12 papers · ranked by Valyu relevance
A H M Osama Haque, Abdullah Al Fahad, M Sohel Rahman, Md Abul Hassan Samee
Alzheimer’s Disease remains a major public health challenge, requiring insights into feature interactions and temporal trends of feature importance. Community-wide data science competitions such as the TADPOLE Challenge provide platforms to benchmark predictive models using ADNI datasets. While top-performing models…
Trang T. Le, Jason H. Moore
treeheatr is an R package for creating interpretable decision tree visualizations with the data represented as a heatmap at the tree’s leaf nodes. The integrated presentation of the tree structure along with an overview of the data efficiently illustrates how the tree nodes split up the feature space and how well the…
Remco R. Bouckaert, Joseph Heled
Phylogenetic analysis like Bayesian MCMC or bootstrapping result in a collection of trees. Trees are discrete objects and it is generally difficult to get a mental grip on a distributions over trees. Visualisation tools like DensiTree can give good intuition on tree distributions. It works by drawing all trees in the…
Zhikang Liu, Yiyang Niu, Tian Le, Daniel G Chen + 2 more
The rapid maturation of single-cell multi-omics technologies has enabled unprecedented resolution for mapping disease states and identifying disease-associated biomarkers. In practice, biomarkers are often discovered through differential detection that treat genomic features as independent contributors to phenotypes…
Piyush Borole, Tongjie Wang, Antonio Vergari, Ajitha Rajan
Survival analysis refers to statistical procedures used to analyze data that focuses on the time until an event occurs, such as death in cancer patients. Traditionally, the linear Cox Proportional Hazards (CPH) model is widely used due to its inherent interpretability. CPH model help identify key disease-associated…
Björn-Hergen Laabs von Holt, Ana Westenberger, Inke R. König
In life sciences random forests are often used to train predictive models. However, gaining any explanatory insight into the mechanics leading to a specific outcome is rather complex, which impedes the implementation of random forests into clinical practice. By simplifying a complex ensemble of decision trees to a…
Jonathan Warrell, Hussein Mohsen, Mark Gerstein
Deep learning methods have achieved state-of-the-art performance in many domains of artificial intelligence, but are typically hard to interpret. Network interpretation is important for multiple reasons, including knowledge discovery, hypothesis generation, fairness and establishing trust. Model transformations provide…
Mehrshad Sadria, Anita Layton, Gary D. Bader
For predictive computational models to be considered reliable in crucial areas such as biology and medicine, it is essential for them to be accurate, robust, and interpretable. A sufficiently robust model should not have its output affected significantly by a slight change in the input. Also, these models should be…
Laura Lema-Perez, Rafael Muñoz-Tamayo, Jose Garcia-Tirado, Hernan Alvarez
Empirical and phenomenological based models are used to represent biological and physiological processes. Phenomenological models are derived from the knowledge of the mechanisms that underlie the behaviour of the system under study, while empirical models are derived from analysis of data to quantify relationships…
Luise Häuser, Alexandros Stamatakis
While there exists a plethora of prior research on phylogenetic tree shape indices, a largescale analysis of the behavior of these indices on empirical trees has not yet been conducted. Here, we address this by computing 54 indices on more than 45,000,000 empirical trees retrieved from the EvoNAPS and RAxML Grove…
Seyed Mostafa Kia
Brain decoding is a popular multivariate approach for hypothesis testing in neuroimaging. Linear classifiers are widely employed in the brain decoding paradigm to discriminate among experimental conditions. Then, the derived linear weights are visualized in the form of multivariate brain maps to further study the…
Merle Behr, M. Azim Ansari, Axel Munk, Chris Holmes
Tree structures, showing hierarchical relationships and the latent structures between samples, are ubiquitous in genomic and biomedical sciences. A common question in many studies is whether there is an association between a response variable measured on each sample and the latent group structure represented by some…