activity
20082022
most citedUncovering protein interaction in abstracts and text using a novel linear model and word proximity networks

33 citations · 39 across the 5 of their papers we have counts for

collaborators

7 papers

stat.ML2022

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…

q-bio.GN2018

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…

math.ST20171 cited

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…

cs.DS2017

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)…

stat.ML2017

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…

stat.ML20175 cited

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…