@article{21982, abstract = {A floating Leidenfrost droplet exhibits curvature inversion of its underside, due to the balance of vapor pressure and surface tension. Using interferometric imaging, we find different behavior for a levitated hydrogel sphere. Curvature inversion is observed briefly just after deposition, but quickly gives way to a steady state with no inversion. We show the essential role of vaporization in shaping the underbelly of the hydrogel, where changes due to direct mass loss are more significant than the balance of vapor pressure and elastic forces.}, author = {Diaz Melian, Vicente L and Lenton, Isaac C and Binysh, Jack and Souslov, Anton and Waitukaitis, Scott R}, issn = {2470-0053}, journal = {Physical Review E}, number = {5}, publisher = {American Physical Society}, title = {{Geometry of the vapor layer under a Leidenfrost hydrogel sphere}}, doi = {10.1103/m7gr-2t6j}, volume = {113}, year = {2026}, } @article{21987, abstract = {We introduce JODIE, a genetic joint modeling approach that estimates how DNA loci influence human traits by partitioning genetic effects into four components: direct effects (from a child’s alleles), indirect maternal and paternal effects (from parents’ alleles), and parent-of-origin (PofO) effects (dependent on parental transmission of alleles), while uniquely accounting for assortative mating. We analyze 30,000 child-mother-father trios from the Estonian Biobank and the Norwegian Mother, Father, and Child Cohort, focusing on height, body mass index, and childhood educational test scores. We find direct effects to be the largest contributor to trait variation, but combined, indirect parental and PofO effects are similarly substantial. We support our results by within-family genome-wide association testing and identify 276 independently associated DNA regions with a complex interplay between direct, indirect, and PofO effects. By joint modeling, we show that direct, indirect, and PofO effects collectively shape human phenotypic variation across loci genome-wide.}, author = {Krätschmer, Ilse and Hegemann, Laura and Hofmeister, Robin J. and Corfield, Elizabeth C. and Mahmoudi, Mahdi and Delaneau, Olivier and Andreassen, Ole A. and Campbell, Archie and Hayward, Caroline and Marioni, Riccardo E. and Ystrom, Eivind and Havdahl, Alexandra and Robinson, Matthew Richard}, issn = {2666-979X}, journal = {Cell Genomics}, publisher = {Elsevier}, title = {{Separating direct, indirect, and parent-of-origin genetic effects in the human population}}, doi = {10.1016/j.xgen.2026.101277}, year = {2026}, } @article{21013, abstract = {We have addressed convective self‐aggregation (CSA) in steady and oscillating sea surface temperature (SST) and solar radiation (SOLIN) cloud‐resolving model simulations in a non‐rotating radiative‐convective equilibrium (RCE) framework. Our experiment designs are motivated by land‐ocean heterogeneity of atmospheric convection. The steady and oscillating forcings are idealizations of ocean and land conditions, respectively, based on their differences in heat capacities. In both kinds of simulations, the diurnal mean SST and SOLIN are the same, and both SST and SOLIN are only varied in time (i.e., they are spatially homogeneous at any given time). We find that diurnally oscillating forcing accelerates CSA. Stronger long‐wave cooling in dry regions at night and during the warm SST phase (late afternoon) both allow the long‐wave feedback, known to favor aggregation, to intensify compared to steady forcing simulations. In addition to the long‐wave, reduced short‐wave warming in dry regions (during the day) further enhances radiative cooling there compared to moist regions. Overall, the radiative cooling is enhanced in dry regions compared to neighboring moist convective regions. A dry subsidence is driven by this net radiative (short‐wave plus long‐wave) cooling, consistent with earlier work on CSA. Stronger radiative cooling allows stronger subsidence which allows low‐level circulation to more efficiently transport moisture and energy up‐gradient, driving convection to aggregate faster. We also note a sensitivity of our experimental setup to initial conditions, more so at warmer SST. This stochastic behavior might be critical in reconciling the differences of opinion regarding the response of convection aggregation to oscillating SST forcing.}, author = {GOSWAMI, BIDYUT B and Lu, Ziyin and Muller, Caroline J}, issn = {1942-2466}, journal = {Journal of Advances in Modeling Earth Systems}, number = {1}, publisher = {Wiley}, title = {{Convective self‐aggregation in diurnally oscillating sea surface temperature and solar forcing experiments}}, doi = {10.1029/2024ms004576}, volume = {18}, year = {2026}, } @article{21015, abstract = {Early embryo geometry is one of the most invariant species-specific traits, yet its role in ensuring developmental reproducibility and robustness remains underexplored. Here we show that in zebrafish, the geometry of the fertilized egg—specifically its curvature and volume—serves as a critical initial condition triggering a cascade of events that influence development. The embryo geometry guides patterned asymmetric cell divisions in the blastoderm, generating radial gradients of cell volume and nucleocytoplasmic ratio. These gradients generate mitotic phase waves, with the nucleocytoplasmic ratio determining individual cell cycle periods independently of other cells. We demonstrate that reducing cell autonomy reshapes these waves, emphasizing the instructive role of geometry-derived volume patterns in setting the intrinsic period of the cell cycle oscillator. In addition to organizing cell cycles, early embryo geometry spatially patterns zygotic genome activation at the midblastula transition, a key step in establishing embryonic autonomy. Disrupting the embryo shape alters the zygotic genome activation pattern and causes ectopic germ layer specification, underscoring the developmental significance of geometry. Together, our findings reveal a symmetry-breaking function of early embryo geometry in coordinating cell cycle and transcriptional patterning.}, author = {Mishra, Nikhil and Li, Yuting I and Hannezo, Edouard B and Heisenberg, Carl-Philipp J}, issn = {1745-2481}, journal = {Nature Physics}, pages = {139--150}, publisher = {Springer Nature}, title = {{Geometry-driven asymmetric cell divisions pattern cell cycles and zygotic genome activation in the zebrafish embryo}}, doi = {10.1038/s41567-025-03122-1}, volume = {22}, year = {2026}, } @misc{21137, author = {Naik, Suyash}, publisher = {Institute of Science and Technology Austria}, title = {{Data associated with Keratins coordinate tissue spreading }}, doi = {10.15479/AT-ISTA-21137}, year = {2026}, } @article{21485, abstract = {Insulating oxides are among the most abundant solid materials in the universe1,2,3. Of the many ways in which they influence natural phenomena, perhaps the most consequential is their capacity to transfer electrical charge during contact4,5,6,7,8,9,10—which occurs even between samples of the same oxide—yet the symmetry-breaking parameter that causes this remains unidentified11,12. Here we show that adventitious carbonaceous molecules adsorbed from the environment are the symmetry-breaking factor in same-material oxide contact electrification (CE). We use acoustic levitation to measure charge exchange between a sphere and a plate composed of identical amorphous silicon dioxide (SiO2). Although charging polarity is random for co-prepared samples, we control it with baking or plasma treatment. Observing the charge-exchange relaxation afterwards, we see dynamics over a timescale of hours and connect this directly to the presence of adventitious carbon with time-of-flight mass spectrometry, low-energy ion scattering and infrared spectroscopy. Going further, we confirm that adventitious carbon can even determine charge exchange among different oxides. Our results identify the symmetry-breaking parameter that causes insulating oxides to exchange charge in settings ranging from desert sands4 to volcanic plumes5,6, while simultaneously highlighting an overlooked factor in CE more broadly.}, author = {Grosjean, Galien M and Ostermann, Markus and Sauer, Markus and Hahn, Michael and Pichler, Christian M. and Fahrnberger, Florian and Pertl, Felix and Balazs, Daniel and Link, Mason M. and Kim, Seong H. and Schrader, Devin L. and Blanco, Adriana and Gracia, Francisco and Mujica, Nicolás and Waitukaitis, Scott R}, issn = {1476-4687}, journal = {Nature}, number = {8106}, pages = {626--631}, publisher = {Springer Nature}, title = {{Adventitious carbon breaks symmetry in oxide contact electrification}}, doi = {10.1038/s41586-025-10088-w}, volume = {651}, year = {2026}, } @article{21762, abstract = {Bacteria, like eukaryotes, use conserved cytoskeletal systems for intracellular organization. The plasmid-encoded ParMRC system forms actin-like filaments that segregate low–copy number plasmids. In multicellular cyanobacteria such as Anabaena sp., we found that a chromosomally encoded ParMR system has evolved into a cytoskeletal system named CorMR with a function in cell shape control rather than DNA segregation. Live-cell imaging, in vitro reconstitution, and cryo–electron microscopy revealed that CorM formed dynamically unstable, antiparallel double-stranded filaments that were recruited to the membrane by CorR through an amphipathic helix conserved in multicellular cyanobacteria. CorMR filaments were regulated by MinC, which excluded them from the poles and division plane. Comparative genomics indicated that the repurposing of ParMR and Min systems coevolved with cyanobacterial multicellularity, highlighting the evolutionary plasticity of cytoskeletal systems in bacteria.}, author = {Springstein, Benjamin L and Javoor, Manjunath and Megrian, Daniela and Hajdu, Roman and Hanke, Dustin M. and Zens, Bettina and Weiss, Gregor L. and Schur, Florian Km and Loose, Martin}, issn = {1095-9203}, journal = {Science}, number = {6795}, publisher = {AAAS}, title = {{Repurposing of a DNA segregation machinery into a cytoskeletal system controlling cell shape}}, doi = {10.1126/science.aea6343}, volume = {392}, year = {2026}, } @phdthesis{21854, abstract = {As neural-network-based models grow both in size and popularity, interest has grown in making the models smaller and more efficient to train. To that end, many methods have been proposed to prune models by reducing their number of nonzero parameters. Additionally, parameter-efficient fine-tuning, in which a much smaller number of parameters than the total contained in the model is updated during training, has become very popular, especially in the space of Large Language Models. At the same time, the increasingly routine deployment of machine learning in real-world applications has spurred a drive to make them more trustworthy - in the sense of, among other things, being unbiased, interpretable, and editable. In this thesis, we examine the interplay between efficiency and trustworthiness. First, we analyze the effects of model pruning on bias in computer vision models, demonstrating that increased sparsity leads to greater bias, largely as a function of increased model uncertainty in marginal cases. Based on this observation, we propose several bias mitigation techniques. Then, we demonstrate that example-specific model pruning can improve model interpretation methods while improving pruning efficiency to make example-specific model pruning feasible in real time. Then, we investigate the effectiveness of parameter-efficient and data-efficient model personalization via fine-tuning, demonstrating that it is highly feasible with very small computational and data resources. Finally, we consider efficiency in editing model knowledge using a custom synthetic data framework, demonstrating that parameter-efficient, low-rank fine-tuning frequently outperforms full-rank fine-tuning, and, additionally, that restricting which model blocks are fine-tuned frequently improves results. Together, the results in this thesis provide new insights and techniques for combining trustworthiness and efficiency during neural network inference and training. -----------------“In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of [name of university or educational entity]’s products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink. If applicable, University Microfilms and/or ProQuest Library, or the Archives of Canada may supply single copies of the dissertation.”}, author = {Iofinova, Eugenia B}, issn = {2663-337X}, pages = {237}, publisher = {Institute of Science and Technology Austria}, title = {{On the utility and effects of efficiency in artificial neural networks}}, doi = {10.15479/AT-ISTA-21854}, year = {2026}, } @unpublished{21859, abstract = {As artificial neural networks, and specifically large language models, have improved rapidly in capabilities and quality, they have increasingly been deployed in real-world applications, from customer service to Google search, despite the fact that they frequently make factually incorrect or undesirable statements. This trend has inspired practical and academic interest in model editing, that is, in adjusting the weights of the model to modify its likely outputs for queries relating to a specific fact or set of facts. This may be done either to amend a fact or set of facts, for instance, to fix a frequent error in the training data, or to suppress a fact or set of facts entirely, for instance, in case of dangerous knowledge. Multiple methods have been proposed to do such edits. However, at the same time, it has been shown that such model editing can be brittle and incomplete. Moreover the effectiveness of any model editing method necessarily depends on the data on which the model is trained, and, therefore, a good understanding of the interaction of the training data distribution and the way it is stored in the network is necessary and helpful to reliably perform model editing. However, working with large language models trained on real-world data does not allow us to understand this relationship or fully measure the effects of model editing. We therefore propose Behemoth, a fully synthetic data generation framework. To demonstrate the practical insights from the framework, we explore model editing in the context of simple tabular data, demonstrating surprising findings that, in some cases, echo real-world results, for instance, that in some cases restricting the update rank results in a more effective update.}, author = {Iofinova, Eugenia B and Alistarh, Dan-Adrian}, booktitle = {arXiv}, title = {{Behemoth: Benchmarking unlearning in LLMs using fully synthetic data}}, doi = {10.48550/arXiv.2601.23153}, year = {2026}, } @phdthesis{21918, author = {Khudiakova, Kseniia}, issn = {2663-337X}, pages = {89}, publisher = {Institute of Science and Technology Austria}, title = {{How epistasis and purifying selection shape genetic diversity}}, doi = {10.15479/AT-ISTA-21918}, year = {2026}, } @article{22148, abstract = {How the twin-arginine translocase (Tat) system transports fully folded substrate proteins across cellular membranes without disrupting membrane integrity has been a fundamental question in cell biology for decades. The Tat system, found in prokaryotes and plant organelles, recognizes a cargo signal peptide via a conserved twin-arginine motif. The multi-subunit Tat complex facilitates the proton-motive-force-dependent translocation process, yet its overall architecture has remained unknown. Here, we present the cryo-electron microscopy (cryo-EM) structure of the Escherichia coli (E. coli) trimeric TatB₃C₃ complex with bound substrate SufI, assembled in vivo. The complex adopts an unusual, wide-open, bowl-shaped architecture with a polar inner cavity. Unexpectedly, the cargo is engaged in a dual-contact mode: while the signal peptide binds inside one TatBC unit, the folded domain docks tightly onto an adjacent unit, possibly performing a proofreading function. This structure provides a mechanistic framework for substrate engagement and suggests the direct involvement of the entire Tat complex in substrate translocation.}, author = {Zhao, Ziyu and Sazanov, Leonid A}, issn = {1097-4164}, journal = {Molecular Cell}, publisher = {Elsevier}, title = {{Structure of E. Coli twin-arginine translocase (Tat) complex with bound cargo}}, doi = {10.1016/j.molcel.2026.05.026}, year = {2026}, } @article{22229, abstract = {Hippocampal CA3 pyramidal neurons (PNs) form the largest autoassociative network in the mammalian brain. Whether CA3–CA3 recurrent connectivity is genetically preconfigured or environmentally shaped during ongoing memory storage is currently unknown. To address this question, we performed multicellular patch-clamp-based circuit mapping of up to eight CA3 PNs in the mouse hippocampus at multiple postnatal time points (P7–8, P18–25, and P45–50). Here, we show that the hippocampal CA3 network undergoes a developmental transformation from local, dense, and random connectivity to a distributed, sparse, and structured configuration. Thus, sparse and structured connectivity may emerge via experience-dependent mechanisms. In parallel, the strength of single synapses is downregulated; single synaptic events are sufficient to trigger postsynaptic spiking early in development, whereas spatial summation of several inputs is required at later time points. Biologically inspired models of memory storage by Hebbian synaptic plasticity and retrieval via pattern completion suggest that developmental changes improve specific aspects of memory storage and retrieval. Our results imply a developmental transformation of the neuronal code and the memory functions in the hippocampal CA3 network.}, author = {Vargas Barroso, Victor M and Watson, Jake and Navas Olivé, Andrea C and Schlögl, Alois and Jonas, Peter M}, issn = {2041-1723}, journal = {Nature Communications}, publisher = {Springer Nature}, title = {{Developmental emergence of sparse and structured synaptic connectivity in the hippocampal CA3 memory circuit}}, doi = {10.1038/s41467-026-71914-x}, volume = {17}, year = {2026}, } @article{21923, abstract = {The appearance of simulated natural phenomena heavily depends on the way surfaces are textured. However, applying texture maps to dynamic deformable surfaces presents a significant challenge, due to ever-shifting differences in length scales involved. When these surfaces move and advect the texture along with them, their final appearance degrades as deformed regions dramatically distort their texture map. Modifications to the texture directly at the pixel level in response to the deformation may introduce ghosting artifacts and look unnatural. In the real world, the appearance of surface details on a deforming material changes through the interplay of physical processes such as rupturing, exposure of internal structure, or wrinkling. Motivated by these behaviors, in this work we explore how physical principles can guide the texturing methods based on the measure of surface deformation. We present two novel wave-based procedural texturing algorithms which reproduce common physical properties like advection and self-similarity, enabling the plausible animation of deforming objects with extreme texture map distortions. Our algorithms are fully procedural, require no actual physics simulation, and store no state or history of deformation besides the input UV map, making them highly parallelizable on the GPU and efficient enough for real-time applications. We show the versatility of the method by animating physical phenomena with extreme deformations such as flowing lava, stretching putty and outpouring sludge.}, author = {Kalinov, Aleksei and Ly, Mickaël and Hafner, Christian and Wojtan, Christopher J}, issn = {0730-0301}, journal = {ACM Transactions on Graphics}, keywords = {Procedural animation}, location = {Los Angeles, CA, United States}, number = {4}, publisher = {Association for Computing Machinery}, title = {{Physics-inspired procedural texturing of extremely deformable surfaces}}, doi = {10.1145/3811353}, volume = {45}, year = {2026}, } @phdthesis{21423, author = {Dunajova, Zuzana}, isbn = {978-3-99078-076-3}, issn = {2663-337X}, pages = {110}, publisher = {Institute of Science and Technology Austria}, title = {{Geometry-driven self-organization of migrating cells and chiral filaments}}, doi = {10.15479/AT-ISTA-21423}, year = {2026}, } @misc{21439, abstract = {These files contain supplementary movies accompanying the PhD thesis “Geometry-driven self-organization of migrating cells and chiral filaments” by Zuzana Dunajova (2026). The videos provide additional visual material supporting the experiments and results described in the thesis.}, author = {Dunajova, Zuzana}, publisher = {Institute of Science and Technology Austria}, title = {{Supplementary movies to PhD thesis “Geometry-driven self-organization of migrating cells and chiral filaments”}}, doi = {10.15479/AT-ISTA-21439}, year = {2026}, } @article{21161, abstract = {In many species, sex-biased expression is widespread and thought to contribute to sexual dimorphism. While bulk RNA-sequencing has been instrumental in identifying strongly sex-biased genes, it lacks resolution to assess variation across cell-types and tissue compartments. Using single-nucleus expression data from the Fly Cell Atlas, we investigate sex differences in adult Drosophila melanogaster. We find that differences in cell-type composition between the sexes are not a major source of sex-bias, as for the vast majority of genes, the degree of sex-bias is similar regardless of whether sex differences in cell-type composition are controlled for or not. Our analysis confirms a deficit of X-linked male-biased genes in the body’s somatic tissues that is widespread across cell-types. We also find the excess of X-linked female-biased genes to be associated with nervous system cells in the head but with epithelial cells in the body’s somatic tissues, showing that single-nucleus data crucially resolves sex-bias at the cell-type level. We investigate dosage compensation (DC) across 15 tissues and 17 cell-types. We observe that it varies throughout the body. Surprisingly, we observe a lack of DC in a cluster of main cells within the male accessory glands. This result highlights the importance of understanding context-dependent DC.}, author = {De Castro Barbosa Rodrigues Barata, Carolina and Vicoso, Beatriz}, issn = {1471-2954}, journal = {Proceedings of the Royal Society B Biological Sciences}, number = {2063}, publisher = {Royal Society of London}, title = {{Single-nucleus resolution of sex-biased expression and dosage compensation in Drosophila melanogaster}}, doi = {10.1098/rspb.2025.2471}, volume = {293}, year = {2026}, } @article{22333, abstract = {RNA polymerase II (Pol II) must be assembled in the cytoplasm before it enters the nucleus, where it transcribes protein-coding genes. Although transcription by Pol II is intensively studied, how this central multi-subunit enzyme is made and the role of dedicated assembly factors remains unclear. Here, we report the integrative structural analysis of a native human Pol II from the cytoplasm captured near the end of biogenesis. The complex contains Gdown1 and three biogenesis factors – RPAP2 and the critical small GTPases GPN1 and GPN3. Cryo-EM analysis of the complex reveals how Gdown1 and RPAP2 associate with Pol II and prevent the premature association of transcription factors. Further biochemical and cryo-EM analysis reveals how RPAP2 tethers GPN1–GPN3 to the complex and how the assembly of the RPAP2–GPN1–GPN3 complex is controlled by GTP hydrolysis. The combined results uncover a network of interactions that chaperone cytoplasmic Pol II to prevent aberrant interactions, reveal a molecular switch regulating biogenesis factor association, and suggest a general mechanism for the action of GPN-loop GTPase family of enzymes.}, author = {Hlavata, Annamaria and Neuditschko, Benjamin and Schellhaas, Ulla and Plaschka, Clemens and Herzog, Franz and Bernecky, Carrie A}, issn = {2041-1723}, journal = {Nature Communications}, publisher = {Springer Nature}, title = {{Structure of cytoplasmic RNA polymerase II}}, doi = {10.1038/s41467-026-75416-8}, year = {2026}, } @phdthesis{21198, abstract = {In recent years there has been a massive increase in the amount of data generated in a decentralized manner. Ever more powerful edge devices, such as smartphones, have become ubiquitous in most societies on earth. Through text typed, photos taken and apps used, these devices, which we refer to as clients, generate enormous amounts of high quality and complex data. Moreover, the nature of these devices means the data they generate is often sensitive and privacy concerns prevent it being gathered and stored in a central location. This presents a challenge to the modern machine learning paradigm that requires central access to large amounts of data. Federated learning (FL) has emerged as one of the answers to this problem. Rather than bringing the data to the model, FL sends the model to the data. Model training takes place on device, with periodically synchronized updates, allowing data to remain locally stored. While this approach offers significant privacy advantages it comes with its own set of unique challenges. These include: data heterogeneity, the notion that different devices generate data in distinct ways which can negatively impact training dynamics; systems heterogeneity, meaning that different devices may have differing hardware specifications; high communication costs, which are induced by the repeated transferring of models over the network and low device computational power, which limits the use of larger models on device. In this thesis we present a range of methods for federated learning. We focus primarily on the challenge of data heterogeneity, though the methods presented are designed to be well adapted to the other challenges of a federated setting, such as the constraints of limited compute and communication overhead. We first present a method for explicitly modeling client data heterogeneity. The approach formulates clients as samples from a certain probability distribution and infers the parameters of this distribution from the available training clients. This learned distribution then represents the heterogeneity present among the clients and can be sampled from in order to create new simulated clients that are similar to the real clients we have observed so far. Following this we present two methods for directly dealing with data heterogeneity through personalization. Highly heterogeneous client data distributions can mean that learning a single global model becomes suboptimal, and some form of personalization of models to each individual client is required. Our approaches are based around hypernetworks, which we use to generate personalized model parameters without the need for additional training or finetuning. In the first approach we focus on generating full parameterizations of client models using learned embeddings of client data and labels, with a hypernetwork located on the central server. In the second approach we address the more challenging scenario where we want to generate a personalized model for a client without any label information. The hypernetwork is trained to generate a low dimensional representation of a client’s personalized model parameters, allowing it to be transferred to and run on the client devices. In our final presented method, we change our focus and rather than aim to directly address the challenge of data heterogeneity, we instead ensure we are unaffected by it. This is done in the context of k-means clustering and we present a method for federated clustering with a focus on added privacy guarantees.}, author = {Scott, Jonathan A}, issn = {2663-337X}, pages = {158}, publisher = {Institute of Science and Technology Austria}, title = {{Data heterogeneity and personalization in federated learning}}, doi = {10.15479/AT-ISTA-21198}, year = {2026}, } @phdthesis{21021, abstract = {This thesis examines how geometry and topology intersect in the representation, transformation, and analysis of complex shapes. It considers how continuous manifolds relate to their discrete analogues, how topological structures evolve in persistence vineyards, and how tools from topological data analysis can illuminate problems in mathematical physics. Central to this exploration is the question of how structure, both geometric and topological, persists or changes under approximation, sampling, or deformation. The work develops new approaches to skeletal and grid-based representations of surfaces, reveals the full expressive capacity of persistence vineyards, and applies topological methods to the longstanding problem of equilibria in electrostatic fields. These threads braid together into a broader understanding of how topology and geometry inform one another across theory, computation, and application.}, author = {Fillmore, Christopher D}, issn = {2663-337X}, pages = {122}, publisher = {Institute of Science and Technology Austria}, title = {{Braiding geometry and topology to study shapes and data}}, doi = {10.15479/AT-ISTA-21021}, year = {2026}, } @phdthesis{22258, abstract = {Uncovering the genetic architecture of complex traits and pinpointing causal molecular drivers require the ability to distinguish true signals from noise within massive, high-dimensional omics datasets. To extract meaningful biological insights from these datasets, such as identifying causal genetic variants and proteins, scalable and accurate inference methods are essential. To this end, this thesis develops novel Bayesian inference frameworks based on Vector Approximate Message Passing and demonstrates their effectiveness in the modeling of disease onset times and quantitative physical and clinical measures. First, we introduce gVAMP, a Bayesian framework tailored for Genome-Wide Association Studies that enables the joint modeling of quantitative complex traits across millions of genetic variants. gVAMP demonstrates superior accuracy in variable selection and out-of-sample polygenic risk prediction compared to state-of-the-art approaches. We model human height using 17 million whole-genome sequence variants from the UK Biobank, incorporating a vast number of rare variants and revealing novel associations. gVAMP achieves a prediction accuracy of approximately 46% for human height, representing the highest reported performance for this trait to date. Second, we present vampW, a Bayesian framework for survival analysis applied to proteomic data. By effectively handling right-censoring and complex protein dependencies within the UK Biobank Pharma Proteomics Project dataset, vampW identifies 219 protein associations across 24 disease outcomes, the majority of which are not among the top marginal discoveries. We further adjust protein levels for exponential age effects, yielding 1,308 associations and highlighting the sensitivity of the analysis to the chosen age-correction methodology. Finally, vampW improves upon the variable selection capabilities of the commonly used (penalized) variants of the Cox proportional hazards model and delivers state-of-the-art out-of-sample prediction of disease onset times. Collectively, these methods provide powerful tools for dissecting the genetic architecture of complex traits and the proteomic drivers of disease onset. Furthermore, by delivering accurate polygenic risk scores and precise predictions of onset times, this work advances the capabilities of personalized medicine and clinical risk stratification.}, author = {Depope, Al}, issn = {2663-337X}, keywords = {Approximate Message Passing, GWAS, Genomics, Proteomics, Survival modeling}, pages = {169}, publisher = {Institute of Science and Technology Austria}, title = {{From sparse selection to risk prediction: Approximate message passing for proteomic survival models and large-scale genomics}}, doi = {10.15479/AT-ISTA-22258}, year = {2026}, }