A5059592292- Genetic Associations and Epidemiology
- Genetic Mapping and Diversity in Plants and Animals
- Bioinformatics and Genomic Networks
- Gene expression and cancer classification
- Cancer Genomics and Diagnostics
- Genomics and Rare Diseases
- Genetic and phenotypic traits in livestock
- Cancer-related molecular mechanisms research
- Ferroptosis and cancer prognosis
- Birth, Development, and Health
- Epigenetics and DNA Methylation
- Advanced Causal Inference Techniques
- Maternal and fetal healthcare
- Biomedical Text Mining and Ontologies
- Pregnancy and preeclampsia studies
- Statistical Methods in Clinical Trials
- Machine Learning in Healthcare
- Nutritional Studies and Diet
- Meta-analysis and systematic reviews
- Privacy-Preserving Technologies in Data
- Chronic Disease Management Strategies
- Lung Cancer Research Studies
- Pluripotent Stem Cells Research
- COVID-19 Impact on Reproduction
- Machine Learning and Data Classification
Cedars-Sinai Medical Center
2022-2024
University of Pennsylvania
2018-2024
University of Michigan
2024
Pennsylvania State University
2013-2018
University of Massachusetts Chan Medical School
2010-2011
Worcester Polytechnic Institute
2009
Gene expression profiles have been broadly used in cancer research as a diagnostic or prognostic signature for the clinical outcome prediction such stage, grade, metastatic status, recurrence, and patient survival, well to potentially improve management. However, emerging evidence shows that gene expression-based varies between independent data sets. One possible explanation of this effect is previous studies were focused on identifying genes with large main effects associated outcomes....
Infrared-emitting PbSe nanocrystals are of increasing interest in both fundamental research and technical application. However, the practical applications greatly limited by their poor stability. In this work, absorption photoluminescence spectra were utilized to observe stability over several conventional factors, that is, particle concentration, size, temperature, light exposure, contacting atmosphere, storage forms (solution or solid powder). Both luminescence exposed air showed...
It is common that cancer patients have different molecular signatures even though they similar clinical features, such as histology, due to the heterogeneity of tumors. To overcome this variability, we previously developed a new approach incorporating prior biological knowledge identifies knowledge-driven genomic interactions associated with outcomes interest. However, no systematic has been proposed identify interaction models between pathways based on multi-omics data. Here novel...
Evaluation of survival models to predict cancer patient prognosis is one the most important areas emphasis in research. A binary classification approach has difficulty directly predicting due characteristics censored observations and fact that predictive power depends on threshold used set two classes. In contrast, traditional Cox regression some drawbacks sense it does not allow for identification interactions between genomic features, which could have key roles associated with prognosis....
Machine learning methods have gained popularity and practicality in identifying linear non-linear effects of variants associated with complex disease/traits. Detection epistatic interactions still remains a challenge due to the large number features relatively small sample size as input, thus leading so-called "short fat data" problem. The efficiency machine can be increased by limiting input features. Thus, it is very important perform variable selection before searching for epistasis. Many...
Effective cancer clinical outcome prediction for understanding of the mechanism various types has been pursued using molecular-based data such as gene expression profiles, an approach that promise providing better diagnostics and supporting further therapies. However, based on profiles varies between independent sets. Further, single-gene is limited evaluation since genes do not act in isolation, but rather interact with other complex signaling or regulatory networks. In addition, pathways...
Clinical data of patients' measurements and treatment history stored in electronic health record (EHR) systems are starting to be mined for better options disease associations. A primary challenge associated with utilizing EHR is the considerable amount missing data. Failure address this issue can introduce significant bias EHR-based research. Currently, imputation methods rely on correlations among structured phenotype variables EHR. However, genetic studies have shown that many phenotypes...
Quantitative Trait Locus (QTL) analysis and Genome-Wide Association Studies (GWAS) have the power to identify variants that capture significant levels of phenotypic variance in complex traits. However, effort time are required select best methods optimize parameters pre-processing steps. Although machine learning approaches been shown greatly assist optimization data processing, applying them QTL GWAS is challenging due complexity large, heterogenous datasets. Here, we describe...
Pleiotropy, where 1 genetic locus affects multiple phenotypes, can offer significant insights in understanding the complex genotype-phenotype relationship. Although individual associations have been thoroughly explored, seemingly unrelated phenotypes be connected genetically through common pleiotropic loci or genes. However, current analyses of pleiotropy challenged by both methodologic limitations and a lack available suitable data sources.In this study, we propose to utilize new regression...
Abstract In cross-cohort studies, integrating diverse datasets, such as electronic health records (EHRs), is both essential and challenging due to cohort-specific variations, distributed data storage, privacy concerns. Traditional methods often require pooling or complex harmonization, which can reduce efficiency limit the scope of learning. We introduce mixWAS, a one-shot, lossless algorithm that efficiently integrates EHR datasets via summary statistics. Unlike existing approaches, mixWAS...
Genome-wide association studies (GWAS) of various heritable human traits and diseases have identified numerous associated single nucleotide polymorphisms (SNPs), most which small or modest effects. Polygenic risk scores (PRS) aim to better estimate individuals' genetic predisposition by aggregating the effects multiple SNPs from GWAS. However, current PRS is designed capture only simple linear across genome, limiting their ability fully account for complex polygenic architecture. To address...
The future of medicine is moving towards the phase precision medicine, with goal to prevent and treat diseases by taking inter-individual variability into account. A large part lies in our genetic makeup. With fast paced improvement high-throughput methods for genome sequencing, a tremendous amount genetics data have already been generated. next hurdle sufficient computational tools analyzing sets data. Genome-Wide Association Studies (GWAS) primary method assess relationship between single...
Abstract Objectives COVID-19, since its emergence in December 2019, has globally impacted research. Over 360 000 COVID-19-related manuscripts have been published on PubMed and preprint servers like medRxiv bioRxiv, with preprints comprising about 15% of all manuscripts. Yet, the role impact COVID-19 research evidence synthesis remain uncertain. Materials Methods We propose a novel data-driven method for assigning weights to individual systematic reviews meta-analyses. This weight termed...
Increasingly, clinical phenotypes with matched genetic data from bio-bank linked electronic health records (EHRs) have been used for pleiotropy analyses. Thus far, analysis using individual-level EHR has limited to one site. However, it is desirable integrate multiple sites improve the detection power and generalizability of results. Due privacy concerns, patients' are not easily shared across institutions. As a result, we introduce Sum-Share, method designed efficiently perform analysis....