Bud Mishra

Algorithmic Methods to Infer the Evolutionary Trajectories in Cancer Progression

The paper introduces PiCnIc, a machine learning pipeline to infer cancer progression models from NGS data, demonstrating its application to colorectal cancer and suggesting novel hypotheses.

TRONCO: an R package for the inference of cancer progression models from heterogeneous genomic data

TRONCO infers cancer progression models from genomic data, providing population-level and individual-level insights into clonal evolution and mutation accumulation.

Design of the TRONCO BioConductor Package for TRanslational ONCOlogy

TRONCO is an R package for cancer progression modeling using cross-sectional genomic data, offering improved algorithms and tools for analysis and visualization.

CAPRI: Efficient Inference of Cancer Progression Models from Cross-sectional Data

CAPRI infers cancer progression models from cross-sectional data, outperforming existing methods in accuracy and robustness to noise and small sample sizes.

Inferring tree causal models of cancer progression with probability raising

This paper introduces CAPRESE, a framework for inferring cancer progression tree models using probabilistic causation, showing improved performance on synthetic and real datasets.

Power-law Null Model for Bystander Mutations in Cancer

The paper shows cancer CNV segment lengths follow a power-law rather than exponential distribution and improves driver gene detection by using this distribution in the null model.

Algorithms in Stringomics (I): Pattern-Matching against "Stringomes"

The paper introduces data-structures for efficient pattern-matching in structured families of strings, called stringomes, with applications in bioinformatics and cybersecurity.

Genomics via Optical Mapping (I): 0-1 Laws for Mapping with Single Molecules

The paper demonstrates that increasing the number of imaged single molecules in optical mapping data leads to a sharp transition from 0 to 1 probability of successful map computation.

Gappy TotalReCaller for RNASeq Base-Calling and Mapping

The paper introduces Gappy TotalReCaller, a method for RNASeq base-calling and mapping that improves Bayesian base-calling using partial transcript assemblies as priors.