Sub-Topic Classification of HIV related Opportunistic Infections. Miguel Anderson and Joseph Fonseca

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1 Sub-Topic Classification of HIV related Opportunistic Infections Miguel Anderson and Joseph Fonseca

2 Introduction Image collected from the CDC

3 Background Info What is HIV? How is it transferred? How is it treated? What are opportunistic infections?

4 The viral particles HIV Is a Retrovirus. The Montgolfier Brothers, University of Bristol,

5 Binding of the coat protein to receptor VIRAL COAT PROTEIN HIV life cycle: How HIV infects a cell and replicates itself using reverse transcriptase, Youtube Video, posted by, Kleptoplast, Jan 6,

6 Conformational change; binding of second protein HIV life cycle: How HIV infects a cell and replicates itself using reverse transcriptase, Youtube Video, posted by, Kleptoplast, Jan 6,

7 Fusion of the Membranes; phospholipids HIV life cycle: How HIV infects a cell and replicates itself using reverse transcriptase, Youtube Video, posted by, Kleptoplast, Jan 6,

8 Degradation of the matrix and capsid protein MATRIX PROTEIN CAPSID PROTEIN HIV life cycle: How HIV infects a cell and replicates itself using reverse transcriptase, Youtube Video, posted by, Kleptoplast, Jan 6,

9 HIV RNA in Cell VIRAL DNA with host nucleotides; single stranded VIRAL SS DNA VIRAL SS RNA HIV life cycle: How HIV infects a cell and replicates itself using reverse transcriptase, Youtube Video, posted by, Kleptoplast, Jan 6,

10 Double stranded DNA with Reverse Transcriptase VIRAL DOUBLE STRANDED DNA HIV life cycle: How HIV infects a cell and replicates itself using reverse transcriptase, Youtube Video, posted by, Kleptoplast, Jan 6,

11 Integrase Protein HIV life cycle: How HIV infects a cell and replicates itself using reverse transcriptase, Youtube Video, posted by, Kleptoplast, Jan 6,

12 Inside the Nucleus HOST CHROMOSOME NUCLEAR PORE VIRAL DNA INTEGRATION ESTABLISHES LIFE LONG INFECTION; ENDONUCLEASE ACTIVITY

13 HIV life cycle: How HIV infects a cell and replicates itself using reverse transcriptase, Youtube Video, posted by, Kleptoplast, Jan 6, Production of Viral Particles-Transcription RNA POLYMERASE

14 HIV life cycle: How HIV infects a cell and replicates itself using reverse transcriptase, Youtube Video, posted by, Kleptoplast, Jan 6, Translation Any viral protein ROUGH ER Ribosome mrna

15 VIRAL Polyproteins and RNA at Infected Cell Surface

16 Virus Budding Off From Infected Cell

17 Protease Clips Polyprotein Chains I PROTEASE POLYPROTEIN CHAIN

19 Treatment with Molecular Targets---Antiretrovirals Two to three drugs to prevent resistance; fast mutation rate. Fusion inhibitors (Gp120 proteins) CC25 antagonists Nucleoside Reverse Transcriptase Inhibitor (affects reverse transcriptase with dummy nucleosides(base and sugar)) Integrase Inhibitors (Allosteric Sites). Less integration causes less CD4 cells to go through apoptosis Protease Inhibitor (active site)

20 Opportunistic Infections and Stages of HIV Opportunistic infections (OIs) are infections that occur more frequently and are more severe in individuals with weakened immune systems, including people with HIV Acute Stage: 2-4 weeks of infections Stage 2: Clinical Latency (HIV inactivity) Stage 3: AIDS which can lead to opportunistic Diseases

21 Overview Fonseca et al. (2018) - Social Network analysis of HIV/AIDS literature Similar to Golgi 2 approach Pletscher-Frankild et al. (2015) - Co-occurrences of features in abstracts based on count occurances Different from approach used - tf*idf and cosine similarity network

22 Approaches tested Train Model in Two Ways Binary Viral vs Bacterial opportunistic infections Multi Class HBV vs HCV vs Syphilis vs Tuberculosis Precision and recall for the model

23 Approaches tested Infection Type MeSH Terms searched [all searched with HIV and Boolean Operators] Viral Viral Infection # of Abstracts retrieved Viral Hepatitis C 2370 Query the actual disease with HIV Retrieved by Golgi2 and PubMed manual search Viral Hepatitis B 1866 Bacterial Bacterial infection 3429 Bacterial Tuberculosis 1829 Bacterial Syphilis 581 Fungal Fungal Infection 947 Fungal Pneumocystis Pneumonia 459 Fungal Candidiasis 377 Fungal Cryptococcal Meningitis 131

Golgi 2 Parameters Min doc frequency %: 5 # tokens can be included in phrase: 3 Threshold rank b/w 0

24 Exploratory Analysis Methods Golgi 2 and PubMed scrapper Vectorize documents and weighting scheme n-gram*freq-idf ranking Latent semantic analysis Semantic Concept clustering Cluster visualization Golgi 2 Parameters Min doc frequency %: 5 # tokens can be included in phrase: 3 Threshold rank b/w 0 and 1:.6 Golgi 2 Parameters Min doc frequency %: 3 # tokens can be included in phrase: 3 Threshold rank b/w 0 and 1:.7

25 Predictive Analysis Methods LightSIDE binary classification using Naïve Bayes LightSIDE sub-topic classification using Logistic Regression Weka sub-topic classification using Logistic regression 10 Fold Cross Validation for Both Evaluation Metric- Accuracy of predicted labels

26 Subtopic Classification Training Features Features were used for training and selected based on accuracy of the model Unigrams outperformed other features The best features of the LightSIDE and Weka models were then compared in

27 Model Confusion Matrix for LightSIDE Model The confusion matrix shows where the model is accurately classifying the labels of the abstracts using the LightSIDE features. This model performed at 78% accuracy

28 Model Confusion Matrix for Weka Model The confusion matrix shows where the model is accurately classifying the labels of the abstracts using the UMLS concepts in Weka. This model performed at 45% accuracy

29 Results LightSIDE Model Testing Results The LightSIDE Model + selected features were used to test the model accuracy The model performed at 80.5% accuracy

30 TAPoR Text Analysis Comparator Figure to right represents the Word Distribution of most common words POI- Relative ratio Figure to left represents the Word Distribution of unique words POI- text counts

31 TAPoR Statistics on the text analysis

32 Discussion/Conclusion See why the predictive model still showed confusion between HBV (text 1) and HCV (text 2) subtopics (possible indication for the high rate of false negatives in the model). The word count for the abstracts of each subtopic were not equal. Although the same amount of abstracts were used, the quality of the abstracts were not accounted for. Based on the analyses it apparent that there were biases towards HCV subtopic classification due to the amount of unique words it possessed.

33 Future Direction Combine UMLS and MeSH terms to see if this increases classification model accuracy Add more robust features to the abstract scraper that test for quality of results Build a predictive model to test where common opportunistic infections may arise in a population. This can be used to determine if there are undiagnosed HIV positive patients in the population

34 References Grimwade, K., & Swingler, G. H. (2006). Cotrimoxazole prophylaxis for opportunistic infections in children with HIV infection. Cochrane Database of Systematic Reviews. doi: / cd pub2 Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), doi: /science.aaa8415 Ortiz, M. S. (2018, June 11). Tokens [Video file]. Retrieved from Ortiz, M. S. (2018, June 11). How does TF-IDF weighting really work? [Video file]. Retrieved from HIV Is a Retrovirus. The Montgolfier Brothers, University of Bristol, HIV life cycle: How HIV infects a cell and replicates itself using reverse transcriptase, Youtube Video, posted by, Kleptoplast, Jan 6, HIV/AIDS. Centers for Disease Control and Prevention, Centers for Disease Control and Prevention, 30 May 2017,

LESSON 4.6 WORKBOOK. Designing an antiviral drug The challenge of HIV

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