A5041863346- Multiple Myeloma Research and Treatments
- Advanced Breast Cancer Therapies
- AI in cancer detection
- Bioinformatics and Genomic Networks
- Single-cell and spatial transcriptomics
- Radiomics and Machine Learning in Medical Imaging
- Cell Image Analysis Techniques
- CAR-T cell therapy research
- Gene expression and cancer classification
- Cancer Genomics and Diagnostics
- Renal cell carcinoma treatment
- Monoclonal and Polyclonal Antibodies Research
- Genomics and Chromatin Dynamics
- Digital Imaging for Blood Diseases
- Peptidase Inhibition and Analysis
- Cancer-related cognitive impairment studies
- Cancer-related molecular mechanisms research
- Ferroptosis and cancer prognosis
- Telomeres, Telomerase, and Senescence
- Mesoporous Materials and Catalysis
- Gene Regulatory Network Analysis
- Inflammatory mediators and NSAID effects
- Domain Adaptation and Few-Shot Learning
- Protein Degradation and Inhibitors
- Cancer Immunotherapy and Biomarkers
Genmab (United States)
2023-2024
Rutgers, The State University of New Jersey
1992-2024
Indiana University School of Medicine
2019-2023
Indiana University – Purdue University Indianapolis
2018-2023
Corning (United States)
2023
Rutgers Health
2022
The Ohio State University
2016-2021
Indiana University Indianapolis
2021
Indiana University
2019-2020
University School
2019-2020
Improved cancer prognosis is a central goal for precision health medicine. Though many models can predict differential survival from data, there strong need sophisticated algorithms that aggregate and filter relevant predictors increasingly complex data inputs. In turn, these should provide deeper insight into which types of are most to improve prognosis. Deep Learning-based neural networks offer potential solution both problems because they highly flexible account complexity in non-linear...
Large language models (LLMs) have made a significant impact on the fields of general artificial intelligence. General purpose LLMs exhibit strong logic and reasoning skills world knowledge but can sometimes generate misleading results when prompted specific subject areas. trained with domain-specific reduce generation information (i.e. hallucinations) enhance precision in specialized contexts. Training new corpora however be resource intensive. Here we explored use retrieval-augmented (RAG)...
Senescent cells within the tumor microenvironment (TME) adopt a proinflammatory, senescence-associated secretory phenotype (SASP) that promotes cancer initiation, progression, and therapeutic resistance. Here, exposure to palbociclib (PD-0332991), CDK4/6 inhibitor, induces senescence robust SASP in normal fibroblasts. Senescence caused by prolonged inhibition is DNA damage-independent associated with Mdm2 downregulation, whereas elicited these largely reliant upon NF-κB activation. Based...
Abstract Background Recent advances in kernel-based Deep Learning models have introduced a new era medical research. Originally designed for pattern recognition and image processing, are now applied to survival prognosis of cancer patients. Specifically, versions the Cox proportional hazards trained with transcriptomic data predict outcomes Methods In this study, broad analysis was performed on TCGA cancers using variety Learning-based models, including Cox-nnet, DeepSurv, method proposed by...
Abstract Heterogeneous response to Enzalutamide, a second-generation androgen receptor signaling inhibitor, is central problem in castration-resistant prostate cancer (CRPC) management. Genome-wide systems investigation of mechanisms that govern Enzalutamide resistance promise elucidate markers heterogeneous treatment and salvage therapies for CRPC patients. Focusing on the de novo role MYC as marker resistance, here we reconstruct CRPC-specific mechanism-centric regulatory network,...
We propose DEGAS (Diagnostic Evidence GAuge of Single cells), a novel deep transfer learning framework, to disease information from patients cells. call such transferrable "impressions," which allow individual cells be associated with attributes like diagnosis, prognosis, and response therapy. Using simulated data ten diverse single-cell patient bulk tissue transcriptomic datasets glioblastoma multiforme (GBM), Alzheimer's (AD), multiple myeloma (MM), we demonstrate the feasibility,...
Multiple myeloma (MM) is an incurable malignancy of plasma cells. To identify targets for MM immunotherapy, we develop integrated pipeline based on mass spectrometry analysis seven cell lines and RNA sequencing (RNA-seq) from 900+ patients. Starting 4,000+ candidates, the most highly expressed surface proteins. We annotate candidate protein expression in many healthy tissues validate promising 30+ patient samples with relapsed/refractory MM, as well primary hematopoietic stem cells T by flow...
Rapid advances in single cell RNA sequencing (scRNA-seq) have produced higher-resolution cellular subtypes multiple tissues and species. Methods are increasingly needed across datasets species to (i) remove systematic biases, (ii) model with ambiguous labels (iii) classify cells map type labels. However, most methods only address one of these problems on broad types or simulated data using a type. It is also important subtypes, subtype from datasets, models trained simultaneously...
Abstract Neoantigen peptides arising from genetic alterations may serve as targets for personalized cancer vaccines and positive predictors of response to immune checkpoint therapy. Mutations in genes regulating RNA splicing are common hematological malignancies leading dysregulated intron retention (IR). In this study, we investigated IR a potential source tumor neoantigens multiple myeloma (MM) patients the relationship IR-induced (IR-neoAg) with clinical outcomes. MM-specific events were...
Gene co-expression network (GCN) mining is a systematic approach to efficiently identify novel disease pathways, predict gene functions and search for potential biomarkers. However, few studies have systematically identified GCNs in multiple brain transcriptomic data of Alzheimer's (AD) patients looked their specific functions.In this study, we first mined GCN modules from AD normal samples datasets respectively; then that are or samples; lastly, condition-specific with similar functional...
Long noncoding RNAs (lncRNAs) are known to regulate gene expression; however, in many cases, the mechanism of this regulation is unknown. One novel lncRNA relevant inflammation and arachidonic acid (AA) metabolism p50-associated COX-2 extragenic RNA (PACER). We focused our research on PACER lung cancer. While function not entirely understood, play a role inflammation-associated conditions. Our data suggest that critically involved transcription dysregulation cancer cells. analysis The Cancer...
Abstract Background Existing studies have demonstrated that the integrative analysis of histopathological images and genomic data can be used to better understand onset progression many diseases, as well identify new diagnostic prognostic biomarkers. However, since development pathological phenotypes are influenced by a variety complex biological processes, complete understanding underlying gene regulatory mechanisms for cell tissue morphology is still challenge. In this study, we explored...
Gene co-expression networks are widely studied in the biomedical field, with algorithms such as WGCNA and lmQCM having been developed to detect co-expressed modules. However, these have limitations insufficient granularity unbalanced module size, which prevent full acquisition of knowledge from data mining. In addition, it is difficult incorporate prior current detection algorithms.In this paper, we propose a novel algorithm based on topology potential spectral clustering modules gene...
Multiple myeloma (MM) has two clinical precursor stages of disease: monoclonal gammopathy undetermined significance (MGUS) and smoldering multiple (SMM). However, the mechanism progression is not well understood. Because gene co-expression network analysis a well-known method for discovering new functions regulatory relationships, we utilized this framework to conduct differential identify interesting transcription factors in publicly available datasets. We then used copy number variation...
Expression quantitative trait loci (eQTL) analysis is useful for identifying genetic variants correlated with gene expression; however, it cannot distinguish between causal and nearby nonfunctional variants. Because the majority of disease-associated SNPs are located in regulatory regions, they can impact allele-specific binding (ASB) transcription factors result differential expression target alleles. In this study, our aim was to identify functional single-nucleotide polymorphisms (SNPs)...
Recent advances in kernel-based Deep Learning models have introduced a new era medical research. Originally designed for pattern recognition and image processing, are now applied to survival prognosis of cancer patients. Specifically, versions the Cox proportional hazards trained with transcriptomic data predict outcomes In this study, broad analysis was performed on TCGA cancers using variety Learning-based models, including Cox-nnet, DeepSurv, method proposed by our group named AECOX...
Abstract Wolves living in the Chernobyl Exclusion Zone (CEZ) for at least six generations have adapted to high radiation exposure and are a unique model studying genetic selection under extreme conditions of oncogenic stress. The objective this study was build off previously described signatures adaptation CEZ wolves, identify networks genes associated with immune determine prognostic significance these human cancer datasets provide insight into immunity. RNA from blood samples wolves CEZ,...
Large language models (LLMs) have made significant advancements in natural processing (NLP). Broad corpora capture diverse patterns but can introduce irrelevance, while focused enhance reliability by reducing misleading information. Training LLMs on poses computational challenges. An alternative approach is to use a retrieval-augmentation (RetA) method tested specific domain. To evaluate LLM performance, OpenAI's GPT-3, GPT-4, Bing's Prometheus, and custom RetA model were compared using 19...
Abstract Background Renal cell carcinoma (RCC) is a complex disease and comprised of several histological subtypes, the most frequent which are clear renal (ccRCC), papillary (PRCC) chromophobe (ChRCC). While lots studies have been performed to investigate molecular characterizations different subtypes RCC, our knowledge regarding underlying mechanisms still incomplete. As alterations eventually reflected on pathway level execute certain biological functions, characterizing perturbations...