A5050873282- Machine Learning in Healthcare
- Artificial Intelligence in Healthcare
- Streptococcal Infections and Treatments
- Machine Learning and Data Classification
- RNA modifications and cancer
- Respiratory viral infections research
- Cutaneous Melanoma Detection and Management
- Bacterial Identification and Susceptibility Testing
- Microbial Metabolic Engineering and Bioproduction
- Immunotherapy and Immune Responses
- vaccines and immunoinformatics approaches
- Clostridium difficile and Clostridium perfringens research
- Gene expression and cancer classification
- Protein Structure and Dynamics
- Law, logistics, and international trade
- Monoclonal and Polyclonal Antibodies Research
- Multimedia Communication and Technology
- Molecular Biology Techniques and Applications
- Digital Imaging for Blood Diseases
- RNA Research and Splicing
- Oral Health Pathology and Treatment
- Genital Health and Disease
- RNA and protein synthesis mechanisms
- Sepsis Diagnosis and Treatment
- COVID-19 diagnosis using AI
Stanford University
2017-2021
Georgia Institute of Technology
2016
Indian Institute of Technology Madras
2013
RNA-binding proteins (RBPs) control the fate of nearly every transcript in a cell. However, no existing approach for studying these posttranscriptional gene regulators combines transcriptome-wide throughput and biophysical precision. Here, we describe an assay that accomplishes this. Using commonly available hardware, built customizable, open-source platform leverages inherent Illumina technology direct measurements. We used to quantitatively measure binding affinity prototypical RBP Vts1...
<title>Abstract</title> Many patients in the emergency department present with signs and symptoms that arouse concern for sepsis; however, other explanations are also possible. There currently no rapid tests used clinical practice reliably distinguish presence of a bacterial or viral infection vs. non-infectious etiology can predict patient’s likelihood to decompensate. The diagnostic prognostic uncertainty “gray zone” complicates decision begin therapy as clinicians need balance risk...
Abstract Predicting the binding affinity between MHC proteins and their peptide ligands is a key problem in computational immunology. State of art performance currently achieved by allele-specific predictor NetMHC pan-allele NetMHCpan, both which are ensembles shallow neural networks. We explore an intermediate prediction: training predictors with synthetic samples generated imputation peptide-MHC matrix. find that strategy useful on alleles very little data. have implemented our as...
Acute infection, if not rapidly and accurately detected, can lead to sepsis, organ failure even death. Current detection of acute infection as well assessment a patient's severity illness are imperfect. Characterization immune response by quantifying expression levels specific genes from blood represents potentially more timely precise means accomplishing both tasks. Machine learning methods provide platform leverage this host for development deployment-ready classification models....
We applied machine learning to the unmet medical need of rapid and accurate diagnosis prognosis acute infections sepsis in emergency departments. Our solution consists a Myrna (TM) Instrument embedded TriVerity classifiers. The instrument measures abundances 29 messenger RNAs patient's blood, subsequently used as features for learning. classifiers convert input an intuitive test report comprising separate likelihoods (1) bacterial infection (2) viral infection, (3) severity (need Intensive...
Acute infection, if not rapidly and accurately detected, can lead to sepsis, organ failure even death. Current detection of acute infection as well assessment a patient's severity illness are imperfect. Characterization immune response by quantifying expression levels specific genes from blood represents potentially more timely precise means accomplishing both tasks. Machine learning methods provide platform leverage this 'host response' for development deployment-ready classification...