145 citations · 793 across the 36 of their papers we have counts for
10 papers · 1 filter
Generative and reproducible benchmarks for comprehensive evaluation of machine learning classifiers
Patryk Orzechowski, Jason H. Moore
Understanding the strengths and weaknesses of machine learning (ML) algorithms is crucial for determine their scope of application. Here, we introduce the DIverse and GENerative ML…
EBIC.JL -- an Efficient Implementation of Evolutionary Biclustering Algorithm in Julia
Paweł Renc, Patryk Orzechowski, Aleksander Byrski +2
Biclustering is a data mining technique which searches for local patterns in numeric tabular data with main application in bioinformatics. This technique has shown promise in multi…
PMLB v1.0: An open source dataset collection for benchmarking machine learning methods
Joseph D. Romano, Trang T. Le, William La Cava +7
Motivation: Novel machine learning and statistical modeling studies rely on standardized comparisons to existing methods using well-studied benchmark datasets. Few tools exist that…
A Rigorous Machine Learning Analysis Pipeline for Biomedical Binary Classification: Application in Pancreatic Cancer Nested Case-control Studies with Implications for Bias Assessments
Ryan J. Urbanowicz, Pranshu Suri, Yuhan Cui +4
Machine learning (ML) offers a collection of powerful approaches for detecting and modeling associations, often applied to data having a large number of features and/or complex ass…
Is deep learning necessary for simple classification tasks?
Joseph D. Romano, Trang T. Le, Weixuan Fu +1
Automated machine learning (AutoML) and deep learning (DL) are two cutting-edge paradigms used to solve a myriad of inductive learning tasks. In spite of their successes, little gu…
Evaluating recommender systems for AI-driven biomedical informatics
William La Cava, Heather Williams, Weixuan Fu +3
Motivation: Many researchers with domain expertise are unable to easily apply machine learning to their bioinformatics data due to a lack of machine learning and/or coding expertis…
EBIC: an evolutionary-based parallel biclustering algorithm for pattern discover
Patryk Orzechowski, Moshe Sipper, Xiuzhen Huang +1
In this paper a novel biclustering algorithm based on artificial intelligence (AI) is introduced. The method called EBIC aims to detect biologically meaningful, order-preserving pa…
Benchmarking Relief-Based Feature Selection Methods for Bioinformatics Data Mining
Ryan J. Urbanowicz, Randal S. Olson, Peter Schmitt +2
Modern biomedical data mining requires feature selection methods that can (1) be applied to large scale feature spaces (e.g. `omics' data), (2) function in noisy problems, (3) dete…
PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison
Randal S. Olson, William La Cava, Patryk Orzechowski +2
The selection, development, or comparison of machine learning methods in data mining can be a difficult task based on the target problem and goals of a particular study. Numerous p…
Automating biomedical data science through tree-based pipeline optimization
Randal S. Olson, Ryan J. Urbanowicz, Peter C. Andrews +3
Over the past decade, data science and machine learning has grown from a mysterious art form to a staple tool across a variety of fields in academia, business, and government. In t…