Search Results for author:
Found 7 papers, 3 papers with code
Pruning neural network models for gene regulatory dynamics using data and domain knowledge
The practical utility of machine learning models in the sciences often hinges on their interpretability.
Scaling up Continuous-Time Markov Chains Helps Resolve Underspecification
Modeling the time evolution of discrete sets of items (e. g., genetic mutations) is a fundamental problem in many biomedical applications.
DRAGON: Determining Regulatory Associations using Graphical models on multi-Omic Networks
The increasing quantity of multi-omics data, such as methylomic and transcriptomic profiles, collected on the same specimen, or even on the same cell, provide a unique opportunity to explore the complex interactions that define cell phenotype and govern cellular responses to perturbations.
Gene targeting in disease networks
This example demonstrates that gene targeting scores are an invaluable addition to gene expression analysis in the characterization of diseases and other complex phenotypes.
The importance of transparency and reproducibility in artificial intelligence research
In their study, McKinney et al. showed the high potential of artificial intelligence for breast cancer screening.
Applications
Cascade Size Distributions: Why They Matter and How to Compute Them Efficiently
Cascade models are central to understanding, predicting, and controlling epidemic spreading and information propagation.
Estimating sample-specific regulatory networks
We demonstrate the accuracy and applicability of our approach in several data sets, including simulated data, microarray expression data from synchronized yeast cells, and RNA-seq data collected from human lymphoblastoid cell lines.
