Leili Shahriyari

Cell types are described in biomedical literature using diverse names, abbreviations, and phenotype phrases, which complicates their recognition and normalization. We developed CellExLink, an end-to-end pipeline that identifies cell-type mentions and normalizes them to Cell Ontology (CL) identifiers. The recognizer was fine-tuned and evaluated on five heterogeneous biomedical corpora spanning full-length articles, article excerpts, figure captions, abstracts, and anatomical text passages. These resources include fine-grained phenotype-defined populations, heterogeneous cell populations, and abbreviated mentions. Across the five corpora, CellExLink achieved macro-average exact- and relaxed-span F1 scores of 0.766 and 0.855, respectively. For CL identifier normalization on gold-standard mention spans, F1 scores ranged from 0.690 to 0.874. In strict end-to-end evaluation, which required both an exact mention span and the correct CL identifier, F1 scores ranged from 0.552 on a figure-caption corpus to 0.725 on a corpus of full-text article excerpts. CellExLink outperformed the evaluated off-the-shelf systems in cell mention recognition, CL identifier normalization, and end-to-end extraction. By converting unannotated biomedical text into cell-type spans linked to standardized CL identifiers, CellExLink provides a practical foundation for downstream applications, including literature curation, relation extraction, and knowledge graph construction.

This paper presents a unified six-state Continuous-Time Markov Chain (CTMC) framework for Chronic Kidney Disease (CKD) progression, with CKD stages 1–5 modeled as transient states and death as an absorbing state. Under a non-homogeneous CTMC formulation, we derive integral representations for transition probabilities, state distributions, sojourn times, and survival-related quantities. We then study the homogeneous case as a tractable baseline and provide explicit formulas for key quantities. Although the methodology is rooted in standard multi-state theory, these expressions are often left implicit in applied analyses; here they are written out explicitly within a unified CKD framework. We construct covariate-dependent transition rates through a proportional hazards structure, using the standard identification of cause-specific hazards with CTMC transition rates. We fit the time-homogeneous baseline model to 335,283 longitudinal observations from 21,100 synthetic electronic health record patients by maximum likelihood. In this synthetic cohort, the covariate model improves held-out log-likelihood per transition over the null model, with stable performance across 10-times-repeated 5-fold cross-validation, and reproduces the main population-level prevalence patterns. The transition-specific estimates can also be translated into sojourn-time and survival summaries. The model suggests that male sex is associated with faster progression across nearly all CKD transitions, and that hypertension shows a stage-dependent association, with lower estimated transition rates in early stages but a substantial acceleration of the Stage 4 to Stage 5 transition. Overall, the proposed framework provides a mathematically explicit approach for studying CKD trajectories from longitudinal health records.

Since cells are the main components of many biological and biomedical studies, cell-type extraction is an important task in biomedical text mining. However, current biomedical text-mining systems either do not explicitly support cell-type extraction, provide limited support for Cell Ontology normalization, or show limited performance in end-to-end cell-type extraction. These limitations can affect downstream tasks that depend on reliable cell-type information. Here, we present CellExLink, an end-to-end biomedical natural language processing pipeline designed specifically for cell-type recognition and Cell Ontology normalization in biomedical text. The pipeline is designed to improve extraction accuracy and practical usability in literature-mining workflows, while accounting for computational efficiency in its recognition and normalization design. We evaluate CellExLink across heterogeneous biomedical corpora and compare it with established and recent biomedical text-mining tools. The results show that CellExLink provides reliable cell-type recognition, Cell Ontology normalization, and end-to-end extraction across these corpora. By addressing the need for reliable end-to-end cell-type recognition and Cell Ontology normalization, CellExLink can support downstream tasks such as curation, search, relation extraction, and knowledge graph construction. Author summary: Cell types are central to biomedical research, but biomedical papers often use different names, abbreviations, and synonyms for the same cell type. This variation makes it difficult for automated processes to collect and compare cell-type information across papers. Reliable automated extraction is important because literature mining requires consistent cell-type identification before evidence from different studies can be searched, integrated, or reused. Existing off-the-shelf biomedical text-mining tools provide useful functionality, but their ability to support cell-type extraction remains limited and inconsistent. To address this gap, we developed CellExLink, a pipeline that finds cell-type entities in biomedical text and links them to standard Cell Ontology identifiers. We evaluated the pipeline on several biomedical corpora and compared it with existing tools that support cell-type extraction. Across these evaluations, CellExLink showed clear accuracy gains in both detecting cell-type entities and assigning correct standard identifiers. Together, these gains make CellExLink a powerful tool for extracting reliable standardized cell-type information from large collections of papers, supporting literature curation, relation extraction, knowledge graph construction, and studies of cell-type-specific roles in diseases, drug responses, and biological pathways.

The ovarian tumor microenvironment (TME) is highly immunosuppressive, limiting immunotherapy effectiveness. We investigated whether the composition, ratios, and polarization of infiltrating immune cells provide prognostic value in ovarian cancer (OC). Our analysis revealed that immune ratios, including CD8/Treg and CD8/CD4, were more predictive of survival than absolute CD8 + , CD4 + , or Treg levels. Prior studies have reported associations between higher CD8/Treg ratios and improved responses to immune checkpoint inhibitors, highlighting the importance of effector–regulator balance; however, immunotherapy response was not evaluated in this cohort. Additionally, macrophage polarization proved to be crucial: pro-tumor M2-macrophages were associated with vascular invasion, persistent tumor, and worse survival, while higher naïve M0-macrophage levels predicted improved outcomes. Surprisingly, anti-tumor M1-macrophages lacked prognostic significance, suggesting possible benefits in preserving an M0-macrophage pool. Neutrophil infiltration, though relatively uncommon, correlated with poor survival, supporting reports that neutrophils suppress T-cell responses. Immune infiltration was linked to aggressive features such as vascular invasion, reflecting both heightened recognition and compensatory recruitment of suppressive populations. Unsupervised clustering identified four immune-defined subtypes, with worse survival in clusters enriched for M2-macrophages and CD4 + T-cells and depleted in M0-macrophages, particularly in advanced disease. Overall, our findings highlight the prognostic value of immune ratios, macrophage polarization, and neutrophil activity in OC, suggesting new avenues for risk stratification and future therapeutic investigation.

Immune checkpoint inhibitors (ICIs) have significantly changed cancer therapy, yet their response rates remain relatively low. Identifying methods for robust prediction is crucial. This study evaluates the efficacy of gene-based methods for deriving predictive tumor-microenvironment scores in cancer patients, focusing on their performances in predicting survival outcomes and response to ICI therapy across various cancer types. The TIP Hot method demonstrated robustness as a predictive method for ICI response, particularly in Non-Small Cell Lung Cancer, Head and Neck Squamous Cell Carcinoma, and Urothelial Cancer. However, no score is robustly applicable to all cancer types. Therefore, significant challenges remain due to the variability of tumor biology and host immune responses, and universally applicable method should be further explored. Future research should aim to refine these predictive scoring methods through larger and more diverse datasets, and integrate advanced computational techniques to enhance predictive accuracy and utility in personalized cancer treatment.

Breast cancer is a challenging global health problem among women. This study investigates the intricate breast tumour microenvironment (TME) dynamics utilizing data from mammary-specific polyomavirus middle T antigen overexpression mouse models (MMTV-PyMT). It incorporates endothelial cells (ECs), oxygen and vascular endothelial growth factors (VEGF) to examine the interplay of angiogenesis, hypoxia, VEGF and immune cells in cancer progression. We introduce an approach to impute immune cell fractions within the TME using single-cell RNA-sequencing (scRNA-seq) data from MMTV-PyMT mice. We quantify our analysis by estimating cell counts using cell size data and laboratory findings from existing literature. We perform parameter estimation via a Hybrid Genetic Algorithm (HGA). Our simulations reveal various TME behaviours, emphasizing the critical role of adipocytes, angiogenesis, hypoxia and oxygen transport in driving immune responses and cancer progression. Global sensitivity analyses highlight potential therapeutic intervention points, such as VEGFs’ role in EC growth and oxygen transportation and severe hypoxia’s effect on cancer and the total number of cells. The VEGF-mediated production rate of ECs shows an essential time-dependent impact, highlighting the importance of early intervention in slowing cancer progression. These findings align with clinical observations demonstrating the VEGF inhibitors’ efficacy and suggest a timely intervention for better outcomes.

Breast cancer is one of the most challenging global health problems among women. This study investigates the intricate breast tumor microenvironment (TME) dynamics utilizing data from Mammary-specific Polyomavirus Middle T Antigen Overexpression mouse models (MMTV-PyMT). It incorporates Endothelial Cells (ECs), oxygen, and Vascular Endothelial Growth Factors (VEGF) to examine the interplay of angiogenesis, hypoxia, VEGF, and the immune cells in cancer progression. We introduce an approach to impute the immune cell fractions within the TME using single-cell RNA-sequencing (scRNA-seq) data from MMTV-PyMT mice. We further quantify our analysis by estimating cell counts using cell size data and laboratory findings from existing literature. Parameter estimation is carried out via a Hybrid Genetic Algorithm (HGA). Our simulations reveal various TME behaviors, emphasizing the critical role of adipocytes, angiogenesis, hypoxia, and oxygen transport in driving immune responses and cancer progression. The global sensitivity analyses highlight potential therapeutic intervention points, such as VEGFs' critical role in EC growth and oxygen transportation and severe hypoxia's effect on the cancer and the total number of cells. The VEGF-mediated production rate of ECs shows an essential time-dependent impact, highlighting the importance of early intervention in slowing cancer progression. These findings align with the observations from the clinical trials demonstrating the efficacy of VEGF inhibitors and suggest a timely intervention for better outcomes.

Predicting cancer drug response using both genomics and drug features has shown some success compared to using genomics features alone. However, there has been limited research done on how best to combine or fuse the two types of features. Using a visible neural network with two deep learning branches for genes and drug features as the base architecture, we experimented with different fusion functions and fusion points. Our experiments show that injecting multiplicative relationships between gene and drug latent features into the original concatenation-based architecture DrugCell significantly improved the overall predictive performance and outperformed other baseline models. We also show that different fusion methods respond differently to different fusion points, indicating that the relationship between drug features and different hierarchical biological level of gene features is optimally captured using different methods. Considering both predictive performance and runtime speed, tensor product partial is the best-performing fusion function to combine late-stage representations of drug and gene features to predict cancer drug response.

The use of digital twins (DTs) has proliferated across various fields and industries, with a recent surge in the healthcare sector. The concept of digital twin for health (DT4H) holds great promise to revolutionize the entire healthcare system, including management and delivery, disease treatment and prevention, and health well-being maintenance, ultimately improving human life. The rapid growth of big data and continuous advancement in data science (DS) and artificial intelligence (AI) have the potential to significantly expedite DT research and development by providing scientific expertise, essential data, and robust cybertechnology infrastructure. Although various DT initiatives have been underway in the industry, government, and military, DT4H is still in its early stages. This paper presents an overview of the current applications of DTs in healthcare, examines consortium research centers and their limitations, and surveys the current landscape of emerging research and development opportunities in healthcare. We envision the emergence of a collaborative global effort among stakeholders to enhance healthcare and improve the quality of life for millions of individuals worldwide through pioneering research and development in the realm of DT technology.