Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “PATIENTS”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,531 records · Page 85
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Domain Shift Analysis in Chest Radiographs Classification in a Veterans Healthcare Administration Population

This study aims to assess the impact of domain shift on chest X-ray classification accuracy and to analyze the influence of ground truth label quality and demographic factors such as age group, sex, and study year. We used a DenseNet121 model pre-trained MIMIC-CXR dataset for deep learning-based multi-label classification using ground truth labels from radiology reports extracted using the CheXpert and CheXbert Labeler. We compared the performance of the 14 chest X-ray labels on the MIMIC-CXR and Veterans Healthcare Administration chest X-ray dataset (VA-CXR). The validation of ground truth and the assessment of multi-label classification performance across various NLP extraction tools revealed that the VA-CXR dataset exhibited lower disagreement rates than the MIMIC-CXR datasets. Additionally, there were notable differences in AUC scores between models utilizing CheXpert and CheXbert. When evaluating multi-label classification performance across different datasets, minimal domain shift was observed in the unseen VA dataset, except for the label “Enlarged Cardiomediastinum.” The subgroup with the most significant variations in multi-label classification performance was study year. These findings underscore the importance of considering domain shift in chest X-ray classification tasks, paying particular attention to the temporality of the exam. Our study reveals the significant impact of domain shift and demographic factors on chest X-ray classification, emphasizing the need for improved transfer learning and robust model development. Addressing these challenges is crucial for advancing medical imaging research and improving patient care.

chest X-ray image classification↗
DOE OSTI2019

Therapeutic effects of moxibustion simultaneously targeting Nrf2 and NF-κB in diabetic peripheral neuropathy

Moxibustion is the main alternative medicine treatment that has been beneficial to diabetic peripheral neuropathy (DPN), a common complication secondary to diabetic microvascular injury. However, the underlying protective mechanism of moxibustion against neuroinflammation remains unclear. We hypothesized that moxibustion treats DPN by regulating the balance of nuclear factor-2 erythroid-related factor-2 (Nrf2)-nuclear factor-kappa light chain enhancer of B cells (NF-кB). In vivo, diabetes was induced in rats by injecting streptozotocin (STZ; 60 mg/kg; i.p.). Moxibustion was then applied to “Zusanli” (ST 36), “Guanyuan” (BL 26), and “Yishu” (EX-B 3) acupuncture points. Nerve conduction was detected. Serum interleukin (IL)-1β, IL-6, and IL-8 levels were determined through enzyme-linked immunosorbent assay. NF-κB and Nrf2 proteins were examined through immunoblot analysis. The mRNA of NF-κB and Nrf2 was evaluated through RT-PCR. We found that the conduction velocity and amplitude of the action potentials of sciatic nerve conduction were reduced in the DPN model group but were rescued by moxibustion treatment. Moxibustion also improved the effect of DPN on other parameters, including ultrastructural changes, NF-κB and Nrf2 expression in the sciatic nerve, and serum IL-1β, IL-6, and IL-8 levels. Our data suggested that moxibustion may alleviate neuroinflammation by inhibiting NF-κB and by activating Nrf2. Moxibustion may also provide therapeutic effects for patients with DPN by simultaneously targeting Nrf2 and NF-κB.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Post-Combat-Injury Opioid Prescription and Alcohol Use Disorder in the Military

Previous studies have identified combat exposure and combat traumatic experience as problematic drinking risk factors. Increasing evidence suggests that opioid use increases the risk of alcohol use disorder. This study investigated the association between opioid prescription use after injury and (1) alcohol use disorder and (2) severity of alcohol use disorder among deployed military servicemembers. Deidentified health records data of 9,029 deployed servicemembers from a retrospective cohort study were analyzed. Data were randomly selected from the Department of Defense Trauma Registry and included servicemembers with combat injuries during deployment in Iraq or Afghanistan (2002–2016). Pharmacy records and International Classification of Diseases, Ninth and Tenth Revision diagnosis codes were used. Three groups were identified (no opioid prescription use, nonpersistent opioid prescription use, and persistent opioid prescription use) and were compared on the basis of alcohol use disorder risk using Cox proportional hazard models. Data analyses were performed in 2021. Of the 9,029 servicemembers with combat injury, 2,262 developed alcohol use disorder (1,322 developed severe alcohol use disorder). Compared with no opioid prescription use, increased alcohol use disorder risk was associated with persistent opioid prescription use, with a hazard ratio of 1.13 (95% CI=1.02, 1.26). After covariate adjustment, increased risk remained statistically significant (hazards ratio=1.24; 95% CI=1.10, 1.39). There was no significant difference in alcohol use disorder risk between no opioid prescription use and nonpersistent opioid prescription use. The risk of severe alcohol use disorder did not vary by opioid use among servicemembers with alcohol use disorder diagnosis. Furthermore, the findings of the study suggest that the incidence of alcohol use disorder was higher among injured servicemembers with persistent opioid prescription use than among those without opioid use. If replicated in prospective studies, the findings highlight the need for clinicians to consider the current and history of alcohol use of patients in initiating treatment involving opioids.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

2.5 Å-resolution structure of human CDK-activating kinase bound to the clinical inhibitor ICEC0942

The human CDK-activating kinase (CAK), composed of CDK7, cyclin H, and MAT1, is involved in the control of transcription initiation and the cell cycle. Because of these activities, it has been identified as a promising target for cancer chemotherapy. A number of CDK7 inhibitors have entered clinical trials, among them ICEC0942 (also known as CT7001). Structural information can aid in improving the affinity and specificity of such drugs or drug candidates, reducing side effects in patients. Here, we have determined the structure of the human CAK in complex with ICEC0942 at 2.5 Å-resolution using cryogenic electron microscopy. Our structure reveals conformational differences of ICEC0942 compared with previous X-ray crystal structures of the CDK2-bound complex, and highlights the critical ability of cryogenic electron microscopy to resolve structures of drug-bound protein complexes without the need to crystalize the protein target.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Multiscale simulations reveal TDP-43 molecular-level interactions driving condensation

The RNA-binding protein TDP-43 is associated with mRNA processing and transport from the nucleus to the cytoplasm. TDP-43 localizes in the nucleus as well as accumulating in cytoplasmic condensates such as stress granules. Aggregation and formation of amyloid-like fibrils of cytoplasmic TDP-43 are hallmarks of numerous neurodegenerative diseases, most strikingly present in >90% of amyotrophic lateral sclerosis (ALS) patients. If excessive accumulation of cytoplasmic TDP-43 causes, or is caused by, neurodegeneration is presently not known. In this work, we use molecular dynamics simulations at multiple resolutions to explore TDP-43 self- and cross-interaction dynamics. A full-length molecular model of TDP-43, all 414 amino acids, was constructed from select structures of the protein functional domains (N-terminal domain, and two RNA recognition motifs, RRM1 and RRM2) and modeling of disordered connecting loops and the low complexity glycine-rich C-terminus domain. All-atom CHARMM36m simulations of single TDP-43 proteins served as guides to construct a coarse-grained Martini 3 model of TDP-43. The Martini model and a coarser implicit solvent Cα model, optimized for disordered proteins, were subsequently used to probe TDP-43 interactions; self-interactions from single-chain full-length TDP-43 simulations, cross-interactions from simulations with two proteins and simulations with assemblies of dozens to hundreds of proteins. Our findings illustrate the utility of different modeling scales for accessing TDP-43 molecular-level interactions and suggest that TDP-43 has numerous interaction preferences or patterns, exhibiting an overall strong, but dynamic, association and driving the formation of biomolecular condensates.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

The AML microenvironment catalyzes a step-wise evolution to gilteritinib resistance

This study reports a stepwise evolution of gilteritinib resistance in acute myeloid leukemia (AML) relapse, and identifies therapeutic vulnerabilities that could be exploited. Our in vitro model demonstrates that extrinsic microenvironmental factors within the bone marrow nurture the growth of residual cells that underlie early gilteritinib resistance. These early resistant cells exhibit a dependency upon Aurora B (AURKB) and undergo dynamic metabolic adaptations that fostered their transition to late resistance. In line with clinical observations of gilteritinib resistance, late resistant cells displayed an expansion of NRAS mutant subclones and a unique metabolic profile that originated during early resistance. Pharmacological inhibition of AURKB resensitized early resistant cells to gilteritinib. Early resistant cells from AML patients were found to display exquisite sensitivitye to AURKB inhibition. Together, our findings support that eradication of early resistant AML cells may increase the efficacy and durability of gilteritinib.

AML, Proteomics↗
DOE OSTI2021

CD63-mediated cloaking of VEGF in small extracellular vesicles contributes to anti-VEGF therapy resistance

Despite wide use of anti-vascular endothelial growth factor (VEGF) therapy for many solid 30 cancers, most of them become resistant to this therapy and develop into progressive disease. 31 Therefore, new biomarkers and strategies for blocking adaptive resistance of cancer to anti-VEGF therapy are needed. As described herein, we found that cancer-derived small extracellular vesicles (EVs) package increasing quantities of VEGF and other factors in response to anti-VEGF therapy. Notably, small EV-VEGF (eVEGF) is not recognized by bevacizumab and can be transferred to endothelial cells and trigger intracrine signaling and promote angiogenesis. Also, serum eVEGF was present at higher levels in ovarian cancer mouse models with adaptive resistance to bevacizumab than in models sensitive to it. Ovarian cancer cell-derived eVEGF increased tumor growth despite treatment with bevacizumab. Notably, the eVEGF level was elevated in patient serum after bevacizumab-containing therapy. Proteomic data of small EVs from mouse models suggested that several novel biomarkers are promising for drug response prediction. Collectively, these data demonstrated a new mechanism whereby eVEGF evades recognition by therapeutic antibodies and promotes tumor angiogenesis and progression. These findings have clinical implications for biomarkers and new therapeutic strategies for ovarian cancer.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

An antibody-free platform for multiplexed, sensitive quantification of protein biomarkers in complex biomatrices

Sensitive, multiplexed protein quantification remains challenging despite recent advancements in LC-MS assays for targeted protein biomarker quantification. High-sensitivity protein biomarker measurements usually require immuno-affinity enrichment of target protein; a process which is highly dependent on capture reagent and limited in capability to measure multiple analytes. Herein, we report a novel antibody-free platform, which measures multiple biomarkers from complex matrices employing a strategically optimized solid-phase extraction cleanup and orthogonal multidimensional LC-MS. Eight human protein biomarkers with different specifications were spiked into canine plasma as a model investigation system. The developed strategy achieved the desired sensitivity, robustness, and throughput via the following steps: (1) post digestion mixed-mode cation exchange-reverse phase SPE enrichment cleaned up the sample initially; (2) rapid, high-pH peptide fractionation further eliminated background components efficiently while selectively enriched signature peptides (SP) to provide sufficient sensitivity for multiple targets; and (3) trapping-micro-LC-MS analysis delivered high sensitivity comparable to a nano-LC-MS method but with much better robustness and throughput for the final analysis. Compared with a conventional LC-MS assay with direct protein digestion and limited clean-up, analysis with this antibody-free platform improved the LLOQ by 1–2 orders of magnitude for the eight protein biomarkers, reaching as low as 5 ng/mL in plasma, with feasible robustness and throughput. In conclusion, this platform was applied for the quantification of biomarkers of respiratory conditions in patients with various lung diseases, demonstrating real-world applicability.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A k-mer based approach for classifying viruses without taxonomy identifies viral associations in human autism and plant microbiomes

Viruses are an underrepresented taxa in the study and identification of microbiome constituents; however, they play an essential role in health, microbiome regulation, and transfer of genetic material. Only a few thousand viruses have been isolated, sequenced, and assigned a taxonomy, which limits the ability to identify and quantify viruses in the microbiome. Additionally, the vast diversity of viruses represents a challenge for classification, not only in constructing a viral taxonomy, but also in identifying similarities between a virus’ genotype and its phenotype. However, the diversity of viral sequences can be leveraged to classify their sequences in metagenomic and metatranscriptomic samples, even if they do not have a taxonomy. To identify and quantify viruses in transcriptomic and genomic samples, we developed a dynamic programming algorithm for creating a classification tree out of 715,672 metagenome viruses. To create the classification tree, we clustered proportional similarity scores generated from the k-mer profiles of each of the metagenome viruses to create a database of metagenomic viruses. The resulting Kraken2 database of the metagenomic viruses can be found here: https://www.osti.gov/biblio/1615774 and is compatible with Kraken2. We then integrated the viral classification database with databases created with genomes from NCBI for use with ParaKraken (a parallelized version of Kraken provided in Supplemental Zip 1), a metagenomic/transcriptomic classifier. To illustrate the breadth of our utility for classifying metagenome viruses, we analyzed data from a plant metagenome study identifying genotypic and compartment specific differences between two Populus genotypes in three different compartments. We also identified a significant increase in abundance of eight viral sequences in post mortem brains in a human metatranscriptome study comparing Autism Spectrum Disorder patients and controls. We also show the potential accuracy for classifying viruses by utilizing both the JGI and NCBI viral databases to identify the uniqueness of viral sequences. Finally, we validate the accuracy of viral classification with NCBI databases containing viruses with taxonomy to identify pathogenic viruses in known COVID-19 and cassava brown streak virus infection samples. Our method represents the compulsory first step in better understanding the role of viruses in the microbiome by allowing for a more complete identification of sequences without taxonomy. Better classification of viruses will improve identifying associations between viruses and their hosts as well as viruses and other microbiome members. Despite the lack of taxonomy, this database of metagenomic viruses can be used with any tool that utilizes a taxonomy, such as Kraken, for accurate classification of viruses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Emerging anomaly detection techniques for electronic health records: A survey

Background Anomaly detection in electronic health records (EHRs) is a cornerstone of biomedical informatics, with direct implications for patient safety, clinical decision-making, and the prevention of healthcare fraud. Once guided primarily by simple rule-based methods, the field has advanced rapidly, driven by increased computing power, richer and more detailed health data, and the rise of machine learning and deep learning techniques. The objective of this paper is to provide a comprehensive overview of modern approaches to detecting anomalies in EHRs, outlining their strengths, limitations, and relevance to key healthcare challenges. We review traditional statistical methods alongside newer ML- and DL-based strategies and hybrid models, with particular attention to how these techniques support transparency and build clinical trust. Methods This paper presents a thorough and critical survey through systematic review (PRISMA-based) of the latest anomaly detection strategies in time-sequence data domains within electronic health record systems. Results We explore a broad spectrum of methodologies, including statistical models, supervised and unsupervised learning approaches, hybrid frameworks, and state-of-the-art ML-based techniques that collectively advance the precision and scalability of detecting anomalies in complex clinical datasets. In addition to mapping current capabilities, we address the enduring challenges that hinder widespread implementation and provide a forward-looking perspective on the future of anomaly detection in the data-rich landscape of modern healthcare. Summary The advancement in AI-based approaches is reported along with the basic principles of the individual approaches and their applicability. The increased availability of high-quality data, advancements in DL approaches, and enhanced computation power are leading to more frequent adaptation of DL-based approaches. Emerging DL-based approaches that have been adapted in other domains or recently applied in the EHR domain are also discussed in detail. Although DL-based approaches can improve model predictions by incorporating comorbidities, their application is limited in low-frequency data domains (e.g., when the total available data remains in the single digits). Therefore, the user must carefully consider the application based on data availability.

Anomaly detection↗
DOE OSTI2021

Benchmarking blockchain-based gene-drug interaction data sharing methods: A case study from the iDASH 2019 secure genome analysis competition blockchain track

Blockchain distributed ledger technology is just starting to be adopted in genomics and healthcare applications. Despite its increased prevalence in biomedical research applications, skepticism regarding the practicality of blockchain technology for real-world problems is still strong and there are few implementations beyond proof-of-concept. We focus on benchmarking blockchain strategies applied to distributed methods for sharing records of gene-drug interactions. We expect this type of sharing will expedite personalized medicine. We generated gene-drug interaction test datasets using the Clinical Pharmacogenetics Implementation Consortium (CPIC) resource. We developed three blockchain-based methods to share patient records on gene-drug interactions: Query Index, Index Everything, and Dual-Scenario Indexing. We achieved a runtime of about 60 s for importing 4,000 gene-drug interaction records from four sites, and about 0.5 s for a data retrieval query. Our results demonstrated that it is feasible to leverage blockchain as a new platform to share data among institutions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Leveraging large language models to automate the identification of healthcare access barriers for veterans

Objective: To develop and evaluate an automated system for identifying healthcare barriers focusing on transportation issues in veterans’ clinical notes using large language models (LLMs) and to assess the impact of different prompting strategies on classification performance and explanation consistency. Methods: We developed a hybrid system combining pattern matching for templated notes with LLM analysis for free-text notes. Using 2000 manually annotated clinical notes, we compared four prompting strategies (dual-role short, dual-role long, analysis-first, analysis-only) across Mistral-7B and Llama-3.1 models. We evaluated classification performance using standard metrics and assessed explanation consistency through embedding similarity analysis. Results: The analysis-first strategy achieved superior performance, with Mistral-7B reaching an F1 score of 0.914, outperforming traditional machine learning approaches (GBM: 0.786, BERT: 0.811). LLMs demonstrated higher explanation consistency within models (mean cosine similarity 0.887–0.908) compared to cross-model similarities (0.767–0.872). Pattern matching successfully handled 6.7% of templated notes deterministically. Mistral-7B showed greater internal consistency but higher abstention rates compared to Llama-3.1. Conclusion: Requiring LLMs to analyze evidence before classification improves both accuracy and explanation consistency for identifying transportation barriers in clinical notes. This approach enables automated barrier detection at scale while providing clinically relevant explanations, supporting both population-level healthcare planning and individual patient care decisions.

Healthcare access barriers↗
DOE OSTI2021

Preclinical Risk Evaluation of Normal Tissue Injury With Novel Radiosensitizers

Genotoxic damage induced by radiation triggers a highly coordinated DNA damage response, and molecular inhibitors of key nodes within this complex response network can profoundly enhance the antitumor efficacy of radiation. This is especially true for drugs targeting the catalytic subunit of DNA-dependent protein kinase, which is a core component of the nonhomologous end-joining DNA repair pathway, and ataxia telangiectasia mutated, which coordinates cell cycle arrest, apoptosis, and DNA repair functionalities after radiation exposure. Unlike the more modest in vitro radiosensitizing effects seen with classic sensitizing agents such as cisplatin, 5-fluorouracil, or taxanes, DNA-dependent protein kinase or ataxia telangiectasia mutated inhibitors provide much more robust sensitizing effects in vitro, as might be anticipated from targeting these key DNA repair modulators. However, patients with homozygous inactivating mutations of ataxia telangiectasia mutated or mice with homozygous defects in DNA-dependent protein kinase (severe combined immunodeficiency) have profoundly enhanced acute normal tissue radiation reactions. Therefore, there is significant potential that the combination of small molecule inhibitors of these kinases with radiation could cause similar dose-limiting acute normal tissue toxicities. Similarly, although less understood, inhibition of these DNA repair response pathways could markedly increase the risk of late radiation toxicities. Because these potent radiosensitizers could be highly useful to improve local control of otherwise radiation-resistant tumors, understanding the potential for elevated risks of radiation injury is essential for optimizing therapeutic ratio and developing safe and informative clinical trials. In this review, we will discuss 2 straightforward models to assess the potential for enhanced mucosal toxicity in the oral cavity and small intestine established in our laboratories. We also will discuss similar strategies for evaluating potential drug–radiation interactions with regard to increased risks of debilitating late effects.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Moving Beyond the Standard of Care: Accelerate Testing of Radiation-Drug Combinations

Radiation therapy is a major treatment modality used in > 60% of cancer patients as definitive local treatment for inoperable locoregionally confined tumors and as palliative therapy. Although cytotoxic chemotherapy enhances the effectiveness of treatment, the benefit over radiation therapy alone is modest. There is a need to enhance the effectiveness of local tumor control over what sequentially or concurrently administered cytotoxic chemotherapy provides. Although many biological pathways are known to enhance the effectiveness of radiation therapy, there is currently a paucity of drugs approved for use in combination. Several clinical trials have tested the effectiveness of combining targeted agents or immunotherapies with radiation therapy, but the results of these trials have been negative, likely stemming from the relative lack of preclinical evidence using appropriate experimental standardization or model systems. Accelerating the identification of agents tested in an appropriate clinical context and experimental systems or models would greatly enhance the potential to bring forward early testing of drugs that would not only be safe but also more effective. This article provides an overview of the opportunities and challenges of developing therapeutics to combine with radiation therapy, and some guidance toward preclinical and early clinical testing to improve the chance that advanced phase testing of drug-radiation combinations would be successful in the long term.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Analysis of antibodies from HCV elite neutralizers identifies genetic determinants of broad neutralization

The high genetic diversity of hepatitis C virus (HCV) complicates effective vaccine development. We screened a cohort of 435 HCV-infected individuals and found that 2%–5% demonstrated outstanding HCV-neutralizing activity. From four of these patients, we isolated 310 HCV antibodies, including neutralizing antibodies with exceptional breadth and potency. High neutralizing activity was enabled by the use of the VH1-69 heavy-chain gene segment, somatic mutations within CDRH1, and CDRH2 hydrophobicity. Structural and mutational analyses revealed an important role for mutations replacing the serines at positions 30 and 31, as well as the presence of neutral and hydrophobic residues at the tip of the CDRH3. Based on these characteristics, we computationally created a de novo antibody with a fully synthetic VH1-69 heavy chain that efficiently neutralized multiple HCV genotypes. Finally, our findings provide a deep understanding of the generation of broadly HCV-neutralizing antibodies that can guide the design of effective vaccine candidates.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Human anti-N1 monoclonal antibodies elicited by pandemic H1N1 virus infection broadly inhibit HxN1 viruses in vitro and in vivo

Neuraminidase (NA) is one of the two influenza virus surface glycoproteins, and antibodies that target it are an independent correlate of protection. However, our current understanding of NA antigenicity is incomplete. Here, in this work, we describe human monoclonal antibodies (mAbs) from a patient with a pandemic H1N1 virus infection in 2009. Two mAbs exhibited broad reactivity and inhibited NA enzyme activity of seasonal H1N1 viruses circulating before and after 2009, as well as viruses with avian or swine N1s. The mAbs provided robust protection from lethal challenge with human H1N1 and avian H5N1 viruses in mice, and both target an epitope on the lateral face of NA. In summary, we identified two broadly protective NA antibodies that share a novel epitope, inhibited NA activity, and provide protection against virus challenge in mice. Our work reaffirms that NA should be included as a target in future broadly protective or universal influenza virus vaccines.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Kinetic analysis of Cas12a and Cas13a RNA-Guided nucleases for development of improved CRISPR-Based diagnostics

Bacterial CRISPR systems provide acquired immunity against invading nucleic acids by activating RNA-programmable RNases and DNases. Cas13a and Cas12a enzymes bound to CRISPR RNA (crRNA) recognize specific nucleic acid targets, initiating cleavage of the targets as well as non-target (trans) nucleic acids. Here, we examine the kinetics of single-turnover target and multi-turnover trans-nuclease activities of both enzymes. High-turnover, non-specific Cas13a trans-RNase activity is coupled to rapid binding of target RNA. By contrast, low-turnover Cas12a trans-nuclease activity is coupled to relatively slow cleavage of target DNA, selective for DNA over RNA, indifferent to base identity, and preferential for single-stranded substrates. Combining multiple crRNA increases detection sensitivity of targets, an approach we use to quantify pathogen DNA in samples from patients suspected of Buruli ulcer disease. Results reveal that these enzymes are kinetically adapted to play distinct roles in bacterial adaptive immunity and show how kinetic analysis can be applied to CRISPR-based diagnostics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Predicting transcription factor activity using prior biological information

Dysregulation of normal transcription factor activity is a common driver of disease. Therefore, the detection of aberrant transcription factor activity is important to understand disease pathogenesis. We have developed Priori, a method to predict transcription factor activity from RNA sequencing data. Priori has two key advantages over existing methods. First, Priori utilizes literature-supported regulatory information to identify transcription factor-target gene relationships. It then applies linear models to determine the impact of transcription factor regulation on the expression of its target genes. Second, results from a third-party benchmarking pipeline reveals that Priori detects aberrant activity from 124 single-gene perturbation experiments with higher sensitivity and specificity than 11 other methods. We applied Priori and other top-performing methods to predict transcription factor activity from two large primary patient datasets. Our work demonstrates that Priori uniquely discovered significant determinants of survival in breast cancer and identified mediators of drug response in leukemia.

59 BASIC BIOLOGICAL SCIENCES↗