3 research outputs found
Data_Sheet_1_A deep learning approach to estimating initial conditions of Brain Network Models in reference to measured fMRI data.pdf
IntroductionBrain Network Models (BNMs) are mathematical models that simulate the activity of the entire brain. These models use neural mass models to represent local activity in different brain regions that interact with each other via a global structural network. Researchers have been interested in using these models to explain measured brain activity, particularly resting state functional magnetic resonance imaging (rs-fMRI). BNMs have shown to produce similar properties as measured data computed over longer periods of time such as average functional connectivity (FC), but it is unclear how well simulated trajectories compare to empirical trajectories on a timepoint-by-timepoint basis. During task fMRI, the relevant processes pertaining to task occur over the time frame of the hemodynamic response function, and thus it is important to understand how BNMs capture these dynamics over these short periods.MethodsTo test the nature of BNMs’ short-term trajectories, we used a deep learning technique called Neural ODE to simulate short trajectories from estimated initial conditions based on observed fMRI measurements. To compare to previous methods, we solved for the parameterization of a specific BNM, the Firing Rate Model, using these short-term trajectories as a metric.ResultsOur results show an agreement between parameterization of using previous long-term metrics with the novel short term metrics exists if also considering other factors such as the sensitivity in accuracy with relative to changes in structural connectivity, and the presence of noise.DiscussionTherefore, we conclude that there is evidence that by using Neural ODE, BNMs can be simulated in a meaningful way when comparing against measured data trajectories, although future studies are necessary to establish how BNM activity relate to behavioral variables or to faster neural processes during this time period.</p
Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data
Deep
learning is rapidly advancing many areas of science and technology
with multiple success stories in image, text, voice and video recognition,
robotics, and autonomous driving. In this paper we demonstrate how
deep neural networks (DNN) trained on large transcriptional response
data sets can classify various drugs to therapeutic categories solely
based on their transcriptional profiles. We used the perturbation
samples of 678 drugs across A549, MCF-7, and PC-3 cell lines from
the LINCS Project and linked those to 12 therapeutic use categories
derived from MeSH. To train the DNN, we utilized both gene level transcriptomic
data and transcriptomic data processed using a pathway activation
scoring algorithm, for a pooled data set of samples perturbed with
different concentrations of the drug for 6 and 24 hours. In both pathway
and gene level classification, DNN achieved high classification accuracy
and convincingly outperformed the support vector machine (SVM) model
on every multiclass classification problem, however, models based
on pathway level data performed significantly better. For the first
time we demonstrate a deep learning neural net trained on transcriptomic
data to recognize pharmacological properties of multiple drugs across
different biological systems and conditions. We also propose using
deep neural net confusion matrices for drug repositioning. This work
is a proof of principle for applying deep learning to drug discovery
and development
Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data
Deep
learning is rapidly advancing many areas of science and technology
with multiple success stories in image, text, voice and video recognition,
robotics, and autonomous driving. In this paper we demonstrate how
deep neural networks (DNN) trained on large transcriptional response
data sets can classify various drugs to therapeutic categories solely
based on their transcriptional profiles. We used the perturbation
samples of 678 drugs across A549, MCF-7, and PC-3 cell lines from
the LINCS Project and linked those to 12 therapeutic use categories
derived from MeSH. To train the DNN, we utilized both gene level transcriptomic
data and transcriptomic data processed using a pathway activation
scoring algorithm, for a pooled data set of samples perturbed with
different concentrations of the drug for 6 and 24 hours. In both pathway
and gene level classification, DNN achieved high classification accuracy
and convincingly outperformed the support vector machine (SVM) model
on every multiclass classification problem, however, models based
on pathway level data performed significantly better. For the first
time we demonstrate a deep learning neural net trained on transcriptomic
data to recognize pharmacological properties of multiple drugs across
different biological systems and conditions. We also propose using
deep neural net confusion matrices for drug repositioning. This work
is a proof of principle for applying deep learning to drug discovery
and development