A5035558591- Bioinformatics and Genomic Networks
- Gene expression and cancer classification
- Computational Drug Discovery Methods
- Gene Regulatory Network Analysis
- Microbial Metabolic Engineering and Bioproduction
- Advanced Proteomics Techniques and Applications
- Genetic Associations and Epidemiology
- Complex Network Analysis Techniques
- Machine Learning in Bioinformatics
- Advanced Graph Neural Networks
- Intimate Partner and Family Violence
- Genomics and Phylogenetic Studies
- Protein Structure and Dynamics
- Genetics, Bioinformatics, and Biomedical Research
- Data Management and Algorithms
- Alzheimer's disease research and treatments
- Metabolomics and Mass Spectrometry Studies
- Graph Theory and Algorithms
- Algorithms and Data Compression
- Genetic Mapping and Diversity in Plants and Animals
- Tryptophan and brain disorders
- Genomic variations and chromosomal abnormalities
- Adolescent and Pediatric Healthcare
- Fungal and yeast genetics research
- Advanced Database Systems and Queries
Case Western Reserve University
2015-2024
Western University of Health Sciences
2024
University Hospitals of Cleveland
2022
University School
2022
Cleveland Research (United States)
2021
Case Comprehensive Cancer Center
2009-2013
Genomics Medicine (Ireland)
2012
Cleveland Clinic
2012
Purdue University West Lafayette
2002-2008
University of California, San Diego
2007-2008
Abstract Motivation: The advent of next-generation sequencing (NGS) techniques presents many novel opportunities for applications in life sciences. vast number short reads produced by these techniques, however, pose significant computational challenges. first step types genomic analysis is the mapping to a reference genome, and several groups have developed dedicated algorithms software packages perform this function. As developers optimize their with respect various considerations, relative...
Abstract Summary Computational characterization of differential kinase activity from phosphoproteomics datasets is critical for correctly inferring cellular circuitry and how signaling cascades are altered in drug treatment and/or disease. Kinase-Substrate Enrichment Analysis (KSEA) offers a powerful approach to estimating changes kinase’s based on the collective phosphorylation its identified substrates. However, KSEA has been limited programmers who able implement algorithms. Thus, make it...
With an ever-increasing amount of available data on protein–protein interaction (PPI) networks and research revealing that these evolve at a modular level, discovery conserved patterns in becomes important problem. Although interactions is currently limited, recently developed algorithms have been shown to convey novel biological insights through employment elegant mathematical models. The main challenge aligning PPI define graph theoretical measure similarity between structures captures...
With rapidly increasing amount of network and interaction data in molecular biology, the problem effectively analyzing this is an important one. Graph theoretic formalisms, commonly used for these analysis tasks, often lead to computationally hard problems due their relation with subgraph isomorphism.This paper presents innovative new algorithm detecting frequently occurring patterns modules biological networks. Using graph simplification technique, which ideally suited networks, our renders...
High-throughput molecular interaction data have been used effectively to prioritize candidate genes that are linked a disease, based on the observation products of associated with similar diseases likely interact each other heavily in network protein-protein interactions (PPIs). An important challenge for these applications, however, is incomplete and noisy nature PPI data. Information flow methods alleviate problems certain extent, by considering indirect multiplicity paths.We demonstrate...
Emerging evidence indicates that gene products implicated in human cancers often cluster together "hot spots" protein-protein interaction (PPI) networks. Additionally, small sub-networks within PPI networks demonstrate synergistic differential expression with respect to tumorigenic phenotypes were recently shown be more accurate classifiers of disease progression when compared single targets identified by traditional approaches. However, many these studies rely exclusively on mRNA data, a...
Abstract Drug response prediction is a well-studied problem in which the molecular profile of given sample used to predict effect drug on that sample. Effective solutions this hold key for precision medicine. In cancer research, genomic data from cell lines are often utilized as features develop machine learning models predictive response. Molecular networks provide functional context integration features, thereby resulting robust and reproducible models. However, inclusion network increases...
While recent technological advances have motivated large-scale deployment of RFID systems, a number critical design issues remain unresolved. In this paper we deal with de- tecting redundant readers (the reader problem). The underlying difficulty associated problem arises from the lack collision detection mechanisms, potential inability to relay packets generated by other readers, and severe resource constraints on tags. We prove that an optimal solution is NP-hard propose randomized,...
Genome-wide linkage and association studies have demonstrated promise in identifying genetic factors that influence health disease. An important challenge is to narrow down the set of candidate genes are implicated by these analyses. Protein-protein interaction (PPI) networks useful extracting functional relationships between known disease genes, based on principle products similar diseases likely exhibit significant connectivity/proximity. Information flow?based methods shown be very...
Abstract Mass spectrometry enables high-throughput screening of phosphoproteins across a broad range biological contexts. When complemented by computational algorithms, phospho-proteomic data allows the inference kinase activity, facilitating identification dysregulated kinases in various diseases including cancer, Alzheimer’s disease and Parkinson’s disease. To enhance reliability activity inference, we present network-based framework, RoKAI, that integrates sources functional information...
ABSTRACT Background Link prediction is an important and well-studied problem in network biology. Recently, graph representation learning methods, including Graph Convolutional Network (GCN)-based node embedding have drawn increasing attention link prediction. Motivation An component of GCN-based the convolution matrix, which used to propagate features across network. Existing algorithms use degree-normalized adjacency matrix for this purpose, as closely related Laplacian, capturing spectral...
Electroluminescence (EL) imaging of photovoltiac (PV) modules offers high-speed, high-resolution information about device performance, affording opportunities for greater insight and efficiency in module characterization across manufacturing, research development, power plant operations management. Predicting electrical properties from EL image features is a critical step toward these applications. In this article, we demonstrate quantification both generalized performance mechanism-specific...
Abstract Although recent work has described the microbiome in solid tumors, microbial content hematological malignancies is not well-characterized. Here we analyze existing deep DNA sequence data from blood and bone marrow of 1870 patients with myeloid malignancies, along healthy controls, for bacterial, fungal, viral content. After strict quality filtering, find evidence dysbiosis disease cases, distinct signatures among subtypes. We also that associated host gene mutations myeloblast cell...
Molecular interaction data plays an important role in understanding biological processes at a modular level by providing framework for cellular organization, functional hierarchy, and evolutionary conservation. As the quality quantity of network increases rapidly, problem effectively analyzing this becomes significant. Graph theoretic formalisms, commonly used these analysis tasks, often lead to computationally hard problems due their relation subgraph isomorphism. This paper presents...
Emerging research demonstrates the potential of protein-protein interaction (PPI) networks in uncovering mechanistic bases cancers, through identification interacting proteins that are coordinately dysregulated tumorigenic and metastatic samples. When used as features for classification, such subnetworks improve diagnosis prognosis cancer considerably over single-gene markers. However, existing methods formulate coordination between multiple genes additive representation their expression...
To determine a molecular basis for prognostic differences in glioblastoma multiforme (GBM), we employed combinatorial network analysis framework to exhaustively search patterns protein-protein interaction (PPI) networks. We identified dysregulated signature distinguishing short-term (survival<225 days) from long-term (survival>635 survivors of GBM using whole genome expression data The Cancer Genome Atlas (TCGA). A 50-gene subnetwork achieved 80% prediction accuracy when tested against an...
Development of high-throughput monitoring technologies enables interrogation cancer samples at various levels cellular activity. Capitalizing on these developments, public efforts such as The Cancer Genome Atlas (TCGA) generate disparate omic data for large patient cohorts. As demonstrated by recent studies, heterogeneous sources provide the opportunity to gain insights into molecular changes that drive pathogenesis and progression. However, are limited vast search space a result low...
In recent years, a large number of photovoltaic (PV) systems have been added to the electrical grid as well installed off-grid systems. The trend suggests that deployment PV will continue rise in future. Thus, accurate forecasting performance is critical for reliability Due complex non-linear variability power output systems, non-trivial task. This affects stability and planning system network, can reduce uncertainty caused during operation. this work, we leverage spatial temporal coherence...