457 citations · 725 across the 16 of their papers we have counts for
6 papers · 1 filter
Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data
Makoto Yamada, Jiliang Tang, Jose Lugo-Martinez +10
Machine learning methods are used to discover complex nonlinear relationships in biological and medical data. However, sophisticated learning models are computationally unfeasible…
Estimating the class prior and posterior from noisy positives and unlabeled data
Shantanu Jain, Martha White, Predrag Radivojac
We develop a classification algorithm for estimating posterior distributions from positive-unlabeled data, that is robust to noise in the positive labels and effective for high-dim…
A new class of metrics for learning on real-valued and structured data
Ruiyu Yang, Yuxiang Jiang, Scott Mathews +3
We propose a new class of metrics on sets, vectors, and functions that can be used in various stages of data mining, including exploratory data analysis, learning, and result inter…
Nonparametric semi-supervised learning of class proportions
Shantanu Jain, Martha White, Michael W. Trosset +1
The problem of developing binary classifiers from positive and unlabeled data is often encountered in machine learning. A common requirement in this setting is to approximate poste…
Community-Wide Evaluation of Computational Function Prediction
Iddo Friedberg, Predrag Radivojac
A biological experiment is the most reliable way of assigning function to a protein. However, in the era of high-throughput sequencing, scientists are unable to carry out experimen…
An expanded evaluation of protein function prediction methods shows an improvement in accuracy
Yuxiang Jiang, Tal Ronnen Oron, Wyatt T Clark +144
Background: The increasing volume and variety of genotypic and phenotypic data is a major defining characteristic of modern biomedical sciences. At the same time, the limitations i…