Title:	Quantum Computing for Analyzing CD4 Lymphocytes and Antiretroviral Therapy
Version:	0.0.4
Description:	Resources, tutorials, and code snippets dedicated to exploring the intersection of quantum computing and artificial intelligence (AI) in the context of analyzing Cluster of Differentiation 4 (CD4) lymphocytes and optimizing antiretroviral therapy (ART) for human immunodeficiency virus (HIV). With the emergence of quantum artificial intelligence and the development of small-scale quantum computers, there's an unprecedented opportunity to revolutionize the understanding of HIV dynamics and treatment strategies. This project leverages the R package 'qsimulatR' (Ostmeyer and Urbach, 2023, https://CRAN.R-project.org/package=qsimulatR), a quantum computer simulator, to explore these applications in quantum computing techniques, addressing the challenges in studying CD4 lymphocytes and enhancing ART efficacy.
License:	MIT + file LICENSE
Encoding:	UTF-8
RoxygenNote:	7.3.2
Depends:	R (≥ 3.5)
LazyData:	true
Suggests:	rmarkdown, testthat (≥ 3.0.0)
Config/testthat/edition:	3
Imports:	dplyr, magrittr
NeedsCompilation:	no
Packaged:	2025-05-20 21:55:13 UTC; acua6
Author:	Juan Pablo Acuña González [aut, cre, cph]
Maintainer:	Juan Pablo Acuña González <22253567@uagro.mx>
Repository:	CRAN
Date/Publication:	2025-05-20 22:30:07 UTC

Classify HIV phenotype interactions using k-means clustering

Description

This function performs k-means clustering on the differences in viral load and CD4 counts to classify interaction types between HIV phenotypes. It returns an object of class InteractionClassification, a data.frame with classification labels.

Usage

InteractionClassification(cd_result, vl_result, k = 4, ns = 100, seed = 123)

Arguments

Value

A data.frame of class InteractionClassification with three columns:

cds3_result

The CD4 count difference for each interaction.

vlogs3_result

The viral load difference (log scale) for each interaction.

classification

An integer label (1 to k) indicating the interaction cluster.

Examples

set.seed(42)
data(cd_3)
cd_data <- cd_3[,-1]
cd_result <- cds_diff(cd_data)
data(vl_3)
vl_data <- vl_3[,-1]
vl_result <- vlogs_diff(vl_data)
result <- InteractionClassification(cd_result = cd_result, vl_result = vl_result)

Longitudinal CD4 Lymphocyte Counts for HIV Patients (2018-2024)

Description

Contains longitudinal measurements of CD4 lymphocyte counts for 176 patients living with HIV, recorded over the period from 2018 to 2024. CD4 counts are a critical indicator of immune function, used to monitor the progression of HIV and the effectiveness of treatments. Measurements were taken at various points throughout the study, with some missing values due to unavailable data for specific patients at certain times.

Usage

cd_3

Format

A data frame with 176 rows and 18 variables:

ID

Unique identifier for each patient.

cd_2018_1

CD4 count for the first measurement in 2018.

cd_2018_2

CD4 count for the second measurement in 2018.

cd_2019_1

CD4 count for the first measurement in 2019.

cd_2019_2

CD4 count for the second measurement in 2019.

cd_2020_1

CD4 count for the first measurement in 2020.

cd_2021_1

CD4 count for the first measurement in 2021.

cd_2021_2

CD4 count for the second measurement in 2021.

cd_2021_3

CD4 count for the third measurement in 2021.

cd_2022_1

CD4 count for the first measurement in 2022.

cd_2022_2

CD4 count for the second measurement in 2022.

cd_2022_3

CD4 count for the third measurement in 2022.

cd_2023_1

CD4 count for the first measurement in 2023.

cd_2023_2

CD4 count for the second measurement in 2023.

cd_2023_3

CD4 count for the third measurement in 2023.

cd_2024_1

CD4 count for the first measurement in 2024.

cd_2024_2

CD4 count for the second measurement in 2024.

cd_2024_3

CD4 count for the third measurement in 2024.

Details

. CD4 counts are used to monitor immune system health in individuals with HIV. A lower CD4 count often indicates a weakened immune system, whereas higher counts suggest a stronger immune response. Some values are missing, indicating no measurement was taken for a particular patient at that time.

Source

Clinical data from Hospital Vicente Guerrero, IMSS, HIV Clinic.

Examples

# Load the dataset
data(cd_3)

# Summarize CD4 counts for the year 2021
summary(cd_3[, c("cd_2021_1", "cd_2021_2", "cd_2021_3")])

Create Mean Differences from Longitudinal CD4 Data

Description

This function calculates the mean differences of CD4 counts across time for each individual in the dataset.

Usage

cd_diff(cd_data)

Arguments

Value

An object of class "Interaction" with the following components:

cd3_diff

Mean differences of raw CD4 count values.

Examples

data(cd_3)
cd_data <- cd_3[,-1]
result <- cd_diff(cd_data)

Create Mean Standardized Differences from Longitudinal CD4 Data

Description

This function calculates the mean standardized differences of CD4 counts across time for each individual in the dataset.

Usage

cds_diff(cd_data)

Arguments

Value

An object of class "Interaction" with the following components:

cds3_diff

Mean standardized differences of raw CD4 count values.

Examples

data(cd_3)
cd_data <- cd_3[,-1]
result <- cds_diff(cd_data)

Estimate Payoff Parameters for HIV Phenotype Interactions

Description

This function estimates the payoff parameters for HIV phenotype interactions based on the provided classification object and predictions from a viral load model. It calculates the mean differences in viral loads and CD4 counts, as well as the average payoffs for each classification.

Usage

estimate_payoffs(object, predictions)

Arguments

Examples

set.seed(42)
data(cd_3)
cd_data <- cd_3[,-1]
cd_result <- cds_diff(cd_data)
data(vl_3)
vl_data <- vl_3[,-1]
vl_result <- vlogs_diff(vl_data)
result <- InteractionClassification(cd_result = cd_result, vl_result = vl_result)
data(preds)
payoffs_results <- estimate_payoffs(result, preds)

Mean Squared Errors for Interaction Classification

Description

Mean squared errors (MSE) for viral load differences and CD4 count differences by comparing the actual values with the group means from the classification.

Computes the mean squared error (MSE) between observed CD4 and viral load differences and their corresponding predicted payoff values within each interaction classification.

Usage

mse(object, ...)

mse(object, ...)

Arguments

Value

A data.frame containing the MSE for CD4 count differences (mse_cds_diff) and (mse_vlogs_diff) for viral load differences.

Examples

set.seed(42)
data(cd_3)
cd_data <- cd_3[,-1]
cd_result <- cds_diff(cd_data)
data(vl_3)
vl_data <- vl_3[,-1]
vl_result <- vlogs_diff(vl_data)
result <- InteractionClassification(cd_result = cd_result, vl_result = vl_result)
mse(result)

set.seed(42)
data(cd_3)
cd_data <- cd_3[,-1]
cd_result <- cds_diff(cd_data)
data(vl_3)
vl_data <- vl_3[,-1]
vl_result <- vlogs_diff(vl_data)
result <- InteractionClassification(cd_result = cd_result, vl_result = vl_result)
data(preds)
payoffs_results <- estimate_payoffs(result, preds)
mse(payoffs_results)

Mean Squared Errors for Interaction Classification

Description

Mean squared errors (MSE) for viral load differences and CD4 count differences by comparing the actual values with the group means from the classification.

Usage

## S3 method for class 'InteractionClassification'
mse(object, ...)

Arguments

Mean Squared Errors for Payoff Predictions

Description

Computes the mean squared error (MSE) between observed CD4 and viral load differences and their corresponding predicted payoff values within each interaction classification.

Usage

## S3 method for class 'payoffs'
mse(object, ...)

Arguments

Find Nearest Payoff

Description

This function computes the nearest simulated payoff from a given list of payoffs based on a viral load difference (vl_diff). It returns both the nearest payoff value and its corresponding payoff name.

Usage

nearest_payoff(vl_diff, payoffs_list)

Arguments

Examples

 I <- diag(2)
 H <- 1 / sqrt(2) * matrix(c(1, 1, 1, -1), 2, 2)
 Z <- diag(c(1, -1))
 gates <- list(I = I, H = H, Z = Z)
 alpha <- 0.3; beta <- 0.1; gamma <- 0.5; theta <- 0.2
 alpha2 <- 0.35; beta2 <- 0.15; gamma2 <- 0.6; theta2 <- 0.25
 pays <- payoffs_list(gates, alpha, beta, gamma, theta, alpha2, beta2, gamma2, theta2)
 nearest_payoff(-0.2, pays)

Compute Payoff Values for Quantum HIV Phenotype Interactions

Description

Computes payoff values for all pairwise combinations of quantum gate strategies provided in a named list. For each pair, the function calculates the payoffs for both phenotypes v and V using two different sets of payoff parameters.

Usage

payoffs_list(gates, alpha, beta, gamma, theta, alpha2, beta2, gamma2, theta2)

Arguments

Examples

I <- diag(2)
H <- 1 / sqrt(2) * matrix(c(1, 1, 1, -1), 2, 2)
Z <- diag(c(1, -1))
gates <- list(I = I, H = H, Z = Z)
payoffs <- payoffs_list(gates, 1, 0.5, 0.3,0.2, 1.5, 0.6, 0.7, 0.8)

Calculate Final State and Payoffs in Quantum Game

Description

This function calculates the final quantum state and expected payoffs for two players in a quantum game based on their strategies. The function uses quantum gates and unitary transformations to simulate the game dynamics.

Usage

phen_hiv(strategy1, strategy2, alpha, beta, gamma, theta)

Arguments

References

Özlüer Başer, B. (2022). "Analyzing the competition of HIV-1 phenotypes with quantum game theory". Gazi University Journal of Science, 35(3), 1190–1198. doi:10.35378/gujs.772616

Examples

strategy1 <- diag(2) # Identity matrix for strategy 1
strategy2 <- diag(2) # Identity matrix for strategy 2
alpha <- 1
beta <- 0.5
gamma <- 2
theta <- 0.1
result <- phen_hiv(strategy1, strategy2, alpha, beta, gamma, theta)

Predictions for Longitudinal Viral Load Values for HIV Patients (2018-2024)

Description

Contains predictions of longitudinal viral load values for 176 patients from 2018 to 2024.

Usage

preds

Format

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 176 rows and 1 columns.

Source

Clinical data from Hospital Vicente Guerrero, IMSS, HIV Clinic.

Examples

data(preds)
head(preds)

Batched Predictions for Longitudinal Viral Load Values for HIV Patients (2018-2024)

Description

Contains batched predictions of longitudinal viral load values for 176 patients from 2018 to 2024.

Usage

preds2

Format

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 176 rows and 1 columns.

Source

Clinical data from Hospital Vicente Guerrero, IMSS, HIV Clinic.

Examples

data(preds2)
head(preds2)

Quantum Phenotype Interactions in HIV Model

Description

The qphen dataset contains 176 observations and 24 variables, representing classified phenotype interactions in a quantum game-theoretic model of HIV phenotypes. The data includes CD4 and viral load differences, quantum game strategies, classification clusters, and tuberculosis/genoresistance indicators.

Usage

data(qphen)

Format

A data frame with 176 rows and 24 variables:

id

(double) Unique identifier for each observation.

vl_diff

(double) Difference in viral load (log scale) between time points.

cd_diff

(double) Difference in CD4 count between time points.

vlogs_diff_mean

(double) Mean difference of viral loads across the dataset.

cds_diff_mean

(double) Mean difference of CD4 counts across the dataset.

n

(double) Number of cases in each interaction cluster.

payoffs

(double) Computed payoff for the phenotype interaction.

#'

payoffs_b

(double) Alternative computed payoff.

nearest_payoff

(double) Closest estimated payoff value.

classification_2

(double) Cluster assignment for phenotype interactions (second clustering method).

classification_3

(double) Cluster assignment for phenotype interactions (third clustering method).

classification_4

(double) Cluster assignment for phenotype interactions (fourth clustering method).

phen_1

(double) Phenotype type (v or V).

str1_2

(double) Strategy of the first phenotype using X, T, or H gate (binary encoding).

str1_3

(double) Alternative strategy of the first phenotype.

str2_2

(double) Strategy of the second phenotype using H, Id, S, T, X, Y, or Z gate (binary encoding).

str2_3

(double) Alternative strategy of the second phenotype.

str2_4

(double) Alternative strategy of the second phenotype.

str2_5

(double) Alternative strategy of the second phenotype.

str2_6

(double) Alternative strategy of the second phenotype.

str2_7

(double) Alternative strategy of the second phenotype.

batch_1

(double) Indicates whether predictions were made on full data or batch data.

TB_1

(double) Indicator for tuberculosis presence (1 = TB, 0 = no TB).

GR_1

(double) Indicator for genoresistance presence (1 = resistant, 0 = non-resistant).

Examples

data(qphen)
head(qphen)

Summarize an InteractionClassification object

Description

Computes summary statistics by classification group from an object of class InteractionClassification, including mean differences in viral load and CD4 counts, and the number of observations per cluster.

This function summarizes the payoffs object by classification.

Usage

summary(object, ...)

summary(object, ...)

Arguments

Value

A data.frame with one row per interaction cluster and the following columns:

classification

Cluster label (as factor).

cds_diff_mean

Mean of CD4 differences in the group.

vlogs_diff_mean

Mean of viral load differences in the group.

n

Number of observations in the group.

Examples

set.seed(42)
data(cd_3)
cd_data <- cd_3[,-1]
cd_result <- cds_diff(cd_data)
data(vl_3)
vl_data <- vl_3[,-1]
vl_result <- vlogs_diff(vl_data)
result <- InteractionClassification(cd_result = cd_result, vl_result = vl_result)
summary(result)
set.seed(42)
data(cd_3)
cd_data <- cd_3[,-1]
cd_result <- cds_diff(cd_data)
data(vl_3)
vl_data <- vl_3[,-1]
vl_result <- vlogs_diff(vl_data)
result <- InteractionClassification(cd_result = cd_result, vl_result = vl_result)
data(preds)
payoffs_results <- estimate_payoffs(result, preds)
summary(payoffs_results)

Summarize an InteractionClassification object

Description

Computes summary statistics by classification group from an object of class InteractionClassification, including mean differences in viral load and CD4 counts, and the number of observations per cluster.

Usage

## S3 method for class 'InteractionClassification'
summary(object, ...)

Arguments

Summarize Payoffs

Description

This function summarizes the payoffs object by classification.

Usage

## S3 method for class 'payoffs'
summary(object, ...)

Arguments

Longitudinal Viral Load Values for HIV Patients (2018-2024)

Description

Contains longitudinal measurements of viral load for 176 patients from 2018 to 2024. Viral load is a critical marker used to monitor the effectiveness of HIV treatment by measuring the amount of HIV RNA in the blood.

Usage

vl_3

Format

A data frame with 176 rows and 18 variables:

ID

Unique identifier for each patient.

vl_2018_1

Viral load for the first measurement in 2018.

vl_2018_2

Viral load for the second measurement in 2018.

vl_2019_1

Viral load for the first measurement in 2019.

vl_2019_2

Viral load for the second measurement in 2019.

vl_2020_1

Viral load for the first measurement in 2020.

vl_2021_1

Viral load for the first measurement in 2021.

vl_2021_2

Viral load for the second measurement in 2021.

vl_2021_3

Viral load for the third measurement in 2021.

vl_2022_1

Viral load for the first measurement in 2022.

vl_2022_2

Viral load for the second measurement in 2022.

vl_2022_3

Viral load for the third measurement in 2022.

vl_2023_1

Viral load for the first measurement in 2023.

vl_2023_2

Viral load for the second measurement in 2023.

vl_2023_3

Viral load for the third measurement in 2023.

vl_2024_1

Viral load for the first measurement in 2024.

vl_2024_2

Viral load for the second measurement in 2024.

vl_2024_3

Viral load for the third measurement in 2024.

Details

The viral load measurements provide insight into the patient's response to antiretroviral therapy (ART). Lower viral load values, especially undetectable levels, indicate better control of the infection. Missing values indicate that no viral load measurement was available for that patient at that specific time.

Source

Clinical data from Hospital Vicente Guerrero, IMSS, HIV Clinic.

Examples

## Not run: 
# Load the dataset
data(vl_3)

# Summarize viral loads for the year 2021
summary(vl_3[, c("cd_2021_1", "cd_2021_2", "cd_2021_3")])

## End(Not run)

Create Mean Differences from Longitudinal Viral Load Data

Description

This function calculates the mean differences of viral loads across time for each individual in the dataset.

Usage

vl_diff(vl_data)

Arguments

Value

An object of class "Interaction" with the following components:

vl3_diff

Mean differences of raw viral load values.

Examples

data(vl_3)
vl_data <- vl_3[,-1]
result <- vl_diff(vl_data)

Create Mean Differences from Logarithmic Viral Load Data

Description

This function calculates the mean differences of lograithmic viral loads across time for each individual in the dataset.

Usage

vlog_diff(vl_data)

Arguments

Value

An object of class "Interaction" with the following components:

vlog3_diff

Mean differences of logarithmic viral load values.

Examples

data(vl_3)
vl_data <- vl_3[,-1]
result <- vlog_diff(vl_data)

Create Mean Standardized Differences from Logarithmic Viral Load Data

Description

This function calculates the mean standardized differences of logarithmic viral loads across time for each individual in the dataset.

Usage

vlogs_diff(vl_data)

Arguments

Value

An object of class "Interaction" with the following components:

vlogs3_diff

Mean standardized differences of logarithmic viral load values.

Examples

data(vl_3)
vl_data <- vl_3[,-1]
result <- vlogs_diff(vl_data)