457 citations · 725 across the 16 of their papers we have counts for
18 papers
LAFA: A Framework for Reproducible Longitudinal Assessment of Protein Function Annotation Models
An Phan, Yanli Wang, Frimpong Boadu +3
Motivation: Protein function prediction is a challenging task and an open problem in computational biology. The Critical Assessment of protein Function Annotation (CAFA) is a trien…
Learning Tree-Structured Composition of Data Augmentation
Dongyue Li, Kailai Chen, Predrag Radivojac +1
Data augmentation is widely used for training a neural network given little labeled data. A common practice of augmentation training is applying a composition of multiple transform…
CAFA-evaluator: A Python Tool for Benchmarking Ontological Classification Methods
Damiano Piovesan, Davide Zago, Parnal Joshi +8
We present CAFA-evaluator, a powerful Python program designed to evaluate the performance of prediction methods on targets with hierarchical concept dependencies. It generalizes mu…
Current and future directions in network biology
Marinka Zitnik, Michelle M. Li, Aydin Wells +34
Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseas…
Leveraging Structure for Improved Classification of Grouped Biased Data
Daniel Zeiberg, Shantanu Jain, Predrag Radivojac
We consider semi-supervised binary classification for applications in which data points are naturally grouped (e.g., survey responses grouped by state) and the labeled data is bias…
Explaining Deep Tractable Probabilistic Models: The sum-product network case
Athresh Karanam, Saurabh Mathur, Predrag Radivojac +3
We consider the problem of explaining a class of tractable deep probabilistic models, the Sum-Product Networks (SPNs) and present an algorithm ExSPN to generate explanations. To th…
The sequencing and interpretation of the genome obtained from a Serbian individual
Wazim Mohammed Ismail, Kymberleigh A. Pagel, Vikas Pejaver +6
Recent genetic studies and whole-genome sequencing projects have greatly improved our understanding of human variation and clinically actionable genetic information. Smaller ethnic…
Identifiability of two-component skew normal mixtures with one known component
Shantanu Jain, Michael Levine, Predrag Radivojac +1
We give sufficient identifiability conditions for estimating mixing proportions in two-component mixtures of skew normal distributions with one known component. We consider the uni…
Enumerating consistent subgraphs of directed acyclic graphs: an insight into biomedical ontologies
Yisu Peng, Yuxiang Jiang, Predrag Radivojac
Modern problems of concept annotation associate an object of interest (gene, individual, text document) with a set of interrelated textual descriptors (functions, diseases, topics)…
Classification in biological networks with hypergraphlet kernels
Jose Lugo-Martinez, Predrag Radivojac
Biological and cellular systems are often modeled as graphs in which vertices represent objects of interest (genes, proteins, drugs) and edges represent relational ties among these…
Recovering True Classifier Performance in Positive-Unlabeled Learning
Shantanu Jain, Martha White, Predrag Radivojac
A common approach in positive-unlabeled learning is to train a classification model between labeled and unlabeled data. This strategy is in fact known to give an optimal classifier…
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…
Uncovering protein interaction in abstracts and text using a novel linear model and word proximity networks
Alaa Abi-Haidar, Jasleen Kaur, Ana G. Maguitman +5
We participated in three of the protein-protein interaction subtasks of the Second BioCreative Challenge: classification of abstracts relevant for protein-protein interaction (IAS)…