NextVir: Enabling Classification of Tumor-Causing Viruses with Genomic Foundation Models
John Robertson, Shorya Consul, Haris Vikalo
PLOS Computational Biology, 2025
Paper
Many cancers are associated with viral infections, yet identifying tumor-causing viruses from sequencing data remains a challenge due to the high diversity of oncoviral families and the limited availability of labeled data. We present NextVir, a foundational model-based framework for oncoviral read classification. NextVir is the first method to leverage genomic foundation models for this task, and is capable of distinguishing reads from 9 tumor-causing viruses and humans with an accuracy exceeding 90%.
XVir: A Transformer-Based Architecture for Identifying Viral Reads from Cancer Samples
Shorya Consul, John Robertson, Haris Vikalo
Journal of Computational Biology, 2025
Paper
Many cancers can be linked to viral infections. With the advent of modern sequencing technologies, we now have access to massive amounts of tumor data, which in turn have allowed studies of the associations between viruses and cancers. However, the high diversity of oncoviral families makes detecting viral DNA challenging, thereby complicating the study of these associations. To that end, we propose XVir, a transformer-based deep learning architecture to reliably identify viral DNA present in human tumors. Results on semi-experimental data demonstrate that XVir is capable of achieving high detection accuracy, generally outperforming state-of-the-art competing methods while being more compact and less computationally demanding.
XHap: Haplotype Assembly using Long-distance Read Correlations learned by Transformers
Shorya Consul, Ziqi Ke, Haris Vikalo
Bioinformatics Advances, 2023
Paper | Code
Reconstructing haplotypes of an organism from a set of sequencing reads is a computationally challenging (NP-hard) problem. In reference-guided settings, at the core of haplotype assembly is the task of clustering reads according to their origin, i.e. grouping together reads that sample the same haplotype. Read length limitations and sequencing errors render this problem difficult even for diploids; the complexity of the problem grows with the ploidy of the organism. We present XHap, a novel method for haplotype assembly that aims to learn correlations between pairs of sequencing reads, including those that do not overlap but may be separated by large genomic distances, and utilize the learned correlations to assemble the haplotypes. This is accomplished by leveraging transformers, a powerful deep-learning technique that relies on the attention mechanism to discover dependencies between non-overlapping reads. Experiments on semi-experimental and real data demonstrate that the proposed method significantly outperforms state-of-the-art techniques in diploid and polyploid haplotype assembly tasks on both short and long sequencing reads.
RF-based Network Inference: Theoretical Foundations
Hasan Burhan Beytur, Shorya Consul, Gustavo de Vecianan, Haris Vikalo
IEEE Military Communications Conference (MILCOM), 2021
Paper
We consider RF-based network inference based on channel usage. The proposed approaches rely on distributed spectrum sensing and are agnostic to the content and communication protocols. We consider inference based solely on observing nodes’ channel usage and show it is equivalent to a Boolean matrix decomposition problem, which in general does not have a unique solution and is an NP-hard problem. We provide necessary and sufficient conditions for the Boolean matrix decomposition problem has a unique solution, i.e., for the network to be recoverable. We also propose a low-complexity network recovery algorithm that finds the unique solution under the recoverability conditions. In addition to that we provide an analysis of the required observation time to collect necessary channel usage data needed for the network recovery algorithm.
Differentially Private Median Forests for Regression and Classification
Shorya Consul, Sinead Williamson
Preprint
Decision forests are popular for both regression and classification but require a large number of queries for training. This makes attaining differential privacy especially challenging. We proposed a novel scheme, DiPriMe forests, that ensures differential privacy while maintaining high utility, i.e., high performance while guaranteeing differential privacy.
Reconstructing Intra-tumor Heterogenity via Convex Optimization and Branch-and-Bound Search
Shorya Consul, Haris Vikalo
ACM Conference on Bioinformatics, Computational Biology and Health Informatics (ACM-BCB), 2019
Paper | Code
Reconstructing tumor populations from heterogeneous samples from high-throughput sequencing data is highly valuable area of study due to its potential to inform targeted studies and treatments. This, however, is a challenging task due to the complex mutations present and read lengths being too short to span regions exhibiting structural variations. We present a novel algorithmic framework, AMTHet, to infer the tumor clonal populations and their frequencies from a heterogeneous sample based on copy number variations.
A MAP Framework for Support Recovery of Sparse Signals Using Orthogonal Least Squares
Shorya Consul, Abolfazl Hashemi, Haris Vikalo
International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018
Paper
We propose MAP-AOLS, an algorithm that leverages the statistical information about the sensing matrix and signal to greedily reconstruct sparse binary signals from their compressed measurements. This stands in contrast to conventional greedy algorithms, such as OLS and OMP, that perform reconstruction without utilizing any knowledge about the statistical distributions.
