Cassie Mitchell

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Associate Professor

Georgia Institute of Technology

United States

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About

Dr. Cassie Mitchell is an Associate Professor at the Georgia Institute of Technology, USA. Her research areas include high-dimensional biomedical data modeling, cognitive impairment progression, and the molecular dynamics of brain connectomes. Recent articles highlight her work on advanced computational frameworks for data analysis in neurodegenerative diseases and cancer survival prediction, showcasing her expertise in artificial intelligence and machine learning applications in healthcare.

Recent Grants

Grant: Close

CAREER: A systems engineering approach to elucidate and treat multi-factorial pathology

Open Date: 2020-06-01

Close Date: 2025-05-31

Grant: Close

A Big Data Approach to BCR ABL Leukemias

Open Date: 2019-04-01

Close Date: 2022-02-28

Grant: Close

Quilting disparate data patches to elucidate Alzheimer’s Disease

Open Date: 2018-01-01

Close Date:

Grant: Close

Optimization of Bi-Pap and PEG intervention protocols for Amyotrophic Lateral Sclerosis

Open Date: 2016-09-01

Close Date: 2018-06-30

Grant: Close

Optimization of Bi-Pap and PEG intervention protocols for Amyotrophic Lateral Sclerosis

Open Date: 2016-09-01

Close Date: 2018-06-30

Articles (20)

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2022

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2021

Collaborators (3)

James Lah

Associate Professor

Emory University

UNITED STATES

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Ashlee Ford Versypt

Associate Professor

State University of New York at Buffalo

UNITED STATES

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vamsi kota

Augusta University

UNITED STATES

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Cassie Mitchell

Research Interests

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About

Recent Grants

Articles (20)

ReduXis: A Comprehensive Framework for Robust Event-Based Modeling and Profiling of High-Dimensional Biomedical Data

Comprehensive Anatomical Staging Predicts Clinical Progression in Mild Cognitive Impairment: A Data-Driven Approach

<tt>TrialSieve</tt>: A Comprehensive Biomedical Information Extraction Framework for PICO, Meta-Analysis, and Drug Repurposing

Network Diffusion-Constrained Variational Generative Models for Investigating the Molecular Dynamics of Brain Connectomes Under Neurodegeneration

Restoring Homeostasis: Treating Amyotrophic Lateral Sclerosis by Resolving Dynamic Regulatory Instability

Cross-Domain Text Mining of Pathophysiological Processes Associated with Diabetic Kidney Disease

Artificial Intelligence-Assisted Comparative Analysis of the Overlapping Molecular Pathophysiology of Alzheimer’s Disease, Amyotrophic Lateral Sclerosis, and Frontotemporal Dementia

An Interpretable Machine Learning Framework for Rare Disease: A Case Study to Stratify Infection Risk in Pediatric Leukemia

Hematological Adverse Events with Tyrosine Kinase Inhibitors for Chronic Myeloid Leukemia: A Systematic Review with Meta-Analysis

Literature-Based Discovery Predicts Antihistamines Are a Promising Repurposed Adjuvant Therapy for Parkinson’s Disease

SeizFt: Interpretable Machine Learning for Seizure Detection Using Wearables

Comparing therapeutic modulators of the SOD1 G93A Amyotrophic Lateral Sclerosis mouse pathophysiology.

Machine Learning Selection of Most Predictive Brain Proteins Suggests Role of Sugar Metabolism in Alzheimer's Disease.

Data Augmentation with Cross-Modal Variational Autoencoders (<tt>DACMVA</tt>) for Cancer Survival Prediction

Literature-Based Discovery to Elucidate the Biological Links between Resistant Hypertension and COVID-19

Cross-Domain Text Mining to Predict Adverse Events from Tyrosine Kinase Inhibitors for Chronic Myeloid Leukemia.

CompositeView: A Network-Based Visualization Tool.

Rule-Enhanced Active Learning for Semi-Automated Weak Supervision.

Optimizations for Computing Relatedness in Biomedical Heterogeneous Information Networks: SemNet 2.0.

Unsupervised Machine Learning to Identify Separable Clinical Alzheimer's Disease Sub-Populations.

Collaborators (3)

James Lah

Ashlee Ford Versypt

vamsi kota

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