Biology subjects

Hosking, B.

Publications and source records attributed to Hosking, B..

5 recordsLinked to original sources

bioRxiv2025

Machine Learning-Driven Discovery of Synergistic Protein Interactions Identifies ATL3 as a Putative Biomarker for Cancer Drug Response

BackgroundResistance to targeted molecular therapies--both primary and acquired--remains a major obstacle to effective cancer treatment. Despite extensive research, the molecular determinants of treatment resistance are still incompletely understood, underscoring the need to identify robust resistance drivers to improve therapeutic outcomes. Recently, the ProCan and Wellcome Sanger Institute released the worlds largest pan-cancer proteomic dataset to date, comprising 949 cancer cell lines treated with 625 anti-cancer agents. Despite progress in identifying single protein markers, scalable methods for detecting synergistic protein interactions driving drug susceptibility remain limited. Machine learning models such as random forests may offer a robust framework for capturing non-linear protein interactions across diverse cancer types. ResultsOur study presents synerOmics, a scalable framework for identifying putative synergistic protein interactions by leveraging parent-child co-occurrences in random forest regression trees to reduce the interaction search space. We validated our approach using simulated data and two independent cancer proteomic datasets, identifying both pan-cancer and breast cancer-specific markers associated with drug susceptibility. Synergistic interactions that consistently replicated across datasets were enriched for endoplasmic reticulum stress pathways. Notably, shared targets included established sensitivity markers for tyrosine kinase inhibitors (TKIs) and revealed a resistance-associated network centred on ATL3, which demonstrated both prognostic and predictive relevance. ConclusionsApplying synerOmics to the largest cancer cell line proteomic dataset to date, we identified ATL3 as a strong candidate biomarker for lapatinib resistance in breast cancer, with superior predictive performance compared to the current gold standard sensitivity biomarker, ERBB2. HighlightsO_LIWe present a machine learning framework for drug response biomarker discovery, leveraging the largest proteomic cancer cell line dataset, ProCan-DepMapSanger C_LIO_LIATL3 is proposed as a candidate predictive biomarker for lapatinib resistance in breast cancer C_LIO_LIElevated expression of a novel resistance-conferring subcluster centered on ATL3 is associated with poor prognosis across 11 TCGA cancer types C_LIO_LIBaseline drug resistance may be broadly driven by stress pathways, with ER stress and protein ATL3 implicated in ER-phagy in response to TKIs. C_LI

cancer biology↗

bioRxiv2022

Timing along the cardiac cycle modulates neural signals of reward-based learning

Natural fluctuations in cardiac activity influence brain activity associated with sensory stimuli and affect perceptual decisions about low magnitude, near-threshold stimuli. However, little is known about the impact of fluctuations in heart activity on other internal representations. Here we investigate cardiac influences on learning-related internal representations - absolute and signed prediction errors. By combining machine learning techniques with electroencephalography (EEG) and both simple, direct indices of task performance and computational model-derived indices of learning, we demonstrate that just as people are more sensitive to low magnitude, near threshold sensory stimuli in certain cardiac phases, so are they more sensitive to low magnitude absolute prediction errors in the same cycles. Importantly, however, this occurs even when the low magnitude prediction errors are associated with clearly suprathreshold sensory events. In addition, participants exhibiting stronger difference in their prediction errors representations between cardiac cycles exhibited higher learning rates and greater task accuracy.

neuroscience↗

bioRxiv2019

BitEpi: A Fast and Accurate Exhaustive Higher-Order Epistasis Search

MotivationComplex genetic diseases may be modulated by a large number of epistatic interactions affecting a polygenic phenotype. Identifying these interactions is difficult due to computational complexity, especially in the case of higher-order interactions where more than two genomic variants are involved. ResultsIn this paper, we present BitEpi, a fast and accurate method to test all possible combinations of up to four bi-allelic variants (i.e. Single Nucleotide Variant or SNV for short). BitEpi introduces a novel bitwise algorithm that is 2.1 and 56 times faster for 3-SNV and 4-SNV search, than established software. The novel entropy statistic used in BitEpi is 44% more accurate to identify interactive SNVs, incorporating a p-value-based significance testing. We demonstrate BitEpi on real world data of 4,900 samples and 87,000 SNPs. We also present EpiExplorer to visualize the potentially large number of individual and interacting SNVs in an interactive Cytoscape graph. EpiExplorer uses various visual elements to facilitate the discovery of true biological events in a complex polygenic environment.

bioinformatics↗

bioRxiv2019

VariantSpark, A Random Forest Machine Learning Implementation for Ultra High Dimensional Data

The demands on machine learning methods to cater for ultra high dimensional datasets, datasets with millions of features, have been increasing in domains like life sciences and the Internet of Things (IoT). While Random Forests are suitable for \"wide\" datasets, current implementations such as Googles PLANET lack the ability to scale to such dimensions. Recent improvements by Yggdrasil begin to address these limitations but do not extend to Random Forest. This paper introduces CursedForest, a novel Random Forest implementation on top of Apache Spark and part of the VariantSpark platform, which parallelises processing of all nodes over the entire forest. CursedForest is 9 and up to 89 times faster than Googles PLANET and Yggdrasil, respectively, and is the first method capable of scaling to millions of features.

bioinformatics↗

bioRxiv2019

A framework for the development of a global standardised marine taxon reference image database (SMarTaR-ID) to support image-based analyses

Video and image data are regularly used in the field of benthic ecology to document biodiversity. However, their use is subject to a number of challenges, principally the identification of taxa within the images without associated physical specimens. The challenge of applying traditional taxonomic keys to the identification of fauna from images has led to the development of personal, group, or institution level reference image catalogues of operational taxonomic units (OTUs) or morphospecies. Lack of standardisation among these reference catalogues has led to problems with observer bias and the inability to combine datasets across studies. In addition, lack of a common reference standard is stifling efforts in the application of artificial intelligence to taxon identification. Using the North Atlantic deep sea as a case study, we propose a database structure to facilitate standardisation of morphospecies image catalogues between research groups and support future use in multiple front-end applications. We also propose a framework for coordination of international efforts to develop reference guides for the identification of marine species from images. The proposed structure follows the Darwin Core standard to allow integration with existing databases. We suggest a management framework where high-level taxonomic groups are curated by a regional team, consisting of both end users and taxonomic experts. We identify a mechanism by which overall quality of data within a common reference guide could be raised over the next decade. Finally, we discuss the role of a common reference standard in advancing marine ecology and supporting sustainable use of this ecosystem.

ecology↗