About

Zeeshan Ahmed is an Assistant Professor in the Department of Medicine at Rutgers University, affiliated with the Graduate Program in Microbiology & Molecular Genetics. His research focuses on disease genetics and gene regulation, utilizing techniques such as bioinformatics, genomics, metabolomics, and transcriptomics. His lab is dedicated to developing intelligent and multi-functional systems for integrative healthcare, analyzing genomics and metabolomics data to discover disease biomarkers and phenotypic information for advanced diagnostics and personalized treatment in Precision Medicine. His research objectives include modeling clinical, genomics, and metabolomics data to identify biological pathways, modifiable risk factors, and actionable information that support early detection and prevention of complex disorders, as well as the development of new therapies to improve patient care. Zeeshan Ahmed's work emphasizes the application of multi-omics, genomics, artificial intelligence, and machine learning to advance personalized medicine and healthcare.

Research topics

• Artificial Intelligence
• Computer Science
• Medicine
• Data Mining
• Data science
• Bioinformatics
• Machine Learning
• Knowledge management
• Computational biology
• Pharmacology
• Biology
• Pathology
• Genetics

Selected publications

• Clinical presentations and hematological alterations in malaria patients and their association with plasmodium species and disease severity at Ghambat Institute of Medical Sciences

  Infectious Diseases Journal of Pakistan · 2026-03-30

  articleOpen access1st authorCorresponding

  Objective: To identify the prevalence of the clinical manifestations and changes in the hematological parameters of patients with malaria and to study their correlation with the various Plasmodium species to identify species-specific trends associated with severity of disease. Material and Methods: The study is a descriptive cross-sectional study, which was carried out at the Department of General Medicine, Ghambat Institute of Medical Sciences, from 17th July 2025-17th January 2026. Consecutive sampling was used to enroll 162 patients aged between 18 and 70 years with malaria confirmed by peripheral blood smear. Hematological parameters and clinical characteristics were compared between different species of malaria. Results: A total of 151 patients with confirmed malaria were included (mean age 32.6 ± 14.1 years; 46.1% males). Plasmodium vivax was the most common species (60.3%), followed by P. falciparum (39.7%). All patients presented with fever with rigors/chills, while headache (81.5%) and nausea/vomiting (75.9%) were the most frequent accompanying symptoms. Thrombocytopenia (79.0%), anemia (57.4%), and high RDW (52.5%) were the most common hematological abnormalities. Anemia and low MCV were more frequently observed in P. falciparum infections and among males, whereas low MCHC was more common in females. Conclusion: The most common haematological changes in malaria patients are thrombocytopenia and anemia, and the presence of fever, headache, and nausea/vomiting is the most frequent presentation of the illness, especially Plasmodium vivax, which underscores the need to identify it at early stages and then treat the illness. Keywords: Anemia, Haematological changes, Malaria, Plasmodium vivax, Thrombocytopenia

  PublisherOA PDFDOI

• Exploring the Role of Artificial Intelligence for Revolutionizing English Language Learning of University-Level Students

  ACADEMIA International Journal for Social Sciences · 2025-07-01

  articleOpen access1st authorCorresponding

  This study explores the transformative potential of Artificial Intelligence (AI) in enhancing English language learning among university-level students. As AI technologies continue to advance, their integration into language education presents new opportunities for personalized, scalable, and interactive learning experiences. This research aims to evaluate how AI can reshape traditional English language pedagogy by examining its impact on core linguistic competencies such as vocabulary acquisition, pronunciation, and speaking confidence. A quantitative survey involving over 100 university students was conducted to assess perceptions and outcomes associated with AI-assisted learning tools. The results indicate a generally positive attitude toward AI applications, with participants reporting noticeable improvements in language proficiency. Nonetheless, the study also highlights key challenges, including limited access to AI resources and a lack of familiarity with available tools among both students and instructors. These barriers hinder the effective adoption of AI in language education. The findings underscore the importance of targeted training, infrastructure development, and strategic integration of AI within blended learning environments. This research contributes to the growing body of literature on educational technology by offering empirical insights into the benefits and limitations of AI in second language acquisition at the tertiary level.

  PublisherOA PDFDOI

• 563 Change in liver function markers in pediatric and adolescent patients with cystic fibrosis on elexacaftor/tezacaftor/ivacaftor (ETI) therapy

  Journal of Cystic Fibrosis · 2025-10-01

  article1st authorCorresponding
  PublisherDOI

• Abstract Wed100: Multimodal AI/ML for discovering novel biomarkers and predicting disease using multi-omics profiles of patients with cardiovascular diseases

  Circulation Research · 2025-08-01

  articleSenior author

  Cardiovascular diseases (CVDs) are complex, multifactorial conditions that require personalized assessment and treatment. Advancements in multi-omics technologies, most importantly whole-genome sequencing (WGS) and RNA sequencing (RNA-seq) have provided translational researchers with a comprehensive view of the human genome and transcriptome. The efficient synthesis and analysis of multimodal data that characterizes genetic variants alongside expression patterns linked to emerging phenotypes, can reveal novel biomarkers and enable the segmentation of patient populations based on personalized risk factors. In this study, we present a cutting-edge and groundbreaking methodology rooted in the integration of traditional bioinformatics, classical statistics, and multimodal artificial intelligence (AI) and machine learning (ML) techniques. Our approach has the potential to uncover the intricate mechanisms underlying CVD, enabling patient-specific risk and response profiling. We sourced transcriptomic expression data and single nucleotide polymorphisms (SNPs) from both CVD patients and healthy controls. By integrating these multi-omics datasets with clinical and demographic information, we generated patient-specific profiles. Utilizing our robust feature selection approach, we identified a signature of transcriptomic features and SNPs that are effective predictors of CVD. Differential expression analysis, combined with minimum redundancy maximum relevance feature selection, highlighted biomarkers that explain the disease phenotype. This approach prioritizes both biological relevance and efficiency in ML. We employed Combination Annotation Dependent Depletion (CADD) scores and allele frequencies to identify variants with pathogenic characteristics in CVD patients. Classification models trained on this signature demonstrated high-accuracy predictions for CVD. The best performing of these models was an XGBoost classifier optimized via Bayesian hyperparameter tuning, which was able to correctly classify all patients in our test dataset. Using SHapley Additive exPlanations, we created risk assessments for patients, offering further contextualization of these predictions in a clinical setting. A comprehensive literature review revealed that a substantial portion of the diagnostic biomarkers identified have previously been associated with CVD. Summarized results are presented in the figure attached.

  PublisherDOI

• A Random Forest Approach for Real-Time Sentiment Analysis of Twitter Data

  SSRN Electronic Journal · 2025-01-01

  preprintOpen accessSenior author
  PublisherDOI

• 3D IntelliGenes: AI/ML application using multi-omics data for biomarker discovery and disease prediction with multi-dimensional visualization

  BMC Medical Research Methodology · 2025-08-08 · 6 citations

  articleOpen accessSenior author

  The cutting-edge artificial intelligence (AI) and machine learning (ML) techniques have proven effective at uncovering elucidative knowledge on disease-causing biomarkers and the biological underpinnings of a plethora of human diseases. However, the high-dimensional nature of multi-omics data presents numerous challenges in its effective presentation, annotation, and interpretation. Traditional 2D visualizations often fall short in capturing the intricate relationships between multi-omics features, hindering our ability to identify meaningful correlations. In this study, we focused on addressing such challenges by developing an innovative solution to better visualize results produced by AI/ML approaches on integrated clinical and multi-omics data for novel biomarker discovery and predictive analysis. We present an advanced version of our earlier published software with intuitive and interactive visualizations of multi-omics data in multi-dimensions i.e., 3D IntelliGenes, which offers deeper insights, most importantly by capturing greater variability in the patient data by understanding both linear and non-linear structures, evaluating AI/ML model performance, and delineating the joint impact of biomarkers on the corresponding disease states. The overall functionality of 3D IntelliGenes is divided into two modules, data clustering and feature plotting. The data clustering module creates configurable 3D scatter plots to visualize the structure-preserving distribution of disease states, AI/ML classifier bias in the form of type I/II errors, and patient similarity through a robust density-driven clustering algorithm. Whereas the feature plotting module supports the joint analysis of pairs of multi-omics features to analyze the interdependence and discriminative power of co-expressed biomarkers. We report evaluated performance of 3D IntelliGenes using diverse cohorts of patients with cardiovascular and other diseases.

  PublisherOA PDFDOI

• Omics approaches to understand cardiovascular disease

  BMC Cardiovascular Disorders · 2025-12-18

  editorialOpen access1st authorCorresponding

  Omics approaches have emerged as indispensable tools in unravelling the intricate molecular landscape of cardiovascular disease (CVD) by providing comprehensive insights into the underlying mechanisms driving CVD pathogenesis, progression, and response to therapy. Integrative omics approaches further enhance our understanding by integrating multi-omics data to explain complex molecular networks and identify novel disease pathways. In this collection of BMC Cardiovascular Disorders, we invited submissions on omics approaches to investigate and understand CVD. Successfully achieving the goals of this collection, we were able to publish some interesting and impactful research articles after a rigorous peer review process.

  PublisherOA PDFDOI

• Chronic Neutrophilic Leukocytosis and Elevated Liver Enzymes with Persistent Body Ache: A Diagnostic Challenge

  Physical Education Health and Social Sciences · 2025-04-11

  articleOpen access1st authorCorresponding

  A 20-year-old nursing student reported experiencing intense body pains for the last two years, which worsened at night but showed some relief during the day. He also had elevated liver enzymes (SGPT: 216 U/L), and neutrophilic leukocytosis. Despite undergoing multiple tests, including bone marrow biopsy, genetic analysis (NGS panel of BCR-ABL, JAK2, and myeloid genes, and an autoimmune profile, a definitive diagnosis could not be reached. The patient developed body rashes, weight gain, high blood pressure, and mood swings, likely due to prolonged use of multiple antibiotics and, more recently corticosteroids. The patient’s body aches and rashes have persisted as chronic symptoms. The challenges highlight the challenges in the diagnosis of individuals with unexplained neutrophilic leukocytosis and underscore the need for further studies on the etiology, including lymph node involvement.

  PublisherOA PDFDOI

• Editorial: Computational genomic and precision medicine

  Frontiers in Genetics · 2025-05-29

  editorialOpen access1st authorCorresponding

  Misdiagnosis has been reported among the leading causes of death, along with cancer, heart disease, and respiratory diseases [1]. A fair amount of literature has been published in impactful peerreviewed journals, which discuss medication error and delayed treatment, and is accessible through authentic resources (e.g., PubMed) [2]). One of the most trending subjects in life sciences, which addresses these issues and contributes to providing personalized treatment to patients, is genomic and precision medicine [3]. It involves patient engagement, analyzing medical records to examine provided diagnoses and treatment outcomes, and investigating the genomic profile to understand disease mechanisms and propose better treatments [4]. Furthermore, it promotes integrating and analyzing different kinds of patient data (e.g., clinical, sociodemographic, behavioral, biomedical image, and multi-omics) to form multimodal data to discover important risk factors and biomarkers, which could be used to prevent and predict diseases [5]. This research topic focuses on gathering the most up-to-date knowledge on recent advances in analytical approaches, including deep and machine learning models for identifying disease-associated genes and rare variants, and predicting the best treatment outcomes for genomic and precision medicine. Successfully achieving the goals of this research topic, we were able to publish five interesting peer-reviewed articles.In, "SAFE-MIL: a statistically interpretable framework for screening potential targeted therapy patients based on risk estimation", Guan et al. set out to construct a generalizable framework for risk assessment of treatment failure for Non-Small Cell Lung Cancer (NSCLC) patients receiving epidermal growth factor receptor tyrosine kinase inhibitor-based treatment. Currently, patients with NSCLC who have the same target gene mutation experience vastly different treatment outcomes, largely due to varying mutation abundance levels and drug sensitivity that existing models don't account for, leading to black boxes and misalignment with Food and Drug Administration (FDA) standards, weakening the clinical applicability of machine learning (ML)driven drug prediction models. This study utilized three independent patient cohorts, implementing We are grateful to the Frontiers, Frontiers in Genetics, and editorial staff for their endless support in 104 the preparation, study collection, peer-review processes, editing, press, and publication process 105 involved in this research topic. We thank the honorable reviewers for their time and constructive 106 suggestions to the authors for the possible quality and scientific improvements to their studies. 107The authors declare that the research was conducted without any commercial or financial 109 relationships that could potentially create a conflict of interest. 110

  PublisherOA PDFDOI

• Efficient control and removal of laser-generated aerosol particles by combining water spray with pre-injection of electrical charged mist for nuclear reactor decommissioning

  Nuclear Science and Techniques · 2025-12-07 · 6 citations

  articleOpen access

  Abstract Laser-induced aerosols, predominantly submicron in size, pose significant environmental and health risks during the decommissioning of nuclear reactors. This study experimentally investigated the removal of laser-generated aerosol particles using a water spray system integrated with an innovative system for pre-injecting electrically charged mist in our facility. To simulate aerosol generation in reactor decommissioning, a high-power laser was used to irradiate various materials (including stainless steel, carbon steel, and concrete), generating aerosol particles that were agglomerated with injected water mist and subsequently scavenged by water spray. Experimental results demonstrate enhanced aerosol removal via aerosol-mist agglomeration, with charged mist significantly improving particle capture by increasing wettability and size. The average improvements for the stainless steel, carbon steel, and concrete were 40%, 44%, and 21%, respectively. The results of experiments using charged mist with different polarities (both positive and negative) and different surface coatings reveal that the dominant polarity of aerosols varies with the irradiated materials, influenced by their crystal structure and electron emission properties. Notably, surface coatings such as ZrO 2 and CeO 2 were found to possibly alter aerosol charging characteristics, thereby affecting aerosol removal efficiency with charged mist configurations. The innovative aerosol-mist agglomeration approach shows promise in mitigating radiation exposure, ensuring environmental safety, and reducing contaminated water during reactor dismantling. This study contributes critical knowledge for the development of advanced aerosol management strategies for nuclear reactor decommissioning. The understanding obtained in this work is also expected to be useful for various environmental and chemical engineering applications such as gas decontamination, air purification, and pollution control.

  PublisherOA PDFDOI

Frequent coauthors

• Saman Zeeshan

  University of Missouri–Kansas City

  79 shared

• Habiba Abdelhalim

  Rutgers, The State University of New Jersey

  44 shared

• Bruce T. Liang

  UConn Health

  38 shared

• Thomas Dandekar

  30 shared

• XinQi Dong

  Rutgers, The State University of New Jersey

  28 shared

• William DeGroat

  Rutgers, The State University of New Jersey

  27 shared

• Shayan Ahmed

  Shaheed Mohtarma Benazir Bhutto Medical University

  25 shared

• Salman Ahmed

  University of Sindh

  25 shared