Quantum Gradient Optimized Drug Repurposing Prototype for

Omics Data

Don Roosan

, Saif Nirzhor

, Rubayat Khan

and Fahmida Hai

School of Engineering and Computational Sciences, Merrimack College, North Andover, U.S.A.

University of Texas Southwestern Medical Center, Dallas, U.S.A.

University of Nebraska Medical Center, Omaha, U.S.A.

Tekurai Inc., San Antonio, U.S.A.

Keywords: Quantum Computing, Drug Repurposing, Massive Genomics Data, Large Language Models, Quantum

Gradient.

Abstract: This paper presents a novel quantum-enhanced prototype for drug repurposing and addresses the challenge of

managing massive genomics data in precision medicine. Leveraging cutting-edge quantum server

architectures, we integrated quantum-inspired feature extraction with large language model (LLM)–-based

analytics and unified high-dimensional omics datasets and textual corpora for faster and more accurate

therapeutic insights. Applying Synthetic Minority Over-sampling Technique (SMOTE) to balance

underrepresented cancer subtypes and multi-omics sources such as TCGA and LINCS, the pipeline generated

refined embeddings through quantum principal component analysis (QPCA). These embeddings drove an

LLM trained on biomedical texts and clinical notes, generating drug recommendations with improved

predicted efficacy and safety profiles. Combining quantum computing with LLM outperformed classical

PCA-based approaches in accuracy, F1 score, and area under the ROC curve. Our prototype highlights the

potential of harnessing quantum computing and next-generation servers for scalable, explainable, and timely

drug repurposing in modern healthcare.

1 INTRODUCTION

Drug repositioning, also known as drug repurposing,

has emerged as a critical strategy for accelerating

therapeutic innovation within the modern healthcare

environment. The conventional drug discovery

process, typically spanning more than a decade,

demands an immense financial outlay (Corsello et al.,

2017; Park, 2019). Moreover, even after preliminary

regulatory approvals, many candidate drugs fail in

subsequent clinical trial phases, resulting in

significant resource loss. Repurposing approved

drugs or late-stage clinical candidates offers a much

more efficient path. These compounds already come

with known safety profiles and pharmacokinetic

characteristics, allowing researchers to skip multiple

preclinical steps, thereby saving both time and

money. This efficiency is especially attractive when

confronting urgent or emerging health crises—such

as newly identified viral pathogens, widespread

cancers, or rare diseases with limited treatment

options—where speed can be a decisive factor. One

of the chief reasons drug repurposing has garnered

attention is the ability to sidestep the most daunting

and time-intensive stages of drug development.

Under standard protocols, discovering and validating

a novel compound involves extensive preclinical

testing to gauge toxicity, clinical complexity dosing

parameters, and efficacy in animal models before it

can even proceed to trials in humans (Islam et al.,

2014; Islam, Mayer, et al., 2016; Islam, Weir, et al.,

2016a, 2016b). These steps alone can consume years

and require substantial financial support. By turning

to substances already verified as safe for human use,

scientists can concentrate more on efficacy for a

novel indication, substantially reducing the overall

timeline. This approach also heightens the likelihood

of success in advanced clinical trials, as the critical

factor of human safety has been substantially

addressed.

Speed is paramount in public health crises,

making repurposing strategies particularly relevant.

When rapid drug deployment is critical, such as

during a pandemic, compounds that are already

Roosan, D., Nirzhor, S., Khan, R., Hai and F.

Quantum Gradient Optimized Drug Repurposing Prototype for Omics Data.

DOI: 10.5220/0013524900003967

In Proceedings of the 14th Inter national Conference on Data Science, Technology and Applications (DATA 2025), pages 465-472

ISBN: 978-989-758-758-0; ISSN: 2184-285X

465

approved—or very close to approval—can be

mobilized faster. If a known medication reveals the

potential to inhibit an emergent virus or alleviate

severe symptoms, it stands a significantly higher

chance of quickly reaching clinical use(Graves et al.,

2018; Islam et al., 2015). Beyond pandemic

scenarios, this model can extend to diseases where no

existing interventions are available, including

neglected tropical diseases or rare genetic disorders,

illustrating how the timeliness afforded by drug

repositioning can address unmet medical

needs(Tukur et al., 2023).

Along with saving time and resources, drug

repurposing is conceptually powerful. A drug

designed for one purpose may act on multiple

biological pathways, broadening its therapeutic

impact. Advances in fields such as genomics and

proteomics have deepened our understanding of how

diseases can share overlapping molecular

mechanisms, supporting a rationale for exploring the

off-label application of known drugs. As knowledge

about intricate disease networks continues to grow,

the argument for systematically examining

alternative uses of existing drugs becomes even more

convincing (Roosan et al., 2019). This approach

effectively acts as a shortcut, delivering novel

treatments to patients more rapidly than de novo drug

discovery efforts typically allow.

Drug repurposing, using medications for new

indications, has a long history, exemplified by

Aspirin (initially for pain, later for heart conditions)

and thalidomide (sedative, later for leprosy and

cancer). Modern methods leverage data-driven

approaches, including bioinformatics and machine

learning, to analyze molecular and clinical datasets

for new drug applications (Roosan et al., 2021;

Roosan, Wu, Tran, et al., 2022; Deng et al., 2022).

However, complex diseases like cancer and diabetes,

involving intricate gene-protein-environment

interactions, and polypharmacology (drugs affecting

multiple mechanisms) make repurposing data-

intensive. Big data from genomics, proteomics, and

electronic medical records offers opportunities but

poses integration challenges due to diverse data types

and high dimensionality (Li et al., 2021; Roosan,

Hwang, et al., 2020; Sammani et al., 2019).

Traditional computational tools struggle with the

"curse of dimensionality," necessitating advanced

analytical methods for biomedical data (Cao et al.,

2011; Roosan et al., 2017). Quantum computing

promises to revolutionize drug repurposing by using

qubits’ superposition and entanglement to efficiently

analyze large datasets (D. Roosan et al., 2024; Doga

& et al., 2024; J. Yang et al., 2024). Though limited

by qubit count and error rates, quantum-inspired

algorithms like quantum kernel methods and

principal component analysis uncover hidden

patterns in biomedical data (Jeyaraman et al., n.d.;

Sung et al., 2018). Applications include precise

molecular modeling for protein-ligand docking and

combinatorial optimization for drug-disease pairings

(D. Roosan et al., 2024; Pandey et al., 2024). LLMs

excel at processing unstructured text and multimodal

data, revealing connections between diseases,

biomarkers, and drugs (Wu et al., 2012; R. Yang et

al., 2023). Challenges include misinformation, biases,

and transparency issues. Quantum-enhanced feature

extraction paired with LLMs could streamline drug

repurposing by tackling high-dimensional data and

interpreting findings (Islam et al., 2014; Itri & Patel,

2018). Disease complexity and big data demand

advanced computational strategies beyond heuristics,

leveraging AI-driven tools for healthcare insights.

Blockchain technology enhances secure,

transparent healthcare data sharing for drug

repurposing by storing immutable records of patient

consents and data access (Dhillon et al., 2017;

Roosan, Wu, Tatla, et al., 2022). In pandemics, AI-

driven analytics, augmented by quantum-inspired

methods, expedite identifying existing drugs for new

pathogens (Challen et al., 2019; Roosan, Chok, et al.,

2020; Roosan et al., 2023). A quantum-enhanced,

large language model (LLM)-based system could

analyze large datasets—from genomic profiles to

clinical observations—using quantum-inspired

feature extraction and LLM interpretation to suggest

repurposed drug candidates with rationales. The

primary goal is to develop and validate a prototype of

this system, focusing on patient-specific data analysis

and interpretable recommendations using high-

dimensional genomic and clinical data.

2 METHOD

2.1 Dataset Preparation

This study utilized a multi-faceted approach to data

collection and integration, aiming to create a robust

foundation for a quantum-enhanced, large language

model (LLM)-driven drug repurposing system. The

dataset encompassed clinical records, synthetic

patient cohorts, and three major omics repositories,

all meticulously harmonized to ensure consistency in

format, terminology, and quality.

DATA 2025 - 14th International Conference on Data Science, Technology and Applications

466

2.1.1 Clinical Data

The MIMIC-III database, a comprehensive collection

of de-identified critical care patient data, served as the

primary source of clinical information (D. Clifford et

al., 2009). The database includes a wealth of data

points, such as patient demographics, vital signs, lab

results, medication records, and outcomes. For this

study, the data extraction focused on oncology-

related cases. The selection criteria prioritized

patients with documented cancer diagnoses, cancer-

related medication records, and sufficient laboratory

data to enable in-depth exploration of their disease

status. Standard protocols for de-identification and

privacy compliance were strictly adhered to

throughout the data handling process. The MIMIC-III

database includes information from over 40,000

patients admitted to critical care units at the Beth

Israel Deaconess Medical Center between 2001 and

2012.

2.1.2 Synthetic Cohort Generation

While MIMIC-III offers a broad range of patient

records, certain cancer subtypes and demographic

groups were underrepresented. To mitigate potential

biases arising from class imbalance, the Synthetic

Minority Over-sampling Technique (SMOTE) was

employed. SMOTE generated 60 synthetic patient

records, effectively balancing the representation of

prevalent or majority classes with those representing

rare or less frequently documented conditions. Each

synthetic patient entry included key features such as

demographic data (e.g., age, sex), lab results (e.g.,

complete blood counts, serum chemistry panels), vital

signs (e.g., systolic and diastolic blood pressure), and

clinical outcome indicators (e.g., survival or

readmission rates). This process ensured a more

uniform representation of diseases and disease stages,

resulting in a more robust training set for machine

learning algorithms. For instance, in the original

MIMIC-III dataset, African American patients

comprised approximately 9% of the total, while

Hispanic patients made up around 3%. After applying

SMOTE, the representation of these groups in the

synthetic cohort was increased to approximately 15%

each, providing a more balanced dataset for training

the model (Chawla et al., 2002).

2.2 Omics Data

In addition to clinical features, the system

incorporated molecular-level information to identify

biological patterns and potential drug repurposing

opportunities.

2.2.1 The Cancer Genome Atlas (TCGA)

RNA-sequencing data for breast cancer (BRCA)

samples were obtained from the TCGA portal. Strict

inclusion criteria ensured that only high-quality gene

expression profiles with robust clinical annotations,

such as tumor stage, lymph node involvement, and

other pathological features, were included. Each

sample's raw data was normalized using established

protocols, and the resulting gene expression matrices

were used for downstream analyses. The TCGA

database contains genomic data from over 11,000

patients across 33 different cancer types. For this

study, the breast cancer (BRCA) subset, which

includes data from approximately 1,100 patients, was

utilized (Tomczak et al., 2015).

2.2.2 Gene Expression Omnibus (GEO)

To further refine and validate cancer-specific gene

expression trends, the GSE2034 dataset, a well-

curated dataset relevant to breast cancer prognosis,

was integrated. This dataset contained microarray-

based expression values, which were meticulously

normalized and mapped to the same gene symbols

used in the TCGA-BRCA subset. The integration of

microarray data from GEO helped address potential

biases arising from reliance on a single technology or

population. The GSE2034 dataset includes gene

expression data from 286 breast cancer patients,

providing a valuable resource for validating findings

from the TCGA data (Barrett et al., 2013).

2.2.3 Library of Integrated Network-Based

Cellular Signatures (LINCS)

The LINCS L1000 dataset provided crucial

information on how various small-molecule

compounds affect gene expression across diverse cell

lines. The focus was on expression signatures that

captured drug-induced upregulation or

downregulation of genes relevant to cancer pathways.

This resource was essential for training the model to

link unique patient gene expression patterns to

possible therapeutic compounds, highlighting

existing drugs that could be repurposed based on their

cellular signatures. The LINCS L1000 dataset

contains gene expression profiles from over 1 million

experiments, measuring the effects of approximately

20,000 small-molecule compounds on various cell

lines (Duan et al., 2016).

Quantum Gradient Optimized Drug Repurposing Prototype for Omics Data

467

2.3 Data Pre-Processing

Data from these multiple sources underwent extensive

processing to ensure standardization and

comparability across clinical and molecular domains.

All patient records, both downloaded and

synthetically generated, underwent thorough quality

checks. Missing values in the clinical data were

imputed using mean or median values, depending on

the distribution of each feature. Outlier detection was

performed by setting interquartile range (IQR)

thresholds, removing samples with extreme values

that could skew the training process. Gene expression

matrices, from both RNA-seq and microarray sources,

were subjected to low-expression filtering,

eliminating genes that lacked sufficient read counts in

most samples.

Feature engineering began with the harmonization

of gene symbols across TCGA-BRCA, GSE2034, and

L1000 data. A targeted approach to feature selection

identified genes with the greatest variance across

disease subtypes, known cancer driver genes, and

genes encoding enzymes relevant to drug metabolism

(e.g., certain cytochrome P450 isoforms).

Dimensionality reduction was initiated through

classical Principal Component Analysis (PCA).

However, the core innovation involved feeding these

partially reduced features into a quantum-inspired

algorithm for further compression, preserving non-

linear relationships that PCA might overlook. In a

manner akin to the data-integration strategies

employed in nutrigenomics—where RNA and DNA

testing illuminate gene-environment interactions we

applied quantum feature mapping within our GNN

framework to enrich molecular data representations.

Recent advances in healthcare informatics

demonstrate how data visualization techniques, such

as heatmaps, can streamline the processing of

complex, unstructured data from electronic health

records (EHRs). Standardizing data is essential for

improving health information exchange and

interoperability, although it is often overlooked in

system-level implementations.

2.4 Quantum Enhanced LLM-Based

Drug Repurposing Model

After preparing cleaned datasets and curated features,

a framework combined quantum-inspired feature

extraction—using simulated Quantum Principal

Component Analysis (QPCA) and quantum kernel

methods due to hardware limitations—with a

transformer-based language model (LLM) for drug

prediction. Gradient-based optimization refined

embeddings. An SVM with a linear kernel classified

drug matches using MIMIC-III and synthetic

SMOTE-generated data, with specified training,

testing, and validation splits. The core system

integrated quantum-enriched embeddings with an

LLM fine-tuned on drug-disease relationships,

biomedical literature, and patient metadata. The

pipeline normalized raw data, reduced dimensions via

classical PCA and quantum transformations, used the

LLM to predict drug efficacy, and ranked candidates

by efficacy and safety. Hyperparameters, code, and

synthetic data are publicly available.To support

reproducibility, we included hyperparameter settings

and made the code and synthetic data publicly

available, addressing the experimentation discussion’s

prior lack of detail.

3 RESULTS

3.1 Model Performance

The quantum-enhanced feature extraction

demonstrated robust gains relative to purely classical

approaches. To quantitatively evaluate these

improvements, we computed standard classification

metrics—accuracy, precision, recall, F1-score, and the

area under the ROC curve (AUC)—for predicting a

successful drug match. A total of 1,200 labeled

instances (synthetic patients with known best treatment

outcomes or prospective matches) were used for

validation. The quantum-inspired transformations

consistently outperformed classical PCA, yielding a

higher F1-score by an average of 8% across multiple

runs. Notably, some samples with subtle gene

expression shifts only achieved clinically meaningful

matches when the quantum kernel transformations

Figure 1: Comparative performance of classical PCA-based

dimensionality reduction versus quantum-enhanced feature

engineering.

DATA 2025 - 14th International Conference on Data Science, Technology and Applications

468

were included, underscoring the sensitivity of this

approach to nuanced genetic variation. The quantum-

based plot reveals tighter clustering among patients

with similar clinical and and molecular profiles,

suggesting an improved capacity for separating

responders from non-responders.

To supplement these visual indicators, the

summary of performance metrics is shown in Table

1, comparing the average performance (± standard

deviation) across five cross-validation folds.

Table 1: Cross-Validation Performance Comparison of

Classical vs. Quantum Approaches.

Method

Accuracy

(%)

Precision

(%)

Recall

(%)

F1-

Score

(%)

AUC

Classica

l PCA

82.3 ±

2.1

80.7 ± 1.9

78.9 ±

2.6

79.8

± 2.4

0.84

0.03

Quantu

Enhance

88.5 ±

1.8

86.2 ± 2.0

84.7 ±

2.1

85.4

± 1.9

0.90

0.02

The table includes accuracy, precision, recall, F1-

score, and AUC, demonstrating consistent gains in all

metrics under the quantum-enhanced setting.To

further highlight the dimensionality reduction aspect,

Figure 2 presents a two-dimensional t-SNE

projection of patient embeddings. The upper panel

(Figure 2A) shows the clustering using only classical

PCA, whereas the lower panel (Figure 2B) overlays

the quantum-transformed embeddings on the same

manifold. We enhanced clustering evaluation by

Figure 2. A two-dimensional t-SNE projection of

patient embeddings, comparing classical PCA-based

clustering (2A) with quantum-transformed

embeddings (2B) on the same manifold.

Incorporating robust metrics such as a silhouette

index of 0.65 for quantum-enhanced embeddings (vs.

0.52 for classical PCA), acknowledging synthetic

data limitations and the need for real clinical

validation. To substantiate efficacy, we compared our

quantum-enhanced model to classical PCA-based

methods on the TCGA-BRCA dataset, achieving a

10% F1-score improvement, with plans to benchmark

against additional state-of-the-art solutions in future

work.

A final summary of representative drug

recommendations is provided in Table 2,

documenting sample outputs for three synthetic

patients with varying clinical statuses. Each row

indicates top drugs, predicted efficacy scores, and

relevant gene targets implicated in the drug match.

Table 2: Excerpt of Drug Recommendations for Three

Synthetic Patients.

Patient

Stage

Top

Recommended

Drug

Predicted

Efficacy

(%)

Key Gene

Targets

SYN-

II Palbociclib 78

CDK4,

CDK6

SYN-

III Tamoxifen 82

ESR1,

ESR2

SYN-

IIIB Sorafenib 74

RAF,

VEGFR,

PDGFR

This table highlights a range of therapy classes—

hormone modulators, kinase inhibitors, and multi-

target agents—all emerging as candidates from the

pipeline’s predictions based on individual patient

molecular and clinical characteristics. Integrating the

quantum-inspired transformations appears to sharpen

distinctions between viable and less-appropriate

options, potentially accelerating the pace of drug

discovery and repurposing for oncology care. These

results are consistent with earlier research

demonstrating that integrating AI techniques can

significantly improve data analysis and decision-

making

3.2 Synthetic Cohort

For the 60-patient synthetic cohort generated via

SMOTE, each individual’s record was processed

through the pipeline to derive recommended

treatments. These synthetic cases were diverse in

terms of disease stages, comorbidity indices, and

molecular profiles. The quantum-enhanced approach

proved particularly valuable in identifying relevant

kinase inhibitors and hormone modulators for

patients mimicking more advanced stages of breast

cancer. Many of these suggestions aligned with

known FDA-approved drugs in related contexts,

thereby highlighting the potential for repurposing in

real-world scenarios.

Quantum Gradient Optimized Drug Repurposing Prototype for Omics Data

469

4 DISCUSSIONS

Our prototype revolutionizes clinical decision-

making by integrating high-dimensional omics data

with clinical records. Unlike traditional drug

repurposing, which relies on slow literature reviews

and outdated machine learning, our quantum-inspired

approach excels at uncovering gene-protein-disease

relationships. Processed by a fine-tuned LLM, it

delivers clear, interpretable drug recommendations,

aiding clinicians in complex cases where standard

treatments fail (Roosan, 2023; Roosan et al., 2023;

Roosan, Roosan, Kim, et al., 2022). The LLM

enhances transparency with explanations linking

recommendations to gene-drug interactions,

validated by synthetic data, though real-world trials

are needed. Its user-friendly interface lets clinicians

explore reasoning from literature, omics, and patient

data, reducing "black-box" skepticism and boosting

trust. Public awareness of off-label treatments,

supported by LLM-generated summaries, can drive

advocacy and trial enrollment, fostering trust in data

sharing. In policy, our quantum-enhanced LLM aids

lawmakers by assessing drug viability, balancing

innovation, safety, and costs. Explainable AI

translates molecular insights into policy-friendly

narratives, speeding up therapy adoption. The

prototype supports future multimodal LLMs,

integrating voice, images, and video for a holistic

patient view, improving equity and precision.

Challenges include quantum hardware costs, LLM

training demands, and integration complexities. Data

biases and latency need addressing, but investment in

quantum research and LLM efficiency can overcome

these. Limitations include hardware constraints, data

requirements, and the need for diverse clinical

validation. Future work will refine the system.

5 CONCLUSIONS

The prototype presented in this study offers a tangible

advancement in drug repurposing by combining

quantum-inspired feature extraction with LLM–based

analytics. By orchestrating high-dimensional omics

datasets—such as RNA-seq and microarray gene

expression profiles—with detailed clinical

information, the system demonstrates a clear

capability to prioritize potential therapies for diverse

patient populations. Unlike conventional machine

learning methods that struggle to handle complex and

expansive data, the quantum-enhanced approach

excels at discerning subtle patterns in gene

expression, ultimately improving classification

metrics such as accuracy, F1-score, and area under

the ROC curve. The pipeline’s ability to integrate

QPCA with LLM-driven interpretation highlights the

potential for scalable, explainable, and timely

solutions in modern healthcare. Though current

hardware limitations and computational demands

pose practical challenges, ongoing innovations in

quantum simulators and AI architectures will likely

reduce operating costs and further streamline this

approach.

ACKNOWLEDGEMENTS

We are grateful to Merrimack College for support.

REFERENCES

Barrett, T., Wilhite, S. E., Ledoux, P., Evangelista, C., Kim,

I. F., Tomashevsky, M., Marshall, K. A., Phillippy, K.

H., Sherman, P. M., Holko, M., Yefanov, A., Lee, H.,

Zhang, N., Robertson, C. L., Serova, N., Davis, S., &

Soboleva, A. (2013). NCBI GEO: Archive for

functional genomics data sets—update. Nucleic Acids

Research, 41(D1), D991–D995. https://doi.org/10.10

93/nar/gks1193

Cao, N., Gotz, D., Sun, J., & Qu, H. (2011). Dicon:

Interactive visual analysis of multidimensional clusters.

Visualization and Computer Graphics, IEEE

Transactions On, 17(12), Article 12.

Challen, R., Denny, J., Pitt, M., Gompels, L., Edwards, T.,

& Tsaneva-Atanasova, K. (2019). Artificial

intelligence, bias and clinical safety. BMJ Quality &

Safety, 28(3), 231–237.

Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer,

W. P. (2002). SMOTE: Synthetic Minority Over-

sampling Technique. Journal of Artificial Intelligence

Research, 16, 321–357. https://doi.org/10.1613/jair.953

Corsello, S. M., Bittker, J. A., Liu, Z., Gould, J., McCarren,

P., Hirschman, J. E., Johnston, S. E., Vrcic, A., Wong,

B., & Khan, M. (2017). The Drug Repurposing Hub: A

next-generation drug library and information resource.

Nature Medicine, 23(4), 405–408. https://www.nature.

com/articles/nm.4306

Clifford, G. D., Scott, D., & Villarroel, M. (2009). User

guide and documentation for the MIMIC II database.

Roosan, D., Chok, J., Li, Y., & Khou T. (2024). Utilizing

Quantum Computing-based Large Language

Transformer Models to Identify Social Determinants of

Health from Electronic Health Records. 2024

International Conference on Electrical, Computer and

Energy Technologies (ICECET, 1–6. https://doi.org/

10.1109/ICECET61485.2024.10698600

Deng, J., Yang, Z., Ojima, I., Samaras, D., & Wang, F.

(2022). Artificial intelligence in drug discovery:

DATA 2025 - 14th International Conference on Data Science, Technology and Applications

470

Applications and techniques. Briefings in

Bioinformatics, 23(1), bbab430. https://doi.org/10.10

93/bib/bbab430

Dhillon, V., Metcalf, D., & Hooper, M. (2017). Blockchain

in Health Care. In Blockchain Enabled Applications

(pp. 125–138). Apress, Berkeley, CA. https://link.

springer.com/chapter/10.1007/978-1-4842-3081-7_9

Doga, H. & et al. (2024). Towards quantum computing for

clinical trial design and optimization: A perspective on

new opportunities and challenges. https://doi.org/

10.48550/arXiv.2404.13113

Duan, Q., Reid, S. P., Clark, N. R., Wang, Z., Fernandez,

N. F., Rouillard, A. D., Readhead, B., Tritsch, S. R.,

Hodos, R., Hafner, M., Niepel, M., Sorger, P. K.,

Dudley, J. T., Bavari, S., Panchal, R. G., & Ma’ayan,

A. (2016). L1000CDS2: LINCS L1000 characteristic

direction signatures search engine. Npj Systems Biology

and Applications, 2(1), 16015. https://doi.org/1

0.1038/npjsba.2016.15

Graves, K. D., Hall, M. J., & Tercyak, K. P. (2018).

Introduction to the Special Issue on Clinical and Public

Health Genomics: Opportunities for translational

behavioral medicine research, practice, and policy.

Translational Behavioral Medicine, 8(1), Article 1.

https://doi.org/10.1093/tbm/ibx068

Islam, R., Mayer, J., & Clutter, J. (2016). Supporting novice

clinicians cognitive strategies: System design

perspective. IEEE-EMBS International Conference on

Biomedical and Health Informatics. IEEE-EMBS

International Conference on Biomedical and Health

Informatics, 2016, 509. https://doi.org/10.1109/BHI.

2016.7455946

Islam, R., Weir, C., & Del Fiol, G. (2016a). Clinical

Complexity in Medicine: A Measurement Model of

Task and Patient Complexity. Methods of Information

in Medicine, 55(01), 14–22. https://doi.org/

10.3414/ME15-01-0031

Islam, R., Weir, C., & Del Fiol, G. (2016b). Clinical

Complexity in Medicine: A Measurement Model of

Task and Patient Complexity. Methods of Information

in Medicine, 55(1), Article 1. https://doi.org/

10.3414/ME15-01-0031

Islam, R., Weir, C., & Fiol, G. D. (2014). Heuristics in

Managing Complex Clinical Decision Tasks in Experts’

Decision Making. 186–193. https://doi.org/10.1109/

ICHI.2014.32

Islam, R., Weir, C. R., Jones, M., Del Fiol, G., & Samore,

M. H. (2015). Understanding complex clinical

reasoning in infectious diseases for improving clinical

decision support design. BMC Medical Informatics and

Decision Making, 15(1), 101. https://doi.org/10.1186/

s12911-015-0221-z

Itri, J. N., & Patel, S. H. (2018). Heuristics and Cognitive

Error in Medical Imaging. American Journal of

Roentgenology, 210(5), Article 5. https://doi.org/

10.2214/AJR.17.18907

Jeyaraman, N., Jeyaraman, M., Yadav, S.,

Ramasubramanian, S., & Balaji, S. (n.d.).

Revolutionizing Healthcare: The Emerging Role of

Quantum Computing in Enhancing Medical

Technology and Treatment. Cureus, 16(8), e67486.

https://doi.org/10.7759/cureus.67486

Li, Y., Duche, A., Sayer, M. R., Roosan, D., Khalafalla, F.

G., Ostrom, R. S., Totonchy, J., & Roosan, M. R.

(2021). SARS-CoV-2 early infection signature

identified potential key infection mechanisms and drug

targets. BMC Genomics, 22(1), Article 1.

https://doi.org/10.1186/s12864-021-07433-4

Pandey, S., Bhushan, B., & Hameed, A. A. (2024).

Securing Healthcare 5.0: Zero-Knowledge Proof (ZKP)

and Post Quantum Cryptography (PQC) Solutions for

Medical Data Security. In C. K. K. Reddy, T. Sithole,

M. Ouaissa, Ö. ÖZER, & M. M. Hanafiah (Eds.), Soft

Computing in Industry 5.0 for Sustainability (pp. 339–

355). Springer Nature Switzerland. https://doi.org/

10.1007/978-3-031-69336-6_15

Park, K. (2019). A review of computational drug

repurposing. Translational and Clinical

Pharmacology, 27(2), 59–63. https://synapse.korea

med.org/articles/1130297

Roosan, D. (2023). Augmented Reality and Artificial

Intelligence: Applications in Pharmacy. In V.

Geroimenko (Ed.), Augmented Reality and Artificial

Intelligence: The Fusion of Advanced Technologies

(pp. 227–243). Springer Nature Switzerland.

https://doi.org/10.1007/978-3-031-27166-3_13

Roosan, D., Chok, J., Karim, M., Law, A. V., Baskys, A.,

Hwang, A., & Roosan, M. R. (2020). Artificial

Intelligence–Powered Smartphone App to Facilitate

Medication Adherence: Protocol for a Human Factors

Design Study. JMIR Res Protoc, 9(11), Article 11.

https://doi.org/10.2196/21659

Roosan, D., Hwang, A., Law, A. V., Chok, J., & Roosan,

M. R. (2020). The inclusion of health data standards in

the implementation of pharmacogenomics systems: A

scoping review. Pharmacogenomics, 21(16), 1191–

1202. https://doi.org/10.2217/pgs-2020-0066

Roosan, D., Hwang, A., & Roosan, M. R. (2021).

Pharmacogenomics cascade testing (PhaCT): A novel

approach for preemptive pharmacogenomics testing to

optimize medication therapy. The Pharmacogenomics

Journal, 21(1), 1–7. https://doi.org/10.1038/s41397-

020-00182-9

Roosan, D., Law, A. V., Karim, M., & Roosan, M. (2019).

Improving Team-Based Decision Making Using Data

Analytics and Informatics: Protocol for a Collaborative

Decision Support Design. JMIR Res Protoc, 8(11),

Article 11. https://doi.org/10.2196/16047

Roosan, D., Padua, P., Khan, R., Khan, H., Verzosa, C., &

Wu, Y. (2023). Effectiveness of ChatGPT in clinical

pharmacy and the role of artificial intelligence in

medication therapy management. Journal of the

American Pharmacists Association. https://doi.org/

10.1016/j.japh.2023.11.023

Roosan, D., Roosan, M. R., Kim, S., Law, A. V., & Sanine,

C. (2022). Applying Artificial Intelligence to create risk

stratification visualization for underserved patients to

improve population health in a community health

setting. https://doi.org/10.21203/rs.3.rs-1650806/v1

Quantum Gradient Optimized Drug Repurposing Prototype for Omics Data

471

Roosan, D., Weir, C., Samore, M., Jones, M., Rahman, M.,

Stoddard, G. J., & Del Fiol, G. (2017). Identifying

Complexity in Infectious Diseases Inpatient Settings:

An Observation Study. Journal of Biomedical

Informatics, 71 Suppl, S13–S21. https://doi.org/

10.1016/j.jbi.2016.10.018

Roosan, D., Wu, Y., Tatla, V., Li, Y., Kugler, A., Chok, J.,

& Roosan, M. R. (2022). Framework to enable

pharmacist access to health care data using Blockchain

technology and artificial intelligence. Journal of the

American Pharmacists Association, 62(4), 1124–1132.

https://doi.org/10.1016/j.japh.2022.02.018

Roosan, D., Wu, Y., Tran, M., Huang, Y., Baskys, A., &

Roosan, M. R. (2022). Opportunities to integrate

nutrigenomics into clinical practice and patient

counseling. European Journal of Clinical Nutrition.

https://doi.org/10.1038/s41430-022-01146-x

Sammani, A., Jansen, M., Linschoten, M., Bagheri, A., De

Jonge, N., Kirkels, H., Van Laake, L. W., Vink, A., Van

Tintelen, J. P., Dooijes, D., Te Riele, A. S. J. M.,

Harakalova, M., Baas, A. F., & Asselbergs, F. W.

(2019). UNRAVEL: Big data analytics research data

platform to improve care of patients with

cardiomyopathies using routine electronic health

records and standardised biobanking. Netherlands

Heart Journal, 27(9), 426–434. https://doi.org/10.10

07/s12471-019-1288-4

Sung, S.-F., Chen, K., Wu, D. P., Hung, L.-C., Su, Y.-H.,

& Hu, Y.-H. (2018). Applying natural language

processing techniques to develop a task-specific EMR

interface for timely stroke thrombolysis: A feasibility

study. International Journal of Medical Informatics,

112, 149–157. https://doi.org/10.1016/j.ijmedinf.20

18.02.005

Tomczak, K., Czerwińska, P., & Wiznerowicz, M. (2015).

Review<br>The Cancer Genome Atlas (TCGA): An

immeasurable source of knowledge. Contemporary

Oncology/Współczesna Onkologia, 2015(1), 68–77.

https://doi.org/10.5114/wo.2014.47136

Tukur, M., Saad, G., AlShagathrh, F. M., Househ, M., &

Agus, M. (2023). Telehealth interventions during

COVID-19 pandemic: A scoping review of

applications, challenges, privacy and security issues.

BMJ Health & Care Informatics, 30(1), Article 1.

https://doi.org/10.1136/bmjhci-2022-100676

Wu, S. T., Liu, H., Li, D., Tao, C., Musen, M. A., Chute, C.

G., & Shah, N. H. (2012). Unified Medical Language

System term occurrences in clinical notes: A large-scale

corpus analysis. Journal of the American Medical

Informatics Association, 19(e1), Article e1.

https://doi.org/10.1136/amiajnl-2011-000744

Yang, J., Jiang, Z., Benthin, F., Hanel, J., Fandrich, T., Joos,

R., Bauer, S., Kolatschek, S., Hreibi, A.,

Rugeramigabo, E. P., Jetter, M., Portalupi, S. L., Zopf,

M., Michler, P., Kück, S., & Ding, F. (2024). High-rate

intercity quantum key distribution with a

semiconductor single-photon source. Light: Science &

Applications,

13(1), 150. https://doi.org/10.1038/s413

77-024-01488-0

Yang, R., Tan, T. F., Lu, W., Thirunavukarasu, A. J., Ting,

D. S. W., & Liu, N. (2023). Large language models in

health care: Development, applications, and challenges.

Health Care Science, 2(4), Article 4.

https://doi.org/10.1002/hcs2.61

DATA 2025 - 14th International Conference on Data Science, Technology and Applications

472