Informatics Seminar Recordings

Deep Learning Architecture for Extracting Protein-Protein Interactions

An End-to-End Deep Learning Architecture for Extracting Protein-Protein Interactions Affected by Genetic Mutations

As part of the BioCreative VI Track IV we built a supervised relation extraction model capable of taking a test article and returning a list of interacting protein pairs identified by their Entrez Gene IDs. Such pairs represent proteins participating in a binary protein-protein interaction (PPI) relation where the interaction is additionally affected by a genetic mutation (PPIm). In this study, we explored a PPIm relation extraction by deploying a three-component pipeline involving deep learning-based named entity recognition and relation classification models along with a knowledge-based approach for gene normalization. We propose several recall-focused improvements to our original challenge entry which placed 2nd in the competition. On exact matching, the new system achieved test results of 37.78% micro-F1 with a precision of 38.22% and recall of 37.34% which corresponds to an improvement by approximately 3 micro-F1 points. When matching on HomoloGene IDs, we report similarly competitive test results at 46.17% micro-F1 with a precision and recall of 46.67% and 45.59%, corresponding to an improvement of more than 8 micro-F1 points over the prior best result. 

Link to recording: 

Streaming: 

https://gwu.webex.com/gwu/ldr.php?RCID=0c2ec6daa0c891665a16f30f3a651f6e

Download: 

https://gwu.webex.com/gwu/lsr.php?RCID=97c4c13c636e27bf1203d47ebe002b2e


Characterization of Critically Ill Patients 

Characterization of Critically Ill Patients: A Clinical Application of the Health Facts Data Set 

Using the Health Facts EMR data, critically ill pediatric patients that had at least one admission to the Intensive Care Unit (ICU) were characterized in terms of the number of hours they were administered with drugs usually administrated when either intubated or in mechanical ventilation. The study analyzed the vectors containing the number of hours each combination of medicines was administered to each patient during different periods of ICU admission and floor admission, using a class of Bayesian regression models with the Dirichlet-Multinomial distribution for the response and random effects to capture the inherent variability of each encounter and hospital, adjusting for demographic information. During this seminar, we will describe the process of cohort and records selection, the model and the interpretation of the parameters, and the results of the characterization. We will also explain how the model can be used for treatment comparisons for similar patients in different hospitals.

Link to recording: 

Streaming: 

https://gwu.webex.com/gwu/ldr.php?RCID=44c5abef9c8ac1e13a70902e7513a357

Download: 

https://gwu.webex.com/gwu/lsr.php?RCID=70ce253ecb4d0b757b46297cd6abae6f


Clinical Outcome Prediction through Deep Learning

Clinical Outcome Prediction through Deep Learning

Accurately predicting clinical outcomes in advance can benefit both healthcare providers and patients, though it remains a challenging task. Artificial Intelligence (AI) has recently generated much excitement due to the breakthroughs made by Deep Neural Networks (DNNs) on many tasks such as image classification and speech recognition. Inspired by those developments, there has been great interest in applying DNNs to the biomedical domain. In this seminar, I will present a DNN-based predictive modeling approach applied to two clinical use cases. DNN models are usually considered as black boxes which would hinder their acceptance by clinicians. Therefore, we also developed a novel method for explaining the predictions of our DNN models.

Link to recording: 

Streaming: https://gwu.webex.com/gwu/ldr.php?RCID=6b4f78fcca2d6e47c6f6576c3e8c613e

Download: https://gwu.webex.com/gwu/lsr.php?RCID=535725886993615bce6077a2e09a7a1f

The FABRIC environment

The FABRIC environment: Architectural Features and Big Data Analytics.

The Flexible Architecture for Building Research Informatics Collaborations (FABRIC) is an informatics platform (in development) which offers a service oriented research toolbox that investigators, clinicians, and patient advocates can use to easily access a wide array of data repositories integrated with customizable query tools. This cloud environment is able to support the rapid formation of dedicated cross-domain research teams, the sharing of raw and de-identified datasets in secured enclaves, and the access to a suite of advanced analytics tools commonly used throughout clinical translational research (CTR). This seminar will focus on some of the architectural features of FABRIC, its connection to the Colonial One HPC cluster, and the integration of the HealthFacts database.

Link to recording: 

Streaming: https://gwu.webex.com/gwu/ldr.php?RCID=660e0fb4a361e2dbe81583aeceb2d7a7

Download: https://gwu.webex.com/gwu/lsr.php?RCID=c5289e6cdc48877f01df247dd04b5704