Software | AutoML | Technology: Tools, Hardware, and Software | All Resources

ALIRO: AI Driven Data Science

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ALIRO is an easy-to-use data science assistant. It allows researchers without machine learning or coding expertise to run supervised machine learning analysis through a clean web interface. It provides results visualization and reproducible scripts so that the analysis can be taken anywhere. And, it has an AI assistant that can choose the analysis to run for you. Dataset profiles are generated and added to a knowledgebase as experiments are run, and the AI assistant learns from this to give more informed recommendations as it is used. Aliro comes with an initial knowledgebase generated from the PMLB benchmark suite.

 

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Ethics and policy | Penn Resources | All Resources

Alliance of Minority Physicians

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The mission of AMP is to develop leaders in clinical, academic, and community medicine through active recruitment, career development, mentorship, social opportunities and community outreach geared towards underrepresented faculty, house staff, and medical students at UPHS, CHOP, and the Perelman School of Medicine. Dr. Iris Reyes provides faculty leadership for AMP.

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AITC Consortium | Basic Information | PennAITech Awardees | All Resources

An Accessible Machine Learning-Based ADRD Screening Tool for Families and Caregivers

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A machine learning-enabled speech-based screening tool for AD/ADRD caregivers
Awardee Organization(s): University of Southern California
Principal Investigator(s): Maja Matarić, PhD
Official Project Title: An Accessible Machine Learning-Based ADRD Screening Tool for Families and Caregivers
AITC Partner: PennAITech
Website(s): www.robotics.usc.edu/~maja

The goal of this project is to develop an app-based screening system capable of detecting early signs of Alzheimer’s Disease (AD) using data captured during sessions of standard clinical AD diagnostics. Approximately 50 million people worldwide are diagnosed with dementia. As of 2021, an estimated 6.2 million Americans, one in nine people 65 and older, are living with AD.The majority of affected people do not obtain early screening toward a timely dementia diagnosis. Consequently, there is a large and rapidly growing need for lowcost, non-invasive, and accessible tools for dementia screening toward alerting families and caregivers and encouraging them to pursue medical evaluation. The proposed app is intended for family members and caregivers and will be designed to be easy to use and encourage regular screening. The goal is for the proposed app to enable convenient early flagging of AD for the general public.

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Clinical/biomedical | Penn Resources | All Resources

Center for Neurodegenerative Disease Research

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The mission of the Center for Neurodegenerative Disease Research (CNDR) is to promote and conduct multidisciplinary clinical and basic research to increase the understanding of the causes and mechanisms leading to brain dysfunction and degeneration in neurodegenerative diseases such as Alzheimer’s disease (AD), Parkinson’s disease (PD), Lewy body dementia (LBD), Frontotemporal degeneration (FTD), Amyotrophic lateral sclerosis (ALS), Primary lateral sclerosis (PLS), Motor neuron disease (MND), and related disorders that occur increasingly with advancing age. Implicit in the mission of the CNDR are two overarching goals: 1.) Find better ways to cure and treat these disorders, 2. Provide training to the next generation of scientists.

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Clinical/biomedical | Penn Resources | All Resources

Center on Alpha-Synuclein Strains in Alzheimer’s Disease and Related Dementias

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The U19 Center and Penn biosample bank that the Penn team has developed over the past 20 years will be a valuable resource to other investigators beyond Penn who pursue research on amyloid polymorphisms and strain. The Penn U19 Center innovates by elucidating mechanisms underlying the heterogeneous progression of cognitive impairment to dementia in LBD compared to AD+aSyn in addition to neurodegeneration mediated by progressive accumulation and cell-to-cell spread of pathological aSyn strains. Thus, the Penn U19 Center seeks to understand the molecular mechanisms that underlie AD+aSyn/LBD heterogeneity.

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AITC Consortium | Basic Information | PennAITech Awardees | All Resources

Conversational Care Technologies

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The PI’s research team will use a human-centered approach to design, build, and evaluate conversational care technologies that scaffold meaningful care discussions between older adults and their informal caregivers
Awardee Organization(s): University of Michigan
Principal Investigator(s): Robin Brewer, PhD
Official Project Title: Conversational Care Technologies
AITC Partner: PennAITech
Website(s): www.umich.edu, www.si.umich.edu

This project focuses on designing and developing conversational care technologies for older adults and their caregivers. In prior work, we surveyed informal caregivers and older adult care receivers to understand their care routines. Survey findings showed how nearly 20% of care partners used voice assistants in their homes, signaling an opportunity to extend research on older adults’ conversational technology use to include care partners. Next, we conducted a diary study and interviews with caregivers and care receivers to investigate gaps in care interactions and conversations. We found that care receivers experienced more communication frustrations than caregivers and that older adult caregivers wanted more opportunities to influence their care routines.
In this project, we will use these findings to develop in-home conversational technologies that use prompts to structure care conversations between older adults and their caregivers.
We contribute a nuanced dyadic perspective to care relationships as most care research focuses solely on caregiver perspectives. We also extend conversational technology research beyond information seeking to include more social uses by developing conversational technology applications with mainstream voice technologies (e.g., Amazon Alexa) to support improved care relationships, social and emotional well-being, and quality of life.

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Software | Technology: Tools, Hardware, and Software | Deep Learning | All Resources

Deep Learning for Toxicology (DTox)

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In drug development, a major reason for attrition is the lack of understanding of cellular mechanisms governing drug toxicity. The black-box nature of conventional classification models has limited their utility in identifying toxicity pathways. Here we developed DTox (Deep learning for Toxicology), an interpretation framework for knowledge-guided neural networks, which can predict compound response to toxicity assays and infer toxicity pathways of individual compounds. We demonstrate that DTox can achieve the same level of predictive performance as conventional models with a significant improvement in interpretability. Using DTox, we were able to rediscover mechanisms of transcription activation by three nuclear receptors, recapitulate cellular activities induced by aromatase inhibitors and PXR agonists, and differentiate distinctive mechanisms leading to HepG2 cytotoxicity. Virtual screening by DTox revealed that compounds with predicted cytotoxicity are at higher risk for clinical hepatic phenotypes. In summary, DTox provides a framework for deciphering cellular mechanisms of toxicity in silico.

 

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