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Found 11 papers, 10 papers with code
Cross-Validation for Training and Testing Co-occurrence Network Inference Algorithms
Previous methods for evaluating the quality of the inferred network include using external data, and network consistency across sub-samples, both which have several drawbacks that limit their applicability in real microbiome composition data sets.
Optimizing ROC Curves with a Sort-Based Surrogate Loss Function for Binary Classification and Changepoint Detection
ROC curves can also be used in other problems that have false positive and true positive rates such as changepoint detection.
Increased peak detection accuracy in over-dispersed ChIP-seq data with supervised segmentation models
We show that the unconstrained multiple changepoint detection model, with alternative noise assumptions and a suitable setup, reduces the over-dispersion exhibited by count data and turns out to detect peaks more accurately than algorithms which rely on these natural assumptions.
Labeled Optimal Partitioning
In partially labeled data, it is important to correctly predict presence/absence of changes in positive/negative labeled regions, in both the train and test sets.
Survival regression with accelerated failure time model in XGBoost
In this work, we implement loss functions for learning accelerated failure time (AFT) models in XGBoost, to increase the support for survival modeling for different kinds of label censoring.
Generalized Functional Pruning Optimal Partitioning (GFPOP) for Constrained Changepoint Detection in Genomic Data
We describe a new algorithm and R package for peak detection in genomic data sets using constrained changepoint algorithms.
Computation
Fast Nonconvex Deconvolution of Calcium Imaging Data
Calcium imaging data promises to transform the field of neuroscience by making it possible to record from large populations of neurons simultaneously.
Methodology Neurons and Cognition Applications
Maximum Margin Interval Trees
Learning a regression function using censored or interval-valued output data is an important problem in fields such as genomics and medicine.
A log-linear time algorithm for constrained changepoint detection
This leads to a new algorithm which can solve problems with arbitrary affine constraints on adjacent segment means, and which has empirical time complexity that is log-linear in the amount of data.
PeakSegJoint: fast supervised peak detection via joint segmentation of multiple count data samples
To select the number of peaks in the segmentation, we propose a supervised penalty learning model.
Support vector comparison machines
In ranking problems, the goal is to learn a ranking function from labeled pairs of input points.
