arXiv subjects

Elif Eyigoz

Publications and source records attributed to Elif Eyigoz.

3 recordsLinked to original sources

arXiv2025

Multi-view biomedical foundation models for molecule-target and property prediction

Quality molecular representations are key to foundation model development in bio-medical research. Previous efforts have typically focused on a single representation or molecular view, which may have strengths or weaknesses on a given task. We develop Multi-view Molecular Embedding with Late Fusion (MMELON), an approach that integrates graph, image and text views in a foundation model setting and may be readily extended to additional representations. Single-view foundation models are each pre-trained on a dataset of up to 200M molecules. The multi-view model performs robustly, matching the performance of the highest-ranked single-view. It is validated on over 120 tasks, including molecular solubility, ADME properties, and activity against G Protein-Coupled receptors (GPCRs). We identify 33 GPCRs that are related to Alzheimer's disease and employ the multi-view model to select strong binders from a compound screen. Predictions are validated through structure-based modeling and identification of key binding motifs.

q-bio.BM↗

arXiv2023

The Impact of COVID-19 on Chronic Pain: Multidimensional Clustering Reveals Deep Insights into Spinal Cord Stimulation Patients

The emergence of COVID-19 offered a unique opportunity to study chronic pain patients as they responded to sudden changes in social environments, increased community stress, and reduced access to care. We report findings from n=70 Spinal Cord Stimulation (SCS) patients before and during initial pandemic stages resulting from advances in home monitoring and artificial intelligence that produced novel insights despite pandemic-related disruptions. From a multi-dimensional array of frequently monitored signals-including mobility, sleep, voice, and psychological assessments-we found that while the overall patient cohort appeared unaffected by the pandemic onset, patients had significantly different individual experiences. Three distinct patient responses (sub-cohorts) were revealed, those with: worsened pain, reduced activities, or improved quality of life. Remarkably, none of the specific measures by themselves were significantly affected; instead, it was their synergy that exposed the effects elicited by the pandemic onset. Partial correlations illustrating linked dimensions by sub-cohort during the pandemic and those associations were different for each sub-cohort before COVID-19, suggesting that daily at-home tele-monitoring of chronic conditions may reveal novel patient types. This work highlights the opportunities afforded by applying modern analytic techniques to more holistic and longitudinal patient outcomes, which might aid clinicians in making more informed treatment decisions in the future.

cs.CE↗

arXiv2022

Health Guardian Platform: A technology stack to accelerate discovery in Digital Health research

This paper highlights the design philosophy and architecture of the Health Guardian, a platform developed by the IBM Digital Health team to accelerate discoveries of new digital biomarkers and development of digital health technologies. The Health Guardian allows for rapid translation of artificial intelligence (AI) research into cloud-based microservices that can be tested with data from clinical cohorts to understand disease and enable early prevention. The platform can be connected to mobile applications, wearables, or Internet of things (IoT) devices to collect health-related data into a secure database. When the analytics are created, the researchers can containerize and deploy their code on the cloud using pre-defined templates, and validate the models using the data collected from one or more sensing devices. The Health Guardian platform currently supports time-series, text, audio, and video inputs with 70+ analytic capabilities and is used for non-commercial scientific research. We provide an example of the Alzheimer's disease (AD) assessment microservice which uses AI methods to extract linguistic features from audio recordings to evaluate an individual's mini-mental state, the likelihood of having AD, and to predict the onset of AD before turning the age of 85. Today, IBM research teams across the globe use the Health Guardian internally as a test bed for early-stage research ideas, and externally with collaborators to support and enhance AI model development and clinical study efforts.

cs.CY↗