2003 |
Shen, Li |
R43Activity Code Description: To support projects, limited in time and amount, to establish the technical merit and feasibility of R&D ideas which may ultimately lead to a commercial product(s) or service(s). |
Id-Tag Protein Profiling Array For the Molecular Charact @ Superarray Bioscience Corporation
DESCRIPTION (provided by applicant): The long-term objective of this proposal is to develop a simple, reliable, sensitive, and cost-effective technological platform that will allow the simultaneous and quantitative analysis of multiple antigens in human tumor samples derived from archived tissue specimens. Such a platform is urgently needed in the clinical laboratory setting to thoroughly characterize cancer. Multiplex analysis of molecular markers can augment and improve conventional methods for determining the primary anatomical site of tumor origin, predicting tumor behavior, and formulating effective therapy. SuperArray Inc.'s proprietary ID-Tag Protein Profiling technology is an antibody-based multiplex detection. Multiple ID tags are used to track multiple protein targets simultaneously. ID-tag signal can be further amplified to increase assay detection limit. Therefore, it is very promise to develop a simple clinical diagnostic tool that will be supersensitive and allow the simultaneous analysis of multiple antigens from archival material such as paraffin-embedded sections and alcohol-fixed cytology specimens. In this phase I proposal, SuperArray, Inc., will collaborate with Dr. Jian Yu Rao, Department of Pathology, University of California at Los Angeles, to evaluate the feasibility and application of the ID-Tag Protein Profiling Array technology for the molecular characterization of human cancers.
|
0.91 |
2008 — 2009 |
Shen, Li |
R03Activity Code Description: To provide research support specifically limited in time and amount for studies in categorical program areas. Small grants provide flexibility for initiating studies which are generally for preliminary short-term projects and are non-renewable. |
Spharm Shape Modeling and Analysis Toolkit For Brain Imaging @ Indiana Univ-Purdue Univ At Indianapolis
DESCRIPTION (provided by applicant): This R03 application is for one year of funding to develop and release SPHARM-MAT, a 3D shape modeling and analysis toolkit for neuroanatomical studies. Shape analysis is becoming of increasing interest to the neuroimaging community because of its potential to provide important information beyond simple volume measurements. SPHARM-MAT is a suite of tools that are designed to effectively characterize and normalize morphometric features of 3D brain structures using spherical harmonic functions (SPHARM). It establishes a foundation on which statistical shape analysis can be performed to discover morphometric changes in neuroanatomical structures related to specific brain disorders. The development of SPHARM-MAT is a synergistic effort in relation to existing tools. It provides an alternative software platform as well as an opportunity for tool comparison and cross-validation. More importantly, it is a powerful toolkit with several new features that add value. It has a broader applicability due to the implementation of several new algorithms that overcome the limitations of the traditional SPHARM method. It is user-friendly and interoperable, offering a graphical interface, modular design structure, and well-documented user manual and source code. The objectives of this research include (1) implementing an improved method for individual SPHARM modeling, (2) implementing an improved method for group analysis using SPHARM, and (3) packaging this functionality together with other necessary components in SPHARM-MAT and releasing the toolkit. SPHARM-MAT will be developed by packaging the existing prototype implementation of SPHARM processing components from previous studies as well as by implementing additional necessary components including a graphical user interface, a visualization module, a user manual, a project wiki site, and documentation of source code. SPHARM-MAT will be released at NITRC (www.nitrc.org) using GNU General Public License for wide dissemination. The dissemination of this new toolkit will enable investigators working on many brain disorders to more effectively test neuroanatomical hypotheses and therefore this project will benefit public health outcomes. PUBLIC HEALTH RELEVANCE: Shape analysis is becoming of increasing interest to the neuroimaging community because of its potential to provide important information beyond simple volume measurements and to understand morphometric changes in neuroanatomical structures related to specific brain disorders. The purpose of this project is to develop and release SPHARM-MAT, a 3D shape modeling and analysis toolkit for neuroanatomical studies. SPHARM-MAT is a synergistic effort in relation to existing tools, and is a powerful toolkit with several new features that add value, including ease of use, broad applicability, good interoperability, and wide dissemination.
|
0.925 |
2011 — 2015 |
Saykin, Andrew (co-PI) [⬀] Shen, Li |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Iii: Small: Collaborative Research: a Large-Scale Data Mining Framework For Genome-Wide Mapping of Multi-Modal Phenotypic Biomarkers and Outcome Prediction
Today's massive generation of digital data is greatly outpacing the development of computational methods and tools and presents critical challenges for achieving the full transformative potential of these data. For example, recent advances in acquiring multi-modal brain imaging and genome-wide array data provide exciting new opportunities to study the influence of genetic variation on brain structure and function. Major computational challenges are, however, bottlenecks for comprehensive joint analysis of these data due to their unprecedented scale and complexity. This project will employ the new capabilities of large-scale data mining techniques in multi-view learning, multi-task learning, and robust classification to address critical challenges in systematically analyzing massive multi-modal genetic, imaging, and other biomarker data. Specifically, this project will: (1) develop new multi-view learning methods to detect task-relevant phenotypic biomarkers from large scale heterogeneous imaging and other biomarker data, (2) implement new sparse multi-task regression models to reveal the genetic basis of phenotypic biomarkers at multiple levels (e.g., SNP, haplotype, gene and/or pathway), (3) design novel robust classification methods via structural sparsity for outcome prediction using integrated genotypic and phenotypic data, and (4) package these new methods into a data mining toolkit and release it to the public.
The intellectual merits of this project derive not only from the development of novel data mining methods, but also from their application to imaging genetic studies. These methods are designed to take into account interrelated structures among multiple data modalities and offer systematic strategies to reveal structural imaging genetic associations. The proposed methods and tools are expected to impact neurological and psychological research and enable investigators to effectively test imaging genetics hypothesis and advance biomedical science and technology. In addition, the proposed data mining framework addresses generic critical needs of large-scale data analysis and integration and, therefore, will impact a large number of research areas where high-value knowledge and complex patterns can potentially be discovered from massive high-dimensional and heterogeneous data sets. This project will facilitate the development of novel educational tools to enhance several current courses at UT Arlington and IUPUI. Both universities are minority-serving institutions, and the PIs will engage the minority students and under-served populations in research activities to give them a better exposure to cutting-edge scientific research.
|
1 |
2012 — 2015 |
Moore, Jason H. Saykin, Andrew J (co-PI) [⬀] Shen, Li |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Bioinformatics Strategies For Multidimensional Brain Imaging Genetics @ Indiana Univ-Purdue Univ At Indianapolis
DESCRIPTION (provided by applicant): Today's generation of multi-modal imaging systems produces massive high dimensional data sets, which when coupled with high throughput genotyping data such as single nucleotide polymorphisms (SNPs), provide exciting opportunities to enhance our understanding of phenotypic characteristics and the genetic architecture of human diseases. However, the unprecedented scale and complexity of these data sets have presented critical computational bottlenecks requiring new concepts and enabling tools. To address these challenges, using the study of Alzheimer's disease (AD) as a test bed, this project will develop and validate novel bioinformatics strategies for multidimensional brain imaging genetics. Aim 1 is to develop a novel bi- multivariate analysis strategy, S3K-CCA, for studying imaging genetic associations. Existing imaging genetics methods are typically designed to discover single-SNP-single-QT, single-SNP-multi-QT or multi-SNP-single- QT associations, and have limited power in revealing complex relationships between interlinked genetic markers and correlated brain phenotypes. To overcome this limitation, S3K-CCA is designed to be a sparse bi- multivariate learning model that simultaneously uses multiple response variables with multiple predictors for analyzing large-scale multi-modal neurogenomic data. Aim 2 is to develop HD-BIG, a visualization and systems biology framework for integrative analysis of High-Dimensional Brain Imaging Genetics data. Machine learning strategies to seamlessly incorporate valuable domain knowledge to produce biologically meaningful results is still an under-explored area in imaging genetics. In this aim, we will develop a user-friendly heat map interface to visualize high-dimensional results, adjust learning parameters and strategies, interact with existing bioinformatics resources and tools, and facilitate visual exploratory and systems biology analysis. A novel imaging genetic enrichment analysis (IGEA) method will be developed to identify relevant genetic pathways and associated brain circuits, and to reveal complex relationships among them. Aim 3 is to evaluate the proposed S3K-CCA and IGEA methods and the HD-BIG framework using both simulated and real imaging genetics data. This project is expected to produce novel bioinformatics algorithms and tools for comprehensive joint analysis of large scale heterogeneous imaging genetics data. The availability of these powerful methods is critical to the success of many imaging genetics initiatives. In addition, they can also help enable new computational applications in other areas of biomedical research where systematic and integrative analysis of large-scale multi-modal data is critical. Using AD as an exemplar, the proposed methods will demonstrate the potential for enhancing mechanistic understanding of complex disorders, which can benefit public health outcomes by facilitating diagnostic and therapeutic progress.
|
0.925 |
2012 |
Shen, Li |
R56Activity Code Description: To provide limited interim research support based on the merit of a pending R01 application while applicant gathers additional data to revise a new or competing renewal application. This grant will underwrite highly meritorious applications that if given the opportunity to revise their application could meet IC recommended standards and would be missed opportunities if not funded. Interim funded ends when the applicant succeeds in obtaining an R01 or other competing award built on the R56 grant. These awards are not renewable. |
A Molecular Switch of the Type Iii Secretion System in Chlamydia Trachomatis @ Lsu Health Sciences Center
DESCRIPTION (provided by applicant): A molecular switch of the type III secretion system in Chlamydia trachomatis The obligate intracellular bacterium C. trachomatis is the causative agent of the most common sexually transmitted disease worldwide and represents a significant public health burden. The severe sequelae of genital chlamydial infections in women include pelvic inflammatory disease, ectopic pregnancy and tubal infertility. A key virulence mechanism of C. trachomatis is the type III secretion system (T3SS) that directly delivers protein effectors into the host cell cytosol to subvert host immunity and enables bacterial survival in hosts. The objective of this project is to study the mechanism by which C. trachomatis controls type III secretion and gene expression, contributing to disease pathogenesis. We hypothesize that T3SS activity is regulated and coupled to gene transcription during C. trachomatis infection by a molecular switch, consisting of CT663 and its protein partners. This hypothesis is strongly supported by our novel finding that chlamydial CT663 is a bi-functional protein acting as both a transcription regulator that interacts with RNA polymerase containing ? 66 and a T3SS chaperone for CopN, which is a regulator as well as an effector of the T3SS. Our specific aims are: Aim 1. To test the hypothesis that CT663 forms a protein complex necessary for type III secretion activity. We will characterize the dynamic interactions of CT663 and its partners, and how these interactions impact the secretion activity using quantitative assays for protein-protein interactions, immunodetection, and an enteropathogenic Escherichia coli (EPEC) system. Aim 2. To test the hypothesis that CT663 differentially regulates the gene transcription. This aim will be achieved using our established transcription assays with reconstituted ?66RNA polymerase in vitro and in E. coli. These studies will uncover how T3SS activity affects gene expression during C. trachomatis infection and vice versa. This project will provide important insights into how C. trachomatis utilizes T3SS to survive in an intracellular niche by coordinating the regulatory events of the T3SS and gene expression during infection. Our research will significantly expand current knowledge of the C. trachomatis infection process and contribute to the identification of potential drug targets for new therapies that could significantly reduce the public health burden caused by C. trachomatis infection.
|
0.903 |
2016 — 2019 |
Shen, Li |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Integrative Bioinformatics Approaches to Human Brain Genomics and Connectomics @ Indiana Univ-Purdue Univ At Indianapolis
Project Summary (Abstract) Human brain connectomics and imaging genomics are two emerging research fields enabled by recent advances in multi-modal neuroimaging and high throughput omics technologies. Integrating brain imaging genomics and connectomics holds great promise for a systematic characterization of both the human brain connectivity and the connectivity-based neurobiological pathway from its genetic architecture to its influences on cognition and behavior. Rich multi-modal neuroimaging data coupled with high density omics data are available from large-scale landmark studies such as the NIH Human Connectome Project (HCP) and Alzheimer's Disease Neuroimaging Initiative (ADNI). The unprecedented scale and complexity of these data sets, however, have presented critical computational bottlenecks requiring new concepts and enabling tools. To bridge the gap, this project is proposed to develop and validate novel integrative bioinformatics approaches to human brain genomics and connectomics, and has three aims. Aim 1 is to develop a novel computational pipeline for a systematic characterization of structural connectome optimized for imaging genomics, where special consideration will be taken to address important issues including reliable tractography and network construction, systematic extraction of network attributes, identification of important network components (e.g., hubs, communities and rich clubs), prioritization of network attributes towards genomic analysis, and identification of outcome-relevant network measures. Aim 2 is to develop novel bioinformatics strategies to determining genetic basis of structural connectome, including novel approaches for analyzing graph-based phenotype data and learning outcome-relevant associations, and an ensemble of effective learning modules to handle a comprehensive set of scenarios on mining genome-connectome associations at the genome-wide connectome-wide scale. Aim 3 is to develop a visual analytic software system for interactive visual exploration and mining of fiber-tracts and brain networks with their genetic determinants and functional outcomes, where new visualization and exploration methods will be implemented for seamlessly combining human expertise and machine intelligence to enable novel contextually meaningful discoveries. This project is expected to produce novel bioinformatics algorithms and tools for comprehensive joint analysis of large scale genomics and connectomics data. The availability of these powerful methods and tools is critical for full knowledge discovery and exploitation of major connectomics and imaging genomics initiatives such as HCP and ADNI. In addition, they can also help enable new computational applications in many other biomedical research areas where integrative analysis of connectomics and genomics data are of interest. Via thorough test and evaluation on HCP and ADNI data, these methods and tools will be demonstrated to have considerable potential for a better understanding of the interplay between genes, brain connectivity and function, and thus be expected to impact biomedical research in general and benefit public health outcomes.
|
0.951 |
2016 — 2017 |
Shen, Li |
R43Activity Code Description: To support projects, limited in time and amount, to establish the technical merit and feasibility of R&D ideas which may ultimately lead to a commercial product(s) or service(s). |
Mobile Application to Deliver Personalized Nutrition For the Prevention of Alzheimer's Disease @ Genben Lifesciences Corporation
Genben Lifesciences (dba GB HealthWatch) is a digital health and nutritional genomics company. Our mission is to help fight common, diet- and lifestyle-related chronic diseases with precision nutrition and advanced mobile technologies. Our company developed the HealthWatch 360 mobile app for tracking dietary intake, physical activity and health-related symptoms. This mobile app has received excellent reviews for both the iOS and Android platforms and has over 70,000 registered users. Health condition- specific goals featured in the app provide refined nutritional recommendations based on clinical guidelines for the prevention of diet-induced, chronic diseases. Alzheimer?s disease (AD) is the leading cause of dementia in the U.S., the 6th leading cause of mortality and a major cost to the nation, families and caregivers. This phase I proposal is for the development of a mobile tool that will deliver personalized nutrition and meal plans based on genetic risk in order to mitigate AD risk. Aim 1: Develop a systematic process to identify specific dietary and nutritional components associated with AD. Using the 1000 Genomes Phase 3 database and nutritional analyses of the traditional diets that correspond with the 26 populations, we will analyze whether specific nutrients correlate with the frequency of genetic variants that predispose risk of AD. We hypothesize that a population?s fitness would be enhanced and AD risk would be lower when the genetic variants that are selected for in a given population are in equilibrium with a diet that is enriched or depleted with the correlated nutrient(s). We will develop statistical models that will quantify these relationships. Aim 2: Translate nutritional patterns to a set of quantitative recommendations for AD prevention. With the nutrient data we obtain from Aim 1, combined with other evidence-based nutrition guidelines for AD, we will synthesize a set of qualitative and quantitative nutritional ?rules? based on the app user?s genotypes, family history of AD and other health conditions. These genotype- and/or phenotype - specific rules will estimate ideal ranges for a given nutrient and amend the conventional ?rules? (i.e. nutritional recommendations) by the 2015-2020 Dietary Guidelines for America. Aim 3: Mobile app for delivery of personalized meal plan for the prevention of AD. This mobile application is designed for guided, proactive and self-executed prevention of AD, and targeted at those who are at elevated risk. We propose developing machine-learning algorithms to create meal plans that employ the modified nutrient ranges (from Aims 1 and 2) for a given AD risk genotype. Users will be able to modify food preference parameters (for example, ?vegetarian?) while maintaining the appropriate nutrient ranges. A key outcome of this project will be a platform that supports population-wide dietary intervention by seamlessly connecting preventive healthcare with daily life in the digital age.
|
0.907 |
2016 — 2020 |
Shen, Li Ning, Xia Li, Lang (co-PI) [⬀] |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Sch: Int: Mining Drug-Drug Interaction Induced Adverse Effects From Health Record Databases
Recent advances in large-scale electronic health record database techniques provide exciting new opportunities to the study of drug safety. Drug-drug interactions (DDIs), a major cause of adverse drug events (ADEs), are a serious global health concern, and a severe detriment to public health. The scale of DDIs involving three or more drugs (also called high-order DDIs) has posed a prohibitory challenge for its molecular pharmacology and clinical research, which motivates alternative strategies such as mining health record data. This project aims to develop large-scale computational strategies and effective software tools for mining high-order DDI effects from health record databases, in order to yield novel discoveries in drug safety, and ultimately to benefit national health and well being.
To achieve the above goal, this project is designed to complete four specific tasks. Task 1 aims to develop a novel statistical framework to discover high-order DDI signals associated with ADEs from health record databases. Task 2 aims to study a novel drug safety problem for mining directional DDI signals. Task 3 aims to develop an innovative approach for mining directional DDI patterns at the drug-group level. Task 4 is devoted to software development, evaluation and validation. The project applies these methods to analyze three independent databases, packages method implementations into a user-friendly software toolkit, and releases the toolkit to the public. This project not only facilitates the development of novel computational techniques in drug safety research, but also addresses emerging scientific questions in modeling, mining, and visual exploration of complex data such as the health record data. The project's educational activities include course development, student mentoring and advising, and involvement of minority and underrepresented students in research activities.
|
1 |
2017 — 2021 |
Shen, Li |
P50Activity Code Description: To support any part of the full range of research and development from very basic to clinical; may involve ancillary supportive activities such as protracted patient care necessary to the primary research or R&D effort. The spectrum of activities comprises a multidisciplinary attack on a specific disease entity or biomedical problem area. These grants differ from program project grants in that they are usually developed in response to an announcement of the programmatic needs of an Institute or Division and subsequently receive continuous attention from its staff. Centers may also serve as regional or national resources for special research purposes. |
Chromatin and Gene Analysis Core @ Icahn School of Medicine At Mount Sinai
PROJECT SUMMARY ? CHROMATIN AND GENE ANALYSIS CORE The Chromatin and Gene Analysis Core provides the technical and bioinformatics infrastructure to optimally mine the large amount of genome-wide gene expression and chromatin data that are generated from the Center's work. Center investigators lead the field in several aspects of genome-wide chromatin analyses, including pioneering these approaches in brain, which offers several technical challenges. We have optimized methods for several next generation sequencing approaches, including RNA-seq, ChIP-seq, ATAC-seq, in situ HiC, and whole genome bisulfite sequencing, among others, for mouse and human brain, the latter offering an additional set of unique technical challenges. These approaches are being used increasingly to study individual cell types within a given limbic brain region, in both mouse and human. The Core has established expertise in analyzing the highly complex datasets obtained, and will continue work on further improving the tools available. All of the genome-wide data generated by our Center are analyzed by this Core. In parallel, the Core runs routine genome-wide assays on defined animal models, with the Animal Models Core, and thereby provides a foundation for the more specific and sophisticated measures in the individual Projects. Indeed, each Project focuses on genes that are among the most robustly regulated across mouse depression models and human depression. As well, the Core pilots novel experimental technologies; an example is our ability to target single chromatin modifications to a single gene within a single brain region and cell type in vivo, thus providing an unparalleled level of proof to establish epigenetic mechanisms of depression. By consolidating the analytical work and routine genome-wide analyses within a centralized Core, we ensure rigorous control over the data and facilitate comparisons of experimental findings across the four individual Projects. This consolidation also makes financial sense, since we concentrate and maximize efficient use of our expertise. Finally, the Core is responsible, with the Administrative Core, in maintaining a platform for sharing our genome-wide datasets and analytical tools across the Center's laboratories as well as with the scientific community and lay public at large.
|
0.957 |
2019 — 2021 |
Shen, Li |
P01Activity Code Description: For the support of a broadly based, multidisciplinary, often long-term research program which has a specific major objective or a basic theme. A program project generally involves the organized efforts of relatively large groups, members of which are conducting research projects designed to elucidate the various aspects or components of this objective. Each research project is usually under the leadership of an established investigator. The grant can provide support for certain basic resources used by these groups in the program, including clinical components, the sharing of which facilitates the total research effort. A program project is directed toward a range of problems having a central research focus, in contrast to the usually narrower thrust of the traditional research project. Each project supported through this mechanism should contribute or be directly related to the common theme of the total research effort. These scientifically meritorious projects should demonstrate an essential element of unity and interdependence, i.e., a system of research activities and projects directed toward a well-defined research program goal. |
Gene and Chromatin Analysis Core @ Icahn School of Medicine At Mount Sinai
PROJECT SUMMARY/ABSTRACT? GENE AND CHROMATIN ANALYSIS CORE The Gene and Chromatin Analysis Core provides the technical and bioinformatics infrastructure to optimally mine the large amount of genome-wide gene expression and chromatin data that are generated from the PPG?s work. PPG investigators lead the field in several aspects of genome-wide analyses, including pioneering these approaches to specific cell types in brain, which offers several technical challenges. We have optimized methods for several next generation sequencing approaches, including RNA-seq, ChIP-seq, ATAC- seq, HiC, and whole genome bisulfite sequencing, among others, for rodent and human brain, the latter offering an additional set of unique technical challenges. These approaches are being used increasingly to study individual cell types within a given brain reward region, in both rodent and human. The Core has established expertise in analyzing the highly complex datasets obtained, and will work on further improving the tools available. All of the genome-wide data generated by our PPG are analyzed by this Core. In parallel, the Core runs routine genome-wide assays on defined animal models, with the Animal Models Core, and thereby provides a foundation for the more specific and sophisticated measures in the individual Projects. Indeed, each Project focuses on genes that are among the most robustly regulated across rodent addiction models and human substance use disorders. As well, the Core pilots novel experimental technologies; an example is our ability to target single chromatin modifications to a single gene within a single brain region and cell type in vivo, thus providing an unparalleled level of proof to establish transcriptional mechanisms of addiction. By consolidating the analytical work and routine genome-wide analyses within a centralized Core, we ensure rigorous control over the data and facilitate comparisons of experimental findings across the four individual Projects. This consolidation also makes financial sense, since we concentrate and maximize efficient use of our expertise. Finally, the Core is responsible, with the Administrative Core, in maintaining a platform for sharing our genome-wide datasets and analytical tools across the PPG?s laboratories as well as with the scientific community and lay public at large.
|
0.957 |
2019 — 2022 |
Shen, Li |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Bigdata: Ia: Collaborative Research: Asynchronous Distributed Machine Learning Framework For Multi-Site Collaborative Brain Big Data Mining @ University of Pennsylvania
Recent advances in multimodal brain imaging and high throughput genotyping and sequencing techniques provide exciting new opportunities to ultimately improve our understanding of brain structure and neural dynamics, their genetic architecture, and their influences on cognition and behavior. However, data privacy and security issues have inhibited data sharing across institutes. Emerging multi-site collaborative data analysis can address these issues and facilitate data and computing resource sharing. In collaborative data analysis, the participating institutes keep their own data, which are analyzed and computed locally, and only share the computed results by communicating with a server. The server communicates with all institutes and updates the local models such that the trained machine learning models indirectly use all data and are shared with all institutes. Although some distributed/parallel computation techniques were recently proposed to address big data mining problems, most of them are synchronous models. Asynchronous distributed learning methods are much more efficient, because they allow the server to update the model with information from only one worker node without waiting for slow worker nodes in each round. However, the convergence analysis for the asynchronous distributed algorithms is much more difficult due to the inconsistent variables update across nodes. Thus, it is challenging to design efficient distributed machine learning algorithms for collaborative big data analysis. The research objective of this project is to address the computational challenges in the emerging multi-site collaborative data mining for brain big data. This project seeks to harness the opportunities of designing new efficient asynchronous distributed machine learning algorithms with rigorous theoretical foundations for multi-site collaborative brain big data mining, creating large-scale computational strategies and effective software tools to reveal sophisticated relationships among heterogeneous brain data. This project designs the asynchronous distributed machine learning and principled big data mining models to conduct the comprehensive study of brain imaging genomics and connectomics. Specifically, the principal investigators investigate: 1) collaborative genotype and phenotype association study using new asynchronous doubly stochastic proximal gradient algorithms; 2) communication-efficient multi-site collaborative data integration models to integrate imaging genomics data for predicting outcomes of interest; 3) collaborative deep learning algorithm speedup by the asynchronous distributed algorithms with applications in temporal cognitive change prediction; and 4) new graph convolutional deep learning models for brain network mining. It is innovative to integrate new distributed machine learning and data-intensive computing with brain imaging genomics and connectomics that hold great promise for a systems biology of the brain. The developed methods and tools impact other neuroimaging, genomics, and neuroscience research, and enable investigators working on brain science to effectively test their scientific hypotheses. This project will also facilitate the development of novel educational tools.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
|
0.951 |
2019 — 2020 |
Shen, Li |
R21Activity Code Description: To encourage the development of new research activities in categorical program areas. (Support generally is restricted in level of support and in time.) |
Probing Mechanism and Outcome of Chlamydia Trachomatis Response to Antimicrobial Insults @ Lsu Health Sciences Center
Abstract An obligate intracellular bacterium, Chlamydia trachomatis (Ct), is the leading sexually transmitted bacterial infection worldwide. Chronic Ct infection leads to serious reproductive complications in women, including pelvic inflammatory disease, pelvic pain, tubal infertility, and ectopic pregnancy. Recent compelling molecular data support historical studies showing that Ct can establish chronic infections even with repeated antimicrobial treatments. Yet, the mechanisms underpinning Ct adaptation, survival, and persistence during this onslaught and host immune-imposed antimicrobial insults are unknown. To evaluate antimicrobial responses of intracellular bacteria, it is desirable to establish quantitative relations between the fitness of growing or persisting bacteria and the target host cells. These important relations are challenging to study during Chlamydia infection due to the current limitations in techniques. To overcome this barrier, we have created a robust endogenous promoter- fluorescent protein reporter system. Building on our recent success in probing the central aspect of a unique Ct developmental cycle in situ, we hypothesize that Ct adapts to, and modulates, host cell metabolic pathways to maintain viability during exposure to external and host immune-imposed antimicrobial insults. To test this hypothesis, we propose two Aims: 1. To develop a novel in situ method using fluorescent protein expression levels to quantify interchangeable Ct developmental cycle in live cells. This powerful tool will allow us to quantitatively probe divergent chlamydial forms in a single Ct-containing vacuole and in a culture composed of physiologically and phenotypically different cells; and 2. To characterize key signaling pathways, in Ct and its host cells, that are vital to Ct adaptation and survival, in the presence of antimicrobial insults. Our innovative studies will be achieved using advanced technologies, including gene reporter assays, florescence activated cell sorting (FACS), and dual RNA sequencing (FACS-dual-RNAseq). Greater understanding of how Ct adapts, survives, and persists in the presence of host immunity or antimicrobial agents, as well as reactivates from persistence may provide important new insights into chlamydial pathogenesis. The findings from these studies will provide the foundation to develop new treatment strategies targeting both acute and the chronic, transmissible reservoirs of Ct infection.
|
0.903 |
2020 |
Moore, Jason H. Saykin, Andrew J (co-PI) [⬀] Shen, Li |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Informatics Algorithms For Genomic Analysis of Brain Imaging Data @ University of Pennsylvania
Project Summary Brain imaging genetics studies the relationship between genetic variations and brain imaging quantitative traits (QTs) and offers enormous potential to reveal the genetic underpinning of the neurobiological system that can impact the development of diagnostic, therapeutic and preventative approaches for complex brain disorders. Two critical gaps limiting the progress of brain imaging genetics include (1) the unprecedented scale and complexity of the imaging genetic data sets, and (2) lack of intermediate-level omics data to capture the molecular effects linking genetics to brain QTs. Our prior studies have contributed substantially to addressing the first gap. The proposed project will develop new informatics strategies to bridge the second gap, where valuable existing data in the omics domain will be leveraged to link brain imaging and genetics. In this project, we will focus on transcriptomics, and will make use of major transcriptomics data repositories including Genotype-Tissue Expression (GTEx) Project, UK Brain Expression Consortium (UKBEC), and Allen Human Brain Atlas (AHBA). Our overarching goal is to identify brain imaging genetic associations with evidence manifested in the human brain transcriptome. Our hypothesis is that, with additional source of evidence at the transcriptomic level, the identified brain imaging genetic associations are biologically more meaningful and less likely to be false positives. To achieve our goal, we propose four aims. Aim 1 is to develop novel bi-multivariate models incorporating regional tissue-specific expression quantitative trait locus (eQTL) knowledge for mining brain imaging genetic associations. Given that eQTL is a source of tissue-specific evidence to link genotype, gene expression, and brain QTs, we will develop novel eQTL-guided bi-multivariate models to identify imaging genetic associations potentially evidenced by regional tissue-specific eQTL knowledge. Aim 2 is to develop novel bi-multivariate models incorporating brain-wide genome-wide (BWGW) cross-domain co-expression patterns for mining brain imaging genetics associations. AHBA, a BWGW gene expression database, is a natural connection between genome and brain. We propose to develop novel biclustering and bi-multivariate methods to identify meaningful AHBA modules with cross-domain co-expression patterns, and use these patterns to guide the search for co-expression-aware associations between genetic variations and multimodal brain imaging measures. Aim 3 is to develop open source software tools for structure-aware mining of brain imaging genetic associations. Aim 4 is to perform evaluation and validation on both simulated data and real imaging genetics cohorts. Successful completion of the above aims will produce innovative informatics methods and tools for integrative analysis of imaging, genetics and transcriptomics data to address a critical barrier in brain imaging genetics. Using ADNI and related cohorts as test beds, these methods and tools will be shown to have considerable potential for understanding the molecular mechanism of Alzheimer?s disease, and be expected to impact neurological and psychiatric research in general and benefit public health outcomes.
|
0.951 |
2021 |
Moore, Jason H. Ritchie, Marylyn D Shen, Li |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Artificial Intelligence Strategies For Alzheimer's Disease Research @ University of Pennsylvania
Alzheimer's disease (AD) is a common disease that is partly due to protein misfolding and aggregation. Research on AD is a national priority with 5.5 million Americans affected at an annual cost of more than $250 billion and no available cure. This is despite heavy investments in the collection of diverse clinical and biological data in experimental and population-based studies. Artificial intelligence (AI) and machine learning have the potential to reveal patterns in clinical and multi-source large-scale Alzheimer?s data that have not been found using standard approaches. We propose here a comprehensive biomedical computing and health informatics research project to develop and apply cutting-edge AI algorithms and biomedical software for the analysis of large- scale AD data. At the heart of this proposed informatics program is the PennAI method and software for automating machine learning through an AI algorithm that can learn from prior analyses. This approach takes the guesswork out of picking the right machine learning algorithms and parameter settings thus making this computing technology accessible to everyone. Specifically, we will develop three novel informatics methods to tailor PennAI to the analysis of AD data. First, we will develop a Multi-Modal Interaction (M2I) feature selection algorithm for identifying genetic interactions that are predictive of AD (AIM 1). Second, we will develop a Knowledge-driven Multi-omics Integration (KMI) algorithm for combining omics features for AI analysis of AD (AIM 2). Third, we will develop a Multidimensional Brain Imaging Omics (MBIO) integration framework for the joint analysis of multi-source large-scale data for predicting AD. Finally, we will integrate all three biomedical informatics methods into our open-source PennAI software package and apply it to two large population-based studies of AD. We expect PennAI will reveal new biomarkers for AD that will open the door for better treatments and clinical decision support.
|
0.951 |
2021 |
Kim, Dokyoon Ning, Xia Shen, Li |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Translational Big Data Analytic Approaches to Advance Drug Repurposing For Alzheimer's Disease @ University of Pennsylvania
Project Summary Alzheimer?s disease (AD) is a major public health crisis with no available cure. Given recent failures of many AD clinical trials, there is an urgent need for developing effective strategies to identify new AD targets for disease modeling and new candidates for drug repurposing and development. We propose here a research project to develop transformative big data analytic approaches in the fields of translational bioinformatics, machine learning and deep learning to advance drug repurposing for AD. Our overarching goal is to develop innovative machine learning and deep learning approaches as well as informatics tools and pipelines that leverage big data in relevant biomedical domains. These big data include large-scale genetic, multi-omics, imaging, cognitive and other phenotypic data from landmark AD studies, functional interaction data among drugs, proteins and diseases, pharmacologic perturbation data, electronic health record data, and MarketScan data. Our proposed computational research is aimed at developing novel translational informatics approaches to analyze various types of molecular, clinical and other relevant data to identify individual drugs or drug combinations with favorable efficacy and toxicity profiles as candidates for repositioning against AD or AD- related dementia (ADRD). To achieve our goal, we have four Aims. Aim 1 is to develop network-based multi- omics data integration methods to identify genes and pathways as novel targets for AD drug repositioning research. Aim 2 is to develop informatics strategies to prioritize and evaluate promising candidate targets via examining their associations with AD biomarkers and phenotypes. Aim 3 is to develop knowledge-driven drug repurposing methods using network reinforcement and drug scoring to identify AD candidate drugs. Aim 4 is to prioritize and evaluate the identified candidate drugs for repurposing against AD/ADRD using pharmacologic perturbation, EHR and MarketScan data. Successful completion of these aims will produce novel translational big data analytic methods and tools to improve our understanding of the genetic, molecular and neurobiological mechanisms of AD, facilitate the identification of novel promising targets and drugs for repurposing, and ultimately have a translational impact on disease treatment and prevention. These advances are fundamental to the NIA NAPA goal of effectively treating or preventing AD/ADRD by 2025. The resulting methods and tools are also expected to impact biomedical research in general and benefit public health outcomes.
|
0.951 |
2021 |
Davatzikos, Christos (co-PI) [⬀] Huang, Heng (co-PI) [⬀] Saykin, Andrew J (co-PI) [⬀] Shen, Li Thompson, Paul M |
U01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Ultrascale Machine Learning to Empower Discovery in Alzheimers Disease Biobanks @ University of Southern California
ABSTRACT In response to PAR-19-269 ?Cognitive Systems Analysis of Alzheimer's Disease Genetic and Phenotypic Data (U01 Clinical Trial Not Allowed)?, our project unites experts in AD genomics, machine learning and AI (including deep learning), large-scale data integration, and international data harmonization to work in a carefully-designed Consortium Structure in close partnership with the NIH, ADSP, and NIAGADS. We will develop a suite of complementary big data analytic approaches for ultra-scale analysis of Alzheimer?s Disease (AD) genomic and phenotypic data. The vast data volumes now generated by the Alzheimer?s Disease Sequencing Project (ADSP), National Alzheimer?s Coordinating Center (NACC), Alzheimer?s Disease Neuroimaging Initiative (ADNI), Accelerating Medications Partnership AD (AMP-AD), and UK Biobank (UKBB), far exceed the capacity of all current analytic methods, which have not kept pace with the scale and speed of data collection. This vast amount of genetic and phenotypic data mandates new and more powerful algorithms to: (1) store, manage, and manipulate whole-genome sequences and associated data on an ever-growing scale; (2) discover novel AD risk and protective loci by merging informatics and AD genomics databases; (3) relate whole-genome changes to the ATN(v) biomarkers that now define biological AD. Our Ultrascale Machine Learning Initiative, or ?ULTRA? - will offer new AI and deep learning tools to discover features in massive scale genomics data - relating whole genome data to biomarker features by merging all relevant data sources. Our team of experienced PIs will coordinate efforts across the U.S. to create these large-scale data analytic tools. Our MPI team and 6 Core Leads have decades of experience working together and with the AD community in pioneering machine learning methods for AD genetics and neuroimaging, including leadership of international neuroimaging consortia across the world. Dedicated Cores focus on Genomic, Imaging, and Cognitive Data Harmonization. Curated data will then be efficiently imported into AI approaches and informatics pipelines that will allow the AD research community to leverage ultra-scale, multidimensional genomic and phenotypic data from the ADSP, NACC, ADNI, AMP-AD, and others. Our work is organized by a carefully-designed and coordinated Consortium guided by all stake-holders, clinical leaders, and pioneering analysts in AD genomics and neuroimaging. Our ultrascale AI tools will advance AD genomics research and will include efforts in training, and a dedicated Drug Repurposing Core. This team effort will accelerate understanding of the genetic, molecular and neurobiological mechanisms of AD, yielding significant translational impact on disease and drug development.
|
0.94 |