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48,453 grants matching “machine learning”
Optimizing the Human-Computer Interaction in Pathology: Understanding the Impact of Computer-Aided Diagnosis Tools on Pathologists' Interpretive Performance
$591,606Joann G Elmore · University Of California Los Angeles · R01 · FY2025 · CA
Medical Advice from Glaucoma Informatics (MAGI)
$591,603University Of California San Diego · R33 · FY2003 · EY
RNA Regulatory Networks in Neuronal Cell Type Diversity and Function
$591,564Chaolin Zhang · Columbia University Health Sciences · R01 · FY2024 · NS
Implications of metabolism on healthy aging in African and Caucasian Americans: the Health ABC study
$591,550Venkatesh Locharla Murthy · University Of Michigan At Ann Arbor · R01 · FY2022 · AG
Data Science (DS) Core B
$591,537Bonnie Lafleur · University Of Arizona · U19 · FY2023 · AG
Data Science (DS) Core B
$591,536Bonnie Lafleur · University Of Arizona · U19 · FY2022 · AG
** AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** UNDERSTANDING PLANT ROOTS IS CRITICAL TO INCREASING PLANT AND CROP EFFICIENCY AND RESILIANCE. HOWEVER, STUDYING ROOTS IS EXTREMELY CHALLENGING. OUR CURRENT TOOLS AND MECHANISMS ARE VERY LIMITED IN WHAT ROOT INFORMATION THEY CAN COLLECT AND THE SCALE IN WHICH THIS ROOT INFORMATION CAN BE OBTAINED. RECENT ADVANCES IN MACHINE-LEARNING COMBINED WITH IMPROVED IMAGING TECHNOLOGY HAS OPENED DOORS TO COLLECT AND EXPLORE ROOT CHARACTERISTICS OF FIELD-GROWN PLANTS MORE EASILY AND EFFICIENTLY.THIS INCLUDES ROOT IMAGERY FROM VARIOUS IMAGING SOURCES INCLUDING X-RAY COMPUTED TOMOGRAPHY (X-RAY CT) SCANS OF PART OF OR WHOLE ROOT STRUCTURES. HOWEVER, A CRITICAL BOTTLENECK FOR USING THIS BIG DATA, SPECIFICALLY X-RAY CT IMAGERY OF ROOTS, IS THE INABILITY OF MACHINE-LEARNING ALGORITHMS TO RECOGNIZE AND DIFFERENTIATE ROOT FEATURES FROM THOSE OF ORGANIC MATTER AND OTHER NOISE EMBEDDED IN THE IMAGERY. IN THIS PROJECT, WE PROPOSE TO DEVELOP AND APPLY AN APPROACH THAT COUPLES ROOT ARCHITECTURE MODELING WITH GRAPH CONVOLUTIONAL NEURAL NETWORKS (GCNNS) FOR IMPROVED DETECTION AND CHARACTERIZATION OF SWITCHGRASS ROOTS FROM X-RAY CT SCANS OF SOIL CORES. WE WILL DEVELOP, IMPLEMENT AND SHARE NEW MACHINE LEARNING METHODS TO AUTOMATE THE UNDERSTANDING OF X-RAY CT SCANS OF ROOTS. THE OUTCOMES OF THIS RESEARCH WILL INCLUDE A NOVEL ROOT PHENOTYPING FRAMEWORK WITH HIGHLY STREAMLINED WORKFLOW THAT WILL TRANSFORM OUR ABILITY TO DETECT AND EXTRACT FEATURES OF ROOT TRAITS FROM SCANNED SOIL CORE IMAGES FROM THE FIELD.
$591,500University Of Florida · · FY2024 · National Institute of Food and Agriculture
** AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** APPROXIMATELY 1 IN 6 AMERICANS ARE AFFECTED BY FOOD CONTAMINATED WITH DANGEROUS MICROORGANISMS EVERY YEAR. RECURRING, EMERGING, AND PERSISTENT (REP) MICROORGANISMS SUCH AS ESCHERICHIA COLI, SALMONELLA ENTERICA AND LISTERIA MONOCYTOGENES CAN RE-EMERGE PERIODICALLY IN FOOD SYSTEMS, CAUSING REPEATED ACUTE OUTBREAKS, OR PERSIST AND CAUSE ILLNESSES OVER LONG PERIODS OF TIME. THUS, THERE IS AN URGENT NEED TO DEVELOP STRATEGIES TO PREDICT THE SPREAD AND SUBSEQUENTLY CONTROL THESE MICROBIAL CONTAMINANTS IN OUR FOOD SUPPLY. TRADITIONALLY, MICROBIAL BEHAVIOR IS MODELED USING SIMPLE MATHEMATICAL AND STATISTICAL MODELS. HOWEVER, IN RECENT YEARS, GENOMIC AND OTHER 'OMICS'-BASED METHODS ARE INCREASINGLY BEING USED TO MONITOR, IDENTIFY, AND CHARACTERIZE PATHOGENIC MICROORGANISMS, INTRODUCING NOVEL DIMENSIONS TO MICROBIAL DATA. IT IS CRITICAL TO DEVELOP ANALYTICAL TOOLKITS OR RISK MITIGATION STRATEGIES TO ANALYZE AND IDENTIFY USEFUL PATTERNS FROM THIS GENOMIC DATA IN ORDER TO EFFECTIVELY PREDICT AND MANAGE FOOD SAFETY RISK OF REP PATHOGENS TO IMPROVE PUBLIC HEALTH. THIS PROPOSAL AIMS TO DEVELOP NOVEL TOOLS AND PIPELINES TO ANALYZE AND PREDICT THE PRESENCE AND BEHAVIOR OF REP PATHOGENS UNDER THE VARIOUS CONDITIONS OBSERVED IN THE AGRICULTURAL AND FOOD ECOSYSTEM. WE WILL LEVERAGE PUBLICLY AVAILABLE GENOMIC AND PHENOTYPIC DATA FOR REP PATHOGENS FROM FOOD PRODUCTION AND PROCESSING ENVIRONMENTS, METAGENOMICS DATA FROM SAMPLING ACTIVITIES, AND PUBLICLY AVAILABLE ENVIRONMENTAL DATA TO DELINEATE MOLECULAR MARKERS ASSOCIATED WITH THE PERSISTENCE AND EVOLUTION OF REP PATHOGENS. WE WILL EMPLOY A COMBINATION OF MACHINE LEARNING, BIOINFORMATICS ANALYSIS AND ADVANCED COMPUTATIONAL MODELS TO ACHIEVE OUR OBJECTIVES. OUR DEVELOPED MODELS AND TOOLS WILL BE UTILIZED TO DEVELOP A REPRODUCIBLE PIPELINE TO IDENTIFY GENETIC DETERMINANTS OF PATHOGEN PERSISTENCE AND RECURRENCE IN THE FOOD AND AGRICULTURAL DOMAIN. THIS IN TURN WILL ASSIST IN MAKING BETTER RISK MANAGEMENT DECISIONS TO IMPROVE FOOD SAFETY AND PROTECT PUBLIC HEALTH FROM REP PATHOGENS.
$591,500University Of Maryland, College Park · · FY2024 · National Institute of Food and Agriculture
** AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** CROP PLANTS ENCOUNTER MANY DIFFERENT BIOTIC AND ABIOTIC STRESSES THROUGHOUT THEIR GROWTH AND OFTEN MULTIPLE STRESSES OCCUR AT ONCE. AS CLIMATE CHANGE INTENSIFIES THESE COMBINED STRESSES ARE EXPECTED TO OCCUR MORE FREQUENTLY AND PROLONGED. STRESSES INDUCE VARIOUS CHEMICAL AND PHYSIOLOGICAL CHANGES IN PLANTS THAT IF QUANTIFIED CAN PROVIDE KEY INFORMATION ON PLANT HEALTH STATUS AND POTENTIALLY AID FUTURE MANAGEMENT DECISIONS. THERE ARE MANY KINDS OF DATA THAT CAN PROVIDE INSIGHTS INTO HOW PLANTS PERCEIVE STRESSES AND RESPOND TO THEIR ENVIRONMENTS HOWEVER THE METHODS REQUIRED TO GENERATE THESE DATA ARE OFTEN TIME CONSUMING COSTLY AND REQUIRE SPECIALIZED EQUIPMENT. THIS PROJECT WILL DEVELOP INTEGRATED SENSING DEVICES THAT CAN BE PLACED ON THE LEAVES OF PLANTS TO MEASURE THE PRESENCE OF STRESS AGENTS AND THE RESPONSES OF THE PLANTS AS THEY PRODUCE MOLECULAR SIGNALS THAT ENABLE THEM TO RESPOND TO THE STRESSES. THE PLANT SENSORS WILL ALSO MONITOR PHYSIOLOGICAL METRICS SUCH AS LEAF WATER CONTENT TEMPERATURE AND HUMIDITY. THE DATA COLLECTED FROM THE SENSORS WILL BE USED TO TRAIN MACHINE LEARNING MODELS THAT WILL IDENTIFY DISTINCT SIGNATURES FOR EACH INDIVIDUAL STRESS AND RESPONSES TO COMBINED STRESSES. THE PERFORMANCE OF THE SENSORS AND THE DEVELOPED DATA ANALYTICS TOOLS WILL BE TESTED UNDER CLIMATE CHANGE SCENARIOS IN ORDER TO DETERMINE HOW STRESS RESPONSES MAY BE ALTERED IN CROPS IN THE FUTURE. AT THE COMPLETION OF THIS PROJECT IT WILL BE POSSIBLE TO PINPOINT AND DIFFERENTIATE INDIVIDUAL AND COMBINED STRESSES USING THE SENSORS AND DATA ANALYTICS. THE VERSATILITY OF THIS METHOD ALLOWS IT TO ADAPT TO DIVERSE CROPS AND STRESSES FOR FUNDAMENTAL RESEARCH OR THE DEVELOPMENT OF NEW DIAGNOSTIC OR MANAGEMENT TOOLS.
$591,499Iowa State University Of Science And Technology · · FY2025 · National Institute of Food and Agriculture
Computational Toolkit for Normalizing the Impact of CT Acquisition and Reconstruction on Quantitative Image Features
$591,491William Hsu · University Of California Los Angeles · R01 · FY2024 · EB
Explainable Analogical Learning for Oncology Diagnosis and Prediction based on Deep Pathophysiology
$591,468Isabelle Bichindaritz · College At Oswego · R15 · FY2025 · CA
Quantifying articulatory performance in children with dysarthria: Development of an automated metric for clinical use
$591,446Katherine C Hustad · University Of Wisconsin-Madison · R01 · FY2025 · DC
LAGRANGIAN AND COUPLED DATA ASSIMILATION ENHANCED BY MACHINE LEARNING TO IMPROVE OPERATIONAL OCEAN PREDICTION
$591,442University Of Maryland, College Park · · FY2019 · Department of the Navy
Real time risk prognostication via scalable hazard trees and forests
$591,396Hemant Ishwaran · University Of Miami School Of Medicine · R01 · FY2025 · HL
The Gut Microbiome and Serum Metabolites as a Biological Mechanism Underlying Pain in Kidney Transplantation (Biome-KT)
$591,373Mark Lockwood · University Of Illinois At Chicago · R01 · FY2024 · DK
Comparative Effectiveness of EEG-Guided Anti-Seizure Treatment in Acute Brain Injury
$591,352Sahar F Zafar · Massachusetts General Hospital · R01 · FY2025 · NS
Improving glucose control with advanced technology designed for high risk patients with type 1 diabetes
$591,323Jessica R Castle · Oregon Health & Science University · R01 · FY2018 · DK
Drug tolerance, bacterial heterogeneity and adverse TB treatment outcomes
$591,215David R Sherman · Rutgers Biomedical And Health Sciences · U19 · FY2025 · AI
A Wireless micro-ECoG Prosthesis for Speech
$591,137Jonathan Viventi · Duke University · R01 · FY2025 · DC
Deep learning based antibody design using high-throughput affinity testing of synthetic sequences
$591,130David K. Gifford · Massachusetts Institute Of Technology · R01 · FY2020 · CA
Deep learning based antibody design using high-throughput affinity testing of synthetic sequences
$591,130David K. Gifford · Massachusetts Institute Of Technology · R01 · FY2021 · CA
Deep learning based antibody design using high-throughput affinity testing of synthetic sequences
$591,130David K. Gifford · Massachusetts Institute Of Technology · R01 · FY2018 · CA
Administrative Core A
$591,100Gerard David Schellenberg · University Of Pennsylvania · U54 · FY2024 · AG
Dietary and Microbial Reprogramming of Intestinal Microbiota-Produced Metabolites
$591,098Justin Sonnenburg · Stanford University · R01 · FY2024 · DK
Mapping the blood cancer exposome for environmental risk profiles of mature B-cell neoplasms
$591,095Douglas Ian Walker · Emory University · R01 · FY2023 · ES