audio Podcast of the Spanish eTOCs

 

Part B:        Ten-Steps Medical Text Analysis Workflow Operation using NLP on the Original Text in

Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS & BioInformatics, Simulations and the Genome Ontology

https://www.amazon.com/dp/B08385KF87

NLP performed by Madison Davis 

https://pharmaceuticalintelligence.com/contributors-biographies/research-assistants/madison-davis-research-assistant-1-text-analysis-initiative/

Interpretation of NLP results: English Text by Domain Knowledge Expert: Dr. Stephen J. Williams

  • Under consideration: Spanish Podcast and other Languages forthcoming

Part C:        Text and Podcast of e-Book’s Editorials – the English Text in

https://www.amazon.com/dp/B08385KF87

Preface and Introduction to Genomics Volume 2: Voices of Aviva Lev-Ari & Stephen Williams

  • Introduction to Part 1: NGS – Voice of Dr. Williams
  • Summary to Part 1: NGS – Voice of Dr. Williams
  • Introduction to Part 2: CRISPR – Voice of Dr. Williams
  • Summary to Part 2: CRISPR – Voice of Dr. Williams
  • Introduction to Part 3: AI in Medicine – Voice of Aviva Lev-Ari and Dr. Williams
  • 3.5 on Machine Learning Algorithms in Medicine by Dr. Dror Nir
  • Summary to Part 3: AI in Medicine – Voice of Aviva Lev-Ari and Dr. Williams
  • Introduction to Part 4: Single Cell Genomics – Voice of Dr. Williams
  • Summary to Part 4: Single Cell Genomics – Voice of Dr. Williams
  • Introduction to Part 5: Genomics Modeling in Evolution – Voice of Professor Feldman
  • Summary to Part 5: Genomics Modeling in Evolution – Voice of Professor Feldman
  • Introduction to Part 6: Simulation Modeling – Voice of Dr. Williams
  • Summary to Part 6: Simulation Modeling – Voice of Dr.  Williams
  • Introduction to Part 7: Applications of Genomics: Genotypes, Phenotypes and Complex Diseases – Voice of Dr. Williams
  • Summary to Part 7: Applications of Genomics: Genotypes, Phenotypes and Complex Diseases – Voice of Dr. Williams
  • Introduction to Part 8: Epigenomics and Genomic Regulation – Voice of Dr. Williams
  • Summary to Part 8: Epigenomics and Genomic Regulation – Voice of Dr. Williams

Summary to Volume 2 – Voice of Aviva Lev-Ari and Professor Williams  

Epilogue – Voice of Aviva Lev-Ari and Professor Williams

 

UPDATED on 7/27/2021

Genomics Volume 2 – Parts 1,2,3,4  were completed by Madison

PENDING REDO Hyper-graphs and Tree Diagrams

Genomics Volume 2 – Parts 5,6,7,8 assigned to Madison

Cancer Volume 1 – Completed by Ms. Danielle

Chapters 1 – 6

PENDING Hyper-graphs and Tree Diagrams

Cancer Volume 1 – Completed by Dr. Pati

Chapters 7 – 12

PENDING Hyper-graphs and Tree Diagrams

  

Ten-Steps NLP Protocol is assigned as Entire Books

 

Genomics Volume 1:

  • Data Preparation for Chapters: 1,2,3,4,5 & 21 – ASSIGNED To Ms. Amandeep Kaur – Completed
  • Data Preparation for Chapters: 6,7,8,9,10,11,12,13,14,15,16,17,18,19,20 – Satwik Sunnam
  • DL-NLP – Satwik Sunnam on entire Book, Chapters 1 – 21

Genomics Volume 2:

  • Data Preparation Parts: 1 – 8 – Madison Davis
  • DL-NLP – Madison Davis

Cancer Volume 1: Chapters 1-6

  • Data preparation – Dr. Premalata Pati – completed
  • DL-NLP – Satwik Sunnam

Cancer Volume 1: Chapters 7-12

  • Data preparation – Danielle Smolyar
  • DL-NLP – Danielle Smolyar

Cancer Volume 2 – ASSIGNED To Ms. Ingle – Chapters 1-10

Cancer Volume 2 – ASSIGNMENT TBA – Chapters 11-20

Cardiovascular Volume 1 – ASSIGNED To YASH – Series A, Volume 1

3D BioPrinting – ASSIGNED To Ms. Joshi – Series E, Volume 4

PENDING Assignments

Series A: Volumes 2,3,4,5,6

Series D: Four Volumes

Series E: Volumes 1,2,3

UPDATED on 7/24/2021

All Interns: Please review

WORKFLOW for a Ten-Steps Medical Text Analysis Operation using NLP on LPBI Medical and Life Sciences Content

Author: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2021/07/15/workflow-for-a-ten-steps-medical-text-analysis-operation-using-nlp-on-lpbi-medical-and-life-sciences-content/

UPDATED on 7/14/2021

We will schedule a Zoom on the following topics:

• All Summer Interns and
• All One Year Academic Training Interns

1. How to access .TXT files in DropBox
2. How to access Wolfram Code written by Madison
3. Yash needs access to Adina’s .TXT files inside the CARDIOVASCULAR Folder
4. Yash needs to WRITE Wolfram code to generate Hyper-graph and Tree Diagram for LPBI’s Cardiovascular 🫀 Proof-of-Concept
• Aviva will present Adina’s PPT with Yash’s two new graphs on TBA meeting with the Insurer on Cardiovascular

5. Amandeep will use Yash code to generate Hyper-graphs and Tree Diagrams one per Chapter in Genomic Volume 1.
• Amandeep will create WordClouds and Bar Diagrams for all articles in Genomics Volume 1 per Madison’s Wolfram’s Code

6. Danielle will do five above for Cancer Volume 1: Chapters 1-6

7. Dr. Pati will do five above for Cancer Volume 1: Chapters 7-12

8. Aviva needs Summer Interns to consider
• One year Academic Training in NLP, ML, AI at 10 hours per week a commitment doing Medical Text Analysis on LPBI’s own content

8.1 take over Genomics Volume 2:
if Madison does not return in Fall:

8.1.1 Redo Hyper-graphs and Tree Diagrams for Madison’s COMPLETED, Parts 1,2,3,4 – using Yash’s code
8.1.2 Produce WordClouds, Bar diagrams per article for Genomics Volume 2: Parts 5,6,7,8
8.1.3 Produce Hyper-graphs and Tree Diagrams one per Chapter for Genomics Volume 2: Parts 5,6,7,8

9. Aviva needs Interns for Cancer Volume 2

10. Aviva needs Interns for Cardiovascular

10.1 Proof-of-concept using 13 articles on Calcium

10.1.1 WordClouds DONE by Adina
10.1.1 to do 13 Bar Diagrams using Madison’s Wolfram code
10.1.2 to do One Hyper-graph and One Tree Diagram for these 13 articles using Yash code

10.2 Move from Proof-of-concept to
Series A: Cardiovascular, Volume 1

10.2.1 Perform
• LPBI 6 Phase Protocol for Medical Text Analysis with NLP on Volume 1

10.2.2 Scaling up 10.2.1 to Volumes 2,3,4,5,6

10.3 Perform 10.2.1 on Series D: Four Volumes on Metabolomics, Immunology, Infectious diseases and Reproduction Genomics

10.4 Perform 10.2.1 on Series E: Four Volumes on Precision Medicine

LINKS in use:

LPBI Text Analysis Comprehensive Tutorial URL:

https://docs.google.com/document/d/1NrRG0FgRxa9oUbTl6wEIrIUhk66aIs6vdBFowr06L6g/edit?usp=sharing

LPBI Text Analysis PowerPoint Visualizations URL:

https://docs.google.com/presentation/d/15hfyW_4u7bbDmrPlpfKhEKG0fgrump3lJjstOMyAjEw/edit?usp=sharing

IRINA created DROPBOX Folder for each Intern – each person’s name

https://www.dropbox.com/sh/vfkqxsj56uqpw3s/AADskZtMurRsEzz4OK-SXWu4a?dl=0

MADISON created a TEMPLATE for all INTERNS to use

https://drive.google.com/drive/folders/1tihvZa_hmefJadr7Qelc1DCn626IdLM7

2021 Academic Internship in

Medical Text Analysis (MTA)

with

Natural Language Processing (MTA-NLP)

9/2020 – 9/2021

INTERNSHIPS in LIFE SCIENCES,

Medicine, Biological Sciences, Drug Discovery 

Intended for STEM Students at ALL education level – tasks adjusted per academics

12th Grade CODING Students

College Students in the Life Sciences

PostDocs in Biological Sciences

CONTACT:

 avivalev-ari@alum.berkeley.edu

UPDATED on 3/30/2021

This AI Can Generate Convincing Text—and Anyone Can Use It

The makers of EleutherAI hope it will be an open source alternative to GPT-3, the well-known language program from OpenAI.

“There is tremendous excitement right now for open source NLP and for producing useful models outside of big tech companies.” says Alexander Rush, a computer science professor at Cornell University, referring to a subfield of AI known as natural language processing that’s focused on helping machines use language. “There is something akin to an NLP space race going on.”

If that’s the case, then GPT-3 might be considered the field’s Sputnik. GPT-3 consists of an enormous artificial neural network that was fed many billions of words of text scraped from the web. GPT-3 can be startlingly eloquent and articulate, although it can also spurt out gibberish and offensive statements. Dozens of research groups and companies are seeking ways to make use of the technology.

The code for GPT-3 has not been released, but the few dozen researchers behind Eleuther, who come from across academia and industry, are drawing on papers that describe how it works.

Rush, who isn’t affiliated with Eleuther, says the project is one of the most impressive of a growing number of open source efforts in NLP. Besides releasing powerful language algorithms modeled after GPT-3, he says the Eleuther team has curated and released a high-quality text data set known as the Pile for training NLP algorithms.

The Eleuther project makes use of distributed computing resources, donated by cloud company CoreWeave as well as Google, through the TensorFlow Research Cloud, an initiative that makes spare computer power available, according to members of the project. To ease access to computer power, the Eleuther team created a way to split AI computations across multiple machines. But it isn’t clear how the computational requirements might be met if the project continues to grow.

OpenAI is betting that GPT-3 can be commercialized. In July 2019, OpenAI received a $1 billion investment from Microsoft, which a year later got exclusive rights to license GPT-3. OpenAI says that over 300 GPT-3 projects are in the works, using a limited-access API. These include a tool for drawing insights from customer feedback, a system that auto-generates emails from bullet points, and never-ending text-based adventure games. Eleuther might make it easier to build similar tools without access to the GPT-3 API.

SOURCE

UPDATED on 3/28/2021

For 4/15/2021 at 10 AM EST – Meeting with LINGUAMATICS:

  1. Danielle (had confirmed) will present 10, below for Chapter 2 in Cancer Volume 1, means 8 & 9, below on ALL articles
  2. Madison, Please CONFIRM, will present 10, below for 17 Cancer articles provided by Dr. Williams, means 8 & 9, below on ALL articles 
  3. If Madison and Danielle are not available to present THEN Dr. Williams will present
  • All Team Members: Please create ONE Folder for EACH Chapter for the book(s) each is working on
  • This Folder will comprise of the following 14 item 

Following is the workflow for MEDICAL TEXT ANALYSIS to be followed by the Team:

Thanks to Amandeep Kaur for streamlining the workflow, 1-10 from a previous e-mail sent to all

1.  One MS Word file for All Articles in a chapter – Chapter’s subject CONTEXT as UNIT CASE for Text Analysis by NLP

2.  One Text file for All Articles in a chapter – to be used for Wolfram NLP

3.  One MS Word File Containing All the Biological Images with Article Name, Author, URL of All Articles in a chapter – to be use for uploading images to the Gallery of the NEW WEBSITE for 2.0 LPBI [under construction]

4.  One MS Word file per Article  – to be INDEXED for Blockchain Transactions Network

5.  One Text file per Article – to be used for 2, above

6.  One WordCloud per Article – Test Analysis visual #1

7.  One Bar Diagram from Above option 1 (means All Articles) – Test Analysis visual #2

7.1  One Bar Diagram produced from 2, above – Text Analysis visual # 2.1 – Word frequency for Subject Context of the Chapter 

8.  One Wolfram Hyper-graph from Above option 1 (means All Articles) – Test Analysis visual #3

9.  One Tree Diagram from Above option 1 (means All Articles) – Test Analysis visual #4

10.  One PowerPoint Presentation PER CHAPTER containing WordClouds, bar diagrams, hyper-graphs, tree diagrams of i.e., Genomic Vol 1 (means All chapters) – Test Analysis visual #5

11.  Domain Knowledge Expert Interpretations in English Text for 8, above – Test Analysis Interpretation of Hyper-graph in English Text File #6 [Place holder for Dr. Williams]

12.  Domain Knowledge Expert Interpretations in English Text for 9, above – Test Analysis Interpretation of Tree Diagram in English Text File #7 [Place holder for Dr. Williams]

13.  Translation of 11, above [Interpretation of Hyper-graph] into three languages each – Test Analysis Translation Texts

#8, #9, #10 [three place holders]

14.  Translation of 12, above [Interpretation of Tree diagram] into three languages each – Test Analysis Translation Texts #11, #12, #13 [three place holders]

UPDATED on 3/3/2021

     Since 2012, Leaders in Pharmaceutical Business Intelligence Group (“LPBI Group”) is a leading, electronic scientific content-creation venture, offering real-time, original scientific content through advanced platform architecture and curation methodologies applied to content in Medicine, Life Sciences, Health Care and Pharmaceutical. 

The company’s commitment is to synthesize, analyze and interpret complex, medical and scientific disease information through electronic publishing venues via the cloud to advance the knowledge and research efforts of the scientific and business community. LPBI is recognized as an online leader in scientific curation and dissemination, scientific communication, and medical & pharmaceutical information analysis. Over these years the LPBI Group has generated several large databases of biomedical information, highly curated by teams of scientific and medical experts.  These corpuses of knowledge consist of four main sources of biomedical text including:

1) a series of 18 BioMed e-books on cancer, genomics, cardiovascular diseases, metabolomics, immunology, infectious diseases and precision medicine.   

2) an Open Access Online Scientific Journal consisting of +6,000 articles in over 730 categories of research, based on an ontology created by the domain knowledge experts of the LPBI Group 

3) a corpus of over 70 e-Proceedings of leading Global Scientific/Medical Conferences, reported and curated based on a methodology developed by the LPBI Group producing with one click the digital document, aka e-Proceedings of the conferences and Tweet collections digital documents of all the tweets posted during the last 36 events we covered by LPBI members of the team functioning as Press on behalf of the Conferences organizers.

4) a Gallery of 5,100 Biological Images that populate the +6,000 scientific articles. Each Image has a text legend, and often text is typed on the images to designate details portrayed on the image. Images source is in text format as well. 

We are now developing a new platform, based on Blockchain technology, to allow Knowledge Workers to access, use and analyze our extensive structured knowledge databases mentioned in 1,2,3,4, above. 

We are seeking Academic partners who can provide natural language processing advice and capabilities for the process of Medical Text Analysis with NLP and Deep Learning. The visualization and other digital products generated by the NLP process for each article and for collections of articles, I.e., 

A. all articles in a Chapter in a Book, and/or 

B. all or a selection of Top n articles, rank ordered by # of views from all articles in one or more of the 730 categories of research assigned as main category by the author for the article fetched in the search submitted by a Knowledge Worker accessing LPBI Blockchain interface in the digital store of the premier Health Care Digital Marketplace designed and operated by an IT vendor based in Denver, CO. This vendor is designing LPBI’s Digital Store by our specifications, this design involve embedding of NLP in the blockchain API layer(s).

This new platform under design will allow scalability for the ever-increasing knowledge set we are generating. 

  • The text of an existing article is subjected for test analysis with NLP.
  • The process generates additional blocks in the article profile:

(1) WordCloud as Abstract

(2) Bar diagram of word frequencies

(3) hyper-graph(s) and

(4) Tree diagram(s) for A and B, above and

(5) Domain knowledge expert INTERPRETATION of the visualization.

(6) Ability to specify getting this interpretation in English text format and/or getting it in several Foreign languages text.

  • Content monetization take place by filling up a FORM with all the selections for content download options:

(a) the original article,

(b) one or n articles from the article set presented by the Recommendation Engine,

(c) original images and NLP generated visuals

(d) NLP results interpretation by domain knowledge experts

(e) choice of English or several Foreign  languages for the text of these interpretations

  • Features of the Blockchain Transactions Network will include: Permissions, Immutable LEDGER for the payments taking place prior to Permission grants to download contents, Smart contracts, Cybersecurity of the IP

We have successfully beta tested NLP algorithms on small-representative, structured datasets of articles, Text files and  Biological images files. 

  • This proof-of-concept is now expected to compare the results LPBI generated by using Wolfram NLP Language for Biological Sciences vs the results now being produced by LINGUAMATICS, a lead NLP vendor for the Pharma industry.
  • Our team and that vendor are using the same data files we produced for Phase 2 of the Proof-of-Concept project.

Our goal is to provide Knowledge Workers with a one-stop shop for Biomedical information and the means to analyze this information on an as-needed basis, i.e., the Knowledge Worker will be able to specify the sub universe of articles on which NLP algorithms are applied. The Blockchain platform with the embedded NLP will retrieve any configuration of articles and will generate the visualizations for the Text analysis with NLP.

Additional details are included in the following article:

2.0 LPBI is a Very Unique Organization

Author: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2021/03/02/2-0-lpbi-is-a-very-unique-organization/

UPDATED on 2/13/2021

Assignments for Interns updated on 2/13/2021

All the Intern will be using Madison’s code and workflow to generate all Steps of Visualization:

  1. WordClouds
  2. Bar Diagrams
  3. Hyper-graphs
  4. Tree Diagrams
  5. Template for Interpretation to be filled up by domain knowledge expert
  6. Present all in PowerPoint
  7. Populate a Database with 1 to 5, above
  8. Use DropBox for Proof-of-Concept and for all the Books NOW in DropBox

Series B: Frontiers in Genomics Research

Assigned intern: Madison Davis – Proof-of-Concept: 16 articles on Genomics, 12 articles in Cancer Biology & Therapies Research Category & Genomics Volume 2

Assigned intern: Amandeep Kaur – Genomics Volume 1, Chapter 21: CRISPR & Chapters: 1-20 in Genomics Volume 1 

Series C: e-Books on Cancer & Oncology

Assigned intern: Danielle Smolyar – Proof-of-Concept: 16 articles on Cancer, 12 articles in Cancer, the Chapter of Warburg article by Dr. Larry and Cancer Volume 1,  2,400 pages  Chapters 1 to 6

Assigned intern: Premalata Pati, PhD – Cancer Volume 1,  2,400 pages  Chapters  to 7 to 12

Assigned intern: TBA – Cancer Volume 2, 3,400 pages

  • to be shared with TBA Intern(s) or current intern reassigned after completion of their current assignments 

Series A: e-Books on Cardiovascular Diseases

Assigned intern: Adina Hazan, PhD – Proof-of-Concept: 13 articles on Calcium & CVD & e-Books on CVD

Series D: e-Books on BioMedicine – Metabolomics, Immunology, Infectious Diseases, Reproductive Genomic Endocrinology

Assigned interns: TBA

Series E: Patient-Centered Medicine – LINKS to e-Books & Cover Pages for Volumes 1,2,3,4

Assigned interns: TBA

UPDATED on 1/17/2021

Dear Madison,

I visited the google doc LINK on your Personal Page on 2021 Medical Text Analysis.

Please also Place the PowerPoint Presentation on the Google Doc AND please the LINK to it on your PERSONAL PAGE.

Madison Davis, Research Assistant 1 – PERSONAL PAGE on 2021 Medical Text Analysis

I am very proud of you to generate the Tree Diagram for the 16 articles in Genomics.

Please create a Tree Diagram one for each of the 16 articles.

  • I assume that that will provide the words and the edges connected we need to submit to the Domain Knowledge Expert to write an interpretation for.

Please add all the new graphs into the PowerPoint and replace the existing Bar Graphs and Hyper-graphs without the legend

As you will generate 16 Tree diagrams we will compare the hyper-graph with the Tree Diagrams and decide WHICH is the way we go 

  • Next, you need to rest a whole week from LPBI
  • Next, we would like to get a Tree diagram for ONE article in the 16 Genomics articles and 6 articles in the main Research Category assigned to that ONE article
  • Next, Madison goes to Genomics Volume 1 and looks at all the articles in CHAPTER 1 – ONE Tree Diagram for that article collection
  • SEMANTIC ANALYSIS is expected to be the strongest among articles in one chapter in any book among our 18 volumes

For Scaling up Madison will work also on

  • Interpretation of our Domain Knowledge Expert of the Tree Diagram for ALL the articles in Genomics Volume 1, Chapter 1 – will be the one that will bring to completion the Proof-of-Concept for Genomics Contnet

THEN will be ready to call the meeting with the Insurer.

By early March 2021 – We need Madison to write a Summary report on

  • What were the contributions that this internship did you your professional development in the first 6 months – 8/15/2020 – 2/28/2021
  • What are the NEW skills you developed in conjunction with working on the Proof-of-Concept

Dear Danielle, 

  • ALL of the ABOVE – we need be done by Danielle on Cancer Content.
  • You need all articles from CHAPTER 1 in CANCER Volume 1

By early March 2021 – We need Danielle to write a Summary report on

  • What were the contributions that this internship did you your professional development in the first 6 months – 8/15/2020 – 2/28/2021
  • What are the NEW skills you developed in conjunction with working on the Proof-of-Concept

I am very pleased that the progress to date enables me to see the forthcoming COMPLETION of the Proof-of-Concept nearing for Genomics and for Cancer.

Dear Dr. Williams,

  • Please ready yourself for several Tree Diagrams interpretations in Genomics and in Cancer
  • Please EXPLORE HOW can we start another Gallery on WordPress.com for ALL THE IMAGES GENERATED BY 2.0 LPBI
  • Shall we date them 2021 upon Upload by the Creator of the graph? 
  • Then we know they are 2.0 LPBI IP vs 
  • Images uploaded in 2012-2020 which are the 5,100 images, aka 1.0 LPBI IP 

Please ALL – read Madison’s e-mail, below.

THANK YOU

Best regards,

Aviva

Aviva Lev-Ari, PhD, RN

Director & Founder

https://lnkd.in/eEyn69r

From: Madison Davis <madisond2302@gmail.com>

Date: Sunday, January 17, 2021 at 3:16 PM

To: “Aviva Lev-Ari, PhD, RN” <aviva.lev-ari@comcast.net>

Subject: Re: Submitted – See attachment

Hi Aviva,

I made an updated, comprehensive instructions guide, and it has been posted on the 2021 Text Analysis Website.  Danielle and I have updated our personal pages showcasing our code and graphics.

I’ve included in these diagrams and documents Tree Diagrams by Wolfram.  Tree Diagrams are actually fairly similar to Hyper-graphs, and they are visually appealing.  I made an algorithm to showcase all Tree Diagrams for all 16 articles, and I will attach a photo here:

Thanks,

Madison Davis

On Sat, Jan 16, 2021 at 9:45 PM Aviva Lev-Ari <aviva.lev-ari@comcast.net> wrote:

Dear Madison,

I was happy to write for you the recommendation. I wish you best of luck to get into the RSI summer program.

Got word Robin that she will conduct a National search to find interns for LPBI.

We wish to have one or more interns on each volume, we got 18 volumes.

All the Intern will be using your code and work flow to generate:

  1. WordClouds
  2. Bar Diagrams
  3. Hyper-graphs
  4. Template for Interpretation to be filled up by domain knowledge expert
  5. Present all in PowerPoint
  6. Populate a Database  with 1 to 4, above

I suggest that you will post all the instructions on 2021 Medical Text Analysis Portal

The code you wrote you will place on your Personal Page on the Portal

  • Danielle is following your work on Genomics articles on her assigned 16 Cancer articles
  • Adina is starting the Proof-of-Concept on 13 Cardiovascular articles. We have 6 volumes on heart diseases. She will connect you for instructions
  • Amanda is starting a Proof-of-Concept for Chapter 21: CRISPR in Genomics Volume 1 – all articles in this chapter.

She will expand to all articles in the several Research Categories on CRISPR in the ontology of the Journal

  • Inbar will start in 6/2021 a Proof-of-Concept on Series D, she will choose her first volume

All new Interns, each will have a volume,

We are building a Team of 18 interns

Regarding of the good new you have for LPBI:

  • These are beautiful results, in attachment.

We will have a bar diagram for each of the 16 articles.

Please explore “Tree Diagram” and figure out if Wolfram allows to represent the hyper-graph as a Tree Diagram

I would like to suggest to run a hyper-graph and a Tree diagram for one article at a time

We will choose among the two only one after deliberation among the two produced for few articles to become the Standard for 3., above: Hyper-graph

The vectors within one article will be provided for semantic interpretation by the domain knowledge expert.

Try above suggestions and let’s look at the results.

Ask me questions as you feel needed.

Aviva Lev-Ari, PhD, RN

Founder & Director

Leaders of Pharmaceutical Business Intelligence (LPBI) Group, Boston

PharmaceuticalIntelligence.com

AvivaLev-Ari@alum.berkeley.edu

617-755-0451

Twitter: @pharma_BI @AVIVA1950

Profile on LinkedIn

Begin forwarded message:

From: Madison Davis <madisond2302@gmail.com>

Date: January 16, 2021 at 9:46:43 PM EST

To: Aviva Lev-Ari <aviva.lev-ari@comcast.net>

Subject: Re: Submitted – See attachment

To Aviva,

I just want to thank you so much for writing a letter of recommendation for me!  It means a lot to me, and I truly appreciate it.

Good news for the LPBI 2.0 Project: I’ve been able to come up with some automation processes so that the word frequencies can be done for any number of articles now, and the hypergraphs now show connections over all 16 articles.  I will attach some photos and notify Danielle about editing the Cancer documents.

Best,

Madison Davis

UPDATED on 1/16/2021

Instructions for launching a Proof-of-Concept in one of the following five domains:

SEE UPDATE on 2/13/2021, above

Assigned intern: Adina Hazan – Proof-of-Concept: 13 articles on Calcium & CVD & e-Books on CVD

Assigned intern: Madison Davis – Proof-of-Concept: 16 articles on Genomics & Genomics Volume 2

Assigned intern: Amandeep Kaur – Genomics Volume 1, Chapter 21: CRISPR & Genomics Volume 1 to be also shared with Madison Davis

Assigned intern: Danielle Smolyar – Cancer Volume 1 to be shared with other Intern(s) – 2,400 pages

Assigned intern: TBA – Cancer Volume 2 to be shared with other Intern(s) – 3,400 pages

Assigned interns: TBA

Assigned interns: TBA

Steps in the development of a Proof-of-Concept

for one of the five e-Series: A, B, C, D, E

Create a PowerPoint file like this attachment, you will need to get Text been analyzed by NLP 

STEPS in Data Preparation

  1. Create an MS Word file for each of the X articles
  2. Create file X+1 that is ALL of the files into one 
  3. Get from Madison and Danielle HOW they did WordClouds using Wolfram
  4. For WordItOut.com use same criteria as they used a slide in the attachment presents that
  5. Create Bar Chart one per article AFTER you eliminate words as The, an, a, and, 
  6. Hyper-graph creation – use code written by Madison
  7. Communicate with your Domain Knowledge Expert for Interpretation of the NLP results in the format of hyper-graph

UPDATED on 8/20/2020

From: Aviva Lev-Ari <aviva.lev-ari@comcast.net>

Date: Thursday, August 20, 2020 at 6:39 AM

To: Madison Davis <madisond2302@gmail.com>

Cc: “Stephen Williams, PhD” <sjwilliamspa@comcast.net>, “Ofer Markman, PhD” <oferm2020@gmail.com>, “Irina Robu, PhD” <irina.stefania@gmail.com>

Subject: Re: Internship Progress

Very impressive.

Let me know 

1. When I can edit your first post

2. What do you suspect could have been behind the Microsoft Decline decision

3. I highly appreciate that fact that you reapply !!!

Note: when our Summer Intern Daniel had applied to a Developer Account for Twitter.com to use Tweepy and Pyton, we had three exchanges with Twitter Team till we were approved a Developer Account.

4. Uploading the Text into container is Step 1.

The next step would be to activate the NLP algorithm on the Text and generate Hypergraphs and other output that the NLP program generate

5. We need to understand Microsoft NLP algorithm, I.e., is driven by frequency of words or by a correlation matrix of inverse distances or my semantics logic predicates

6. Example:

23andMe – sequence a Genome of one Person at a time

6.1 it used algorithm x for sequencing (Illumina)

6.2 It compares it to a benchmark

6.3 It issue a report that has

6.3.1 text 

6.3.2 Geographic Map of ancestry

6.3.3 Graphics on genes

6.3.4 List of genes over expressed posing Risk for Diseases x, y, z and protection again Disease k, l, m

I assume that AZURE API for NLP algorithms when apply on text for FHRI Service will yield a report like 6.3, above.

Such a report will be interpreted by LPBI experts.

It will be very nice if you would create the following:

GENOMICS:

16 articles

For each article:

1. Create a MS Word Text file (no figures)

2. Place figures in another file assigned the articleID images removed from

3. Repeat 1 & 2, above for all 16 articles

Top 12 viewed articles:

4. Perform 1,2,3 above for each of the 12 – Expect for Our Team, this, N= 11

Create Two containers:

• One for Genomics

• One for Top Viewed articles

• One for Cancer – Danielle will perform 1,2,3, above for 16 articles on Cancer, you will write a Standard Operation Procedure for HOW to perform 1,23, above for any collection of articles from the Journal. For each article we need to have one column of Categories of Research that any article had been assigned to by the Author, that list is in the Table of the Collection of 16 articles in Genomics, Cancer and Top Viewed articles.

5. We need a DESCRIPTION File for AZURE’s NLP algorithm

6. We need for 5, above same information as 6.3, above.

A DESCRIPTION File about the output of AZURE’s NLP algorithm

Please ask questions 

It is OK to contact AZURE Customer Service and discuss what is your goal and get a Microsoft representative to walk you through the steps to follow to achieve the OUTPUT of application of NLP to our Article’s Text.

• Daniel, mentioned, above had contacted WordPress.com to clarify if a count of Views of our Home Page include or not click done from the Dashboard of an LPBI team member vs a count of a view does not include Internal vs External IP Address of Website Access.

•• That clarification involved 6 e-mail exchanges between Daniel and WordPress.com (WP) representative.

••• that exchange is included in the LPBI Documentation Appendix as evidence for WP definition of a “View” of an article on our Site

•••• Any Click on an Article is assign the intent to download and agreement to pay $30. 

••••• Any Click on the Homepage is assign the intent to access for a Search of a Private Archive and agreement to pay $5.

We derive the Valuation of the Journal based on # of Views of each article across 6,000 articles and across 687,000 searches on the Homepage times the two price categories above for 1.8 Million for both. The Cumulative projection to 2025 is of $48 Million.

The ONE GRAPH I sent last evening.

I mention the WP e-mail exchange to encourage you to contact an AZURE representative to get your guidance on the process and maintain full documentation which we will in due time VOPY and PASTE as the instructions apply to achieve the 6.3, above LIKE a report for our TEXT ANALYSIS operation.

Any questions, please e-mail me or coordinate availability for Zoom meeting with and any other Mentor at LPBI.

A. I am aware about School year start.

B. As you mentioned, time management will allow you to pursue that Annual Internship which will develop your 

1. experience on technical domains and on 

2. corporate/enterprise environment 

I am very pleased to watch you grow and excel with our Team.

BACKGROUND

2020 VISION 

https://pharmaceuticalintelligence.com/vision/

2020 VISTA 

https://pharmaceuticalintelligence.com/2019-vista/

BioMed e-Series 

https://pharmaceuticalintelligence.com/biomed-e-books/

Artificial Intelligence in Cancer & Genomics Portal 

https://pharmaceuticalintelligence.com/ai-in-genomics-cancer/

2020 Summer Internship on Data Curation & Data Annotation 

https://pharmaceuticalintelligence.com/2020-summer-internship/

Testimonials 

https://pharmaceuticalintelligence.com/praising-lpbi/

Founder 

https://pharmaceuticalintelligence.com/founder/

Research Assistants 

https://pharmaceuticalintelligence.com/contributors-biographies/research-assistants/

Overarching Plan: 9/2020 – 9/2021

 The Overarching Plan will be updated with a Date and will GUIDE all INTERNS activities

The New Strategies: TNS #1, #2, #3, #4, #5, #6

TNS #1TEXT Analysis on our CONTENTS: NLP, ML, AI – INSIGHTS MEAN VALUE and Up-selling content = NEW WARES to be brought to the market

Phase 1: Natural Language Processing(NLP) – 24 months FREE Microsoft AZURE for Health

(1) Top 12 articles by views (all domains)

(2) Sixteen Cancer Articles representing 8 topics in Cancer Volume 1

(3) Sixteen Genomics Articles representing 21 chapters in Genomics Volume 1

Phase 2: Piloting the Proof of Concept on 4 of LPBI BioMed 16 volumes:

Cancer, Volume 1 & 2 and Genomics, Volume 1 & 2

Series C: e-Books on Cancer & Oncology
Volume 1:
Cancer Biology and Genomics for Disease Diagnosis
http://www.amazon.com/dp/B013RVYR2K 
Volume 2:
Cancer Therapies: Metabolic, Genomics, Interventional, Immunotherapy and Nanotechnology in Therapy Delivery
http://www.amazon.com/dp/B071VQ6YYK 

and

Series B: Frontiers in Genomics Research
Volume 1: 
Genomics Orientations for Personalized Medicine
http://www.amazon.com/dp/B018DHBUO6
Volume 2:
Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS & BioInformatics, Simulations and the Genome Ontology
https://www.amazon.com/dp/B08385KF87

Phase 3: Scaling up NLP, ML, AI to LPBI Group’s FOUR Corpuses Using LPBI’s Vendor of Choice:

LPBI Group’s FOUR Corpuses

(1) All 6,000 Journal Articles 

(2) 16 Books in Medicine 

(3) 60 e-Proceedings & 36 Tweet Collections of TOP Global Medical and Biotech Conferences, 2013 – 2020

(4) Content Segmentation by other criteria for Narrow casting the content of the journal ontology, Categories of Research (N = 715). About 70% are related to Drug DIscovery

More Instructions

  1. The 2020 Summer Internship YIELDED the following three Deliverables which will serve as INPUT for the PROOF OF CONCEPT that the INTERNS in LPBI Group’s 2020/2021 Academic Internship in Medical Text Analysis with Natural Language Processing (MTA-NLP) will be using
  2. GOAL for 2020 4Q: TASK #1
    Launch and Completion of the Proof of Concept – and its presentation to a TOP HealthCate Insurer in UT and to a Healthcare Blockchain Transaction Network
  3. TASK #2: 2021 – GOAL: Upon completion of the Proof of Concept —>>>> We will scale up the MTA-NLP operation to our following 4 Volumes: (a) Cancer: Volumes 1 & 2 and (b) Genomics: Volumes 1 & 2 – – See Overarching PLAN, above for updates
  4. TASK #3: Upon completion of TASK #2, we will engage in task expansion and Scaling up NLP, ML, AI to LPBI Group’s FOUR Corpuses: – See Overarching PLAN, above for updates

(1) All 6,000 Journal Articles 

(2) 16 Books in Medicine 

(3) 60 e-Proceedings & 36 Tweet Collections of TOP Global Medical and Biotech Conferences, 2013 – 2020

(4) Content Segmentation by other criteria for Narrow casting the content of the journal ontology, Categories of Research (N = 715). About 70% are related to Drug DIscovery

  • DELIVERABLES produced by INTERNS during LPBI Group’s 2020 Summer Internship on Data Curation & Data Annotation SERVE AS INPUT for

    2020/2021 Academic Internship in

    Medical Text Analysis (MTA)

    with

    Natural Language Processing (MTA-NLP)

TASK #1:

USE ONE of the Three following INPUTS to be used in building the Proof of Concept described in

https://pharmaceuticalintelligence.com/2021-medical-text-analysis-nlp/

Three INPUTS to be used in building the Proof of Concept

WordCloud Visualization of LPBI’s Top Twelve Articles by Views at All Time and their Research Categories in the Ontology of PharmaceuticalIntelligence.com

Curators: Daniel Menzin, Noam Steiner-Tomer, Zach Day, Ofer Markman, PhD, Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2020/07/21/wordcloud-visualization-of-lpbis-top-twelve-articles-by-views-at-all-time-and-their-research-categories-in-the-ontology-of-pharmaceuticalintelligence-com/

 

WordCloud Visualization of LPBI’s Top Sixteen Articles on CANCER in eight categories and by Views at All Time and their Research Categories in the Ontology of PharmaceuticalIntelligence.com

Curator: Stephen J. Williams, PhD and WordCloud Producers: Daniel Menzin, Noam Steiner-Tomer, Zach Day, Ofer Markman, PhD and Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2020/07/30/wordcloud-visualization-of-lpbis-top-sixteen-articles-on-cancer-in-eight-categories-and-by-views-at-all-time-and-their-research-categories-in-the-ontology-of-pharmaceuticalintelligence-com/

 

WordCloud Visualization of LPBI’s Top Sixteen Articles on GENOMICS by Views at All Time and their Research Categories in the Ontology of PharmaceuticalIntelligence.com

Curators: Stephen J. Williams, Aviva Lev-Ari, PhD, RN and WordCloud Producers: Daniel Menzin, Noam Steiner-Tomer, Zach Day, Ofer Markman, PhD

https://pharmaceuticalintelligence.com/2020/07/30/wordcloud-visualization-of-lpbis-top-sixteen-articles-on-genomics-by-views-at-all-time-and-their-research-categories-in-the-ontology-of-pharmaceuticalintelligence-com/

TASK #2:

Expand applications of Microsoft AZURE to – See Overarching PLAN, above for updates

Series C: e-Books on Cancer & Oncology

Volume 1:

Cancer Biology and Genomics for Disease Diagnosis

http://www.amazon.com/dp/B013RVYR2K 

Volume 2:

Cancer Therapies: Metabolic, Genomics, Interventional, Immunotherapy and Nanotechnology in Therapy Delivery

http://www.amazon.com/dp/B071VQ6YYK 

and

Series B: Frontiers in Genomics Research

Volume 1: 

Genomics Orientations for Personalized Medicine

http://www.amazon.com/dp/B018DHBUO6

Volume 2:

Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS & BioInformatics, Simulations and the Genome Ontology

https://www.amazon.com/dp/B08385KF87

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