Biology subjects

Alexeyenko, A.

Publications and source records attributed to Alexeyenko, A..

4 recordsLinked to original sources

bioRxiv2026

Textural features for pathway-level representation of omics data in biological networks

More than 50 years ago, Haralick and co-authors proposed a family of gray-level co-occurrence statistics that became known as textural features. These features are widely used in image analysis, but their application to biological networks has remained limited because cellular networks are sparse, irregular graphs rather than regular pixel grids. This work presents a network-adapted version of Haralick texture analysis for generating pathway-level features from gene-level omics profiles. The resulting profiles reduce dimensionality and can be used as candidate predictors of anti-cancer drug response. Performance of these features is compared with original gene expression variables and with pathway features from network enrichment analysis (NEA), whose robustness has been demonstrated previously. Although technically simpler than NEA, Haralick features showed comparable sensitivity. More importantly, selected Haralick features were preserved between in vitro drug screens and clinical treatment-associated survival analyses, supporting their potential use for prioritizing robust pathway-level drug-response correlates.

systems biology↗

bioRxiv2021

Individualized discovery of rare cancer drivers in global network context

Late advances in genome sequencing expanded the space of known cancer driver genes several-fold. However, most of this surge was based on computational analysis of somatic mutation frequencies and/or their impact on the protein function. On the contrary, experimental research necessarily accounted for functional context of mutations interacting with other genes and conferring cancer phenotypes. Eventually, just such results become "hard currency" of cancer biology. The new method, NEAdriver employs knowledge accumulated thus far in the form of gene interaction networks and functionally annotated pathways in order to recover known and predict novel driver genes. The driver discovery was individualized by accounting for mutations co-occurrence in tumour genomes. For each somatic genome change, probabilistic estimates from two lanes of network analysis were combined into joint likelihoods of being a driver. Thus, ability to detect previously unnoticed candidate driver events emerged from combining individual genomic context with network perspective. The procedure was applied to ten largest cancer cohorts followed by evaluating error rates against previous cancer gene sets. The discovered driver combinations were shown to be informative on cancer outcome. We demonstrate that the individualized discovery revealed driver events which were individually rare, not detectable by other computational approaches, and related to cancer biology domains poorly covered by previous analyses. Considering the novel driver candidates and their constellations in individual tumor genomes opens a novel avenue for personalized cancer medicine.

systems biology↗

bioRxiv2019

A Shh/Gli-driven three-node timer motif controls temporal identity and fate of neural stem cells

How time is measured by neural stem cells during temporal neurogenesis has remained unresolved. By combining experiments and computational modelling, we here define a Shh/Gli-driven three-node timer underlying the sequential generation of motor neurons (MNs) and serotonergic neurons in the brainstem. The timer is founded on temporal decline of Gli-activator and Gli-repressor activities established through downregulation of Gli transcription. The circuitry conforms an incoherent feedforward loop, whereby Gli proteins promote expression of Phox2b and thereby MN-fate, but also account for a delayed activation of a self-promoting Tgf{beta}-node triggering a fate switch by repressing Phox2b. Hysteresis and spatial averaging by diffusion of Tgf{beta} counteracts noise and increases temporal accuracy at the population level. Our study defines how time is reliably encoded during the sequential specification of neurons.

developmental biology↗

bioRxiv2018

Prediction of response to anti-cancer drugs becomes robust via network integration of molecular data

In order to tackle heterogeneity of cancer samples and high data space dimensionality, we propose a method NEAmarker for finding sensitive and robust biomarkers at the pathway level. In this method, scores from network enrichment analysis transform the original space of altered genes into a lower-dimensional space of pathways, which is then correlated with phenotype variables. The analysis was first done on in vitro anti-cancer drug screen datasets and then on clinical data. In parallel, we tested a panel of state-of-the-art enrichment methods. In this comparison, our method proved superior in terms of 1) universal applicability to different data types with a possibility of cross-platform integration, 2) consistency of the discovered correlates between independent drug screens, and 3) ability to explain differential survival of treated patients. Our new in vitro screen validated performance of the discovered multivariate models. Finally, NEAmarker was the only method to discover predictors of both in vitro response and patient survival given administration of the same drug.

cancer biology↗