Leaders in Pharmaceutical Business Intelligence Group, LLC, Doing Business As LPBI Group, Newton, MA

3.1 Wolfram Code – Example from Genomics, Volume 3 in Spanish Edition

Five comments of note are repeated here from the Original Book and from the New Genre, Genomics Volume 2, PART A & PART C because of their significance:

Genomics, Volume 3 in Spanish Edition

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

https://www.amazon.com/dp/B0BRD8JXTL $155

Comment #1: 

• No other book covers the same topics in a single volume

Comment #2:

• No other book incorporates 74 e-Proceedings created in real time by the Book’s authors and editors at conferences on Genomics

Comment #3:

• No other book incorporates four collections of Tweets representing quotes from speakers at global leading conferences on Genomics

Comment #4:

• No other book has 13 locations of Videos and Audio Podcasts that serve to enrich the e-Reader’s experience

Comment #5:

• No other book has 326 articles on the topics covered and included in the title of this Book: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS & BioInformatics, Simulations and the Genome Ontology

The e-Reader is encouraged to read the Preface, the Introduction and the Epilogue to Genomics Volume 2

• Original Volume Two:

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

On Amazon.com since 12/28/2019

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

AND

the e-Reader is encouraged to continue to Genomics Volume 1

Genomics Orientations for Personalized Medicine

VOLUME ONE

On Amazon.com since 23/11/2015

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

NEW GENRE Genomics, Volume Three:

Results of Medical Text Analysis with Natural Language Processing (NLP): Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS

https://www.amazon.com/dp/B0BRD8JXTL

Results of Medical Text Analysis with Natural Language Processing (NLP): Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS & BioInformatics, … de la investigación genómica Book 3) Kindle Edition

by Madison Davis (Author), Aviva Lev-Ari (Editor), Stephen J. Williams (Editor), Marcus W. Feldman (Editor) Format: Kindle Edition

Serie B: Fronteras de la investigación genómica 

(3 book series) Kindle Edition

by Larry H. Bernstein (Author) , Stephen J. Williams (Author) , Sudipta Saha (Author) , Ritu Saxena (Author) , Tilda Barliya (Author) , Anamika Sarkar (Author) , Marcus W. Feldman (Author) , Demet Sag (Author) , Prabodh kandala (Author) , Larry H. Bernstein (Author) , Aviva Lev-Ari (Author) , Madison Davis (Author)

https://www.amazon.com/dp/B0BQGZYZVT?binding=kindle_edition&ref=dbs_dp_rwt_sb_pc_tukn

Appendix to this volume – Wolfram Code

• for Hypergraph Plots &
• for Tree Diagram Plots 

NLP Code writing for 7/8 Parts 1,2,3,4,5,7,8

[No Code for Part 6 in Volume 3]

by

Madison Davis

Code used in production of Hyper-graph Plots at the Book Part Level

 

Hyper-  graph Plot #1   for 1.1.1 based on 16 articles & on 14 keywords

wordSheet ={  protein,cancer,dna,gene,genes,rna,survival,immune,tumor,research,patients,human, genome,expression }; topN = Length[wordSheet]; words = {}; For[i = 1, i <= topN, i++, words = Append[words, ToString[wordSheet[[i]]]]]; Print[words]; maxN = 3; broadTerms = {}; words1 = {}; words2 = {}; For[i = 1, i <= topN, i++,If[Length[Position[WordData[], Extract[words, i]]] > 0, broadTerms = Append[broadTerms, Flatten[Values[WordData[Extract[words, i], “BroaderTerms”]]]]; words1 = Append[words1, Extract[words, i]], words2 = Append[words2, Extract[words, i]]]]; For[i = 1, i <= Length[broadTerms], i++, If[Length[broadTerms[[i]]] > maxN, broadTerms[[i]] = broadTerms[[i]][[1 ;; maxN]]]];   edgeList = {}; edgeList = Append[edgeList,Prepend[words1, “”]] ; For[i = 1, i <= Length[words1], i++,  edgeList = Append[edgeList, Append[broadTerms[[i]], words1[[i]]]]]; ResourceFunction[“HypergraphPlot”][edgeList, VertexSize -> 0.01, VertexLabels -> Automatic, PlotTheme -> “Detailed”, SubsetEdge -> False, SubsetBoundaryScale -> 7, VertexStyle -> Red,”BaseLayout” -> “RadialEmbedding”,”SubsetEdgeStyle” -> ColorData[100, “ColorList”]]

Hypergraph Plot #2  for 1.1.2 based on 17 articles & on 12 keywords

wordSheet ={  cancer,mutations,patients,gene,disease,genetic,mutation,data,clinical,tumor,genes, protein }; topN = Length[wordSheet]; words = {}; For[i = 1, i <= topN, i++, words = Append[words, ToString[wordSheet[[i]]]]]; Print[words];

Code used in production of Tree Diagram Plots

 

Tree Diagram Plot #1  for 1.1.1 based on 16 articles & on 14 keywords

wordSheet ={  protein,cancer,dna,gene,genes,rna,survival,immune,tumor,research,patients,human, genome,expression }; topN = Length[wordSheet]; words = {}; For[i = 1, i <= topN, i++, words = Append[words, ToString[wordSheet[[i]]]]]; Print[words]; maxN = 3; broadTerms = {}; words1 = {}; words2 = {}; For[i = 1, i <= topN, i++,If[Length[Position[WordData[], Extract[words, i]]] > 0, broadTerms = Append[broadTerms, Flatten[Values[WordData[Extract[words, i], “BroaderTerms”]]]]; words1 = Append[words1, Extract[words, i]], words2 = Append[words2, Extract[words, i]]]]; For[i = 1, i <= Length[broadTerms], i++, If[Length[broadTerms[[i]]] > maxN, broadTerms[[i]] = broadTerms[[i]][[1 ;; maxN]]]];   edgeList = {}; For[i = 1 , i <= Length[broadTerms], i++,  edgeList = Append[edgeList, broadTerms[[i]]]]; For[i = 1 , i <= Length[broadTerms], i++, For[j = 1, j <= Length[edgeList[[i]]], j++,  edgeList[[i]][[j]] = words1[[i]] -> edgeList[[i]][[j]]]]; For[i = 1 , i <= Length[words1], i++, edgeList = Append[edgeList, {“Chapter 1.1.1” -> words1[[i]]}]]; TreePlot[Flatten[edgeList], VertexSize -> 0.1, VertexLabels -> Automatic]

Tree Diagram Plot #2  for 1.1.2 based on 17 articles & on 12 keywords

wordSheet ={  cancer,mutations,patients,gene,disease,genetic,mutation,data,clinical,tumor,genes, protein }; topN = Length[wordSheet]; words = {}; For[i = 1, i <= topN, i++, words = Append[words, ToString[wordSheet[[i]]]]]; Print[words];

3.2 Pilot Study with UK-based TOP NLP company, 2021

3.3 Grok 4.1

• Exploratory Protocol for Comparison of NLP to LLM on Same Oncology Slice

Curators: Aviva Lev-Ari, PhD, RN and Stephen J. Williams, PhD, KOL on Cancer & Oncology

https://pharmaceuticalintelligence.com/2025/12/08/exploratory-protocol-for-comparison-of-nlp-to-llm-on-same-oncology-slice/

• 2025 Grok 4.1 Causal Reasoning & Multimodal on Identical Proprietary Oncology Corpus: From 673 to 5,312 Novel Biomedical Relationships: A Direct Head-to-Head Comparison with 2021 Static NLP – NEW Foundation Multimodal Model in Healthcare: LPBI Group’s Domain-aware Corpus Transforms Grok into the “Health Go-to Oracle”

Authors:

• Stephen J. Williams, PhD (Chief Scientific Officer, LPBI Group)
• Aviva Lev-Ari, PhD, RN (Founder & Editor-in-Chief Journal and BioMed e-Series, LPBI Group)
• Grok 4.1 by xAI

https://pharmaceuticalintelligence.com/2025/12/15/2025-grok-4-1-causal-reasoning-multimodal-on-identical-proprietary-oncology-corpus-from-673-to-5312-novel-biomedical-relationships-a-direct-head-to-head-comparison-with-2021-static-nlp-new-foun/

• 2026 Grok Multimodal Causal Reasoning on Proprietary Cardiovascular Corpus: From 2021 Wolfram NLP Baseline to Thousands of Novel Relationships – A Second Head-to-Head Validation of LPBI’s Domain-Aware Training Advantage

  Authors:

  • Aviva Lev-Ari, PhD, RN (Founder & Editor-in-Chief, Journal and BioMed e-Series, LPBI Group)
  • Grok 4.1 by xAI

  https://pharmaceuticalintelligence.com/2026/01/06/2026-grok-multimodal-causal-reasoning-on-proprietary-cardiovascular-corpus-from-2021-wolfram-nlp-baseline-to-thousands-of-novel-relationships-a-second-head-to-head-validation-of-lpbi/