Alternative splicing (AS) is a major contributor to transcriptome and proteome diversity in health and disease. A plethora of tools has been developed for studying alternative splicing in RNA-seq data. Previous benchmarks focused on isoform quantification and mapping. They neglected event detection tools which arguably provide the most detailed insights into the alternative splicing process....
Protein-protein interaction (PPI) networks are an important resource in systems biology. PPI interactions are identified in tedious experiments. Due to the high number of possible interactions, efforts are limited to testing only major protein isoforms, hence neglecting the considerable influence of Alternative splicing (AS) on the interactome.
To close this gap, we developed DIGGER (Domain...
Opinion spreading in a society decides the fate of elections, the success of products, and the impact of political or social movements.
The model by Hegselmann and Krause is a well-known theoretical model to study such opinion formation processes in social networks.
In contrast to many other theoretical models, it does not converge towards a situation where all agents agree on the same...
The class-agnostic discovery of objects in images is a challenging problem in several computer vision pipelines. To address this problem, we propose the efficient object proposal generation system AttentionMask based on a Convolutional Neural Network (CNN). AttentionMask produces pixel-precise object proposals to discover and segment arbitrary objects. Based on a CNN backbone, AttentionMask...
The design and study of plasma-based accelerators greatly relies on numerical simulations with particle-in-cell codes. These simulations accurately model the interaction between plasmas, lasers, and charged particle beams, but are often computationally expensive. Thus, given the wide range of physical parameters involved, optimizing the accelerator performance requires efficient methods that...
Quantum computing is expected to offer numerous applications in science and industry. Its main obstacle is the erroneous behaviour of current devices. To counteract these errors with methods such as quantum error mitigation, understanding them and predicting their impacts on computations is essential. Thus, it is necessary to construct and evaluate accurate noise models. Moreover, the quality...
Viral infections by RNA viruses emerged in the past decade as a global health challenge, given their enormous epidemic potential and their severe pathological outcomes. Despite a relatively high genetic similarity and overall conserved replication strategies, these viruses evolved finely tuned and divergent mechanisms of host exploitation, resulting in extraordinarily distinct tropisms and...
Cancer is a heterogeneous disease characterized by unregulated cell growth and promoted by mutations in cancer driver genes some of which encode suitable drug targets. Since the distinct set of cancer driver genes can vary between and within cancer types, evidence-based selection of drugs is crucial for targeted therapy following the precision medicine paradigm. However, many putative cancer...
Nonlinear optical phenomena are the basis for a wide range of applications such as novel optical sources and measurements or diagnostic techniques. With growing complexity of physics and models, advanced discretization techniques and optimal performance properties of algorithms are of increasing importance for the simulation of nonlinear optical phenomena. Here we confirm the accuracy and...
Bacterial insecticides are important in green agricultural pest control and the combat against arboviruses. They act very specific on target organisms, thus neither harming other insects, nor vertebrates (including humans). Their occurrence as native nanocrystals and lack of structural homologues prevent current structure determination efforts to understand their mode of action. Alphafold v2.0...
Complex applications may demand for analyzing multiple sensor data, such as different camera views
in videos, radar data, or similar.
Furthermore, the need for interpreting these data may aim at different classification tasks, e.g.,
one classifier shall regonize numbers, another colours, a further shall track objects in the same sequence of images.
Even furthermore, those...
At the time when FLASH was constructed, controlling a high-repetition SASE FEL represented a bunch if challenges like the extraordinary requirements on timing on the femtosecond scale and the high number of electron bunches accelerated by the superconducting Linac. Especially the operation of the FLASH1 and FLASH2 beamlines by the same accelerator in parallel requires a reliable...
DAPHNE4NFDI (DAta from PHoton and Neutron Experiments for NFDI) is one of 19 consortia receiving funding as part of the German National Research Data Infrastructure (NFDI e.V.). The aim of DAPHNE4NFDI is to create a comprehensive infrastructure to process research data from large scale photon and neutron infrastructures according to the FAIR principles (Findable, Accessible, Interoperable,...
Area X-ray detectors became bigger (having more megapixels) and faster (measuring more frames per second). This allows to measure dynamical processes in protein crystals with high resolution and below 1ms time scale. The price to pay is the amount of data that such detectors generate. Unfortunately, storage volume is growing much slower. Therefore, there is an increasing gap between the data...
Single-cell RNA sequencing (scRNA-seq) technology provides an unprecedented opportunity to understand gene functions and interactions at single-cell resolution. Various computational methods have been developed for differential expression and co-expression analysis in scRNA-seq data. However, little attention has been paid to differential co-expression analysis that potentially holds valuable...
One of the most prominent challenges in the field of diffractive imaging is the phase retrieval problem: In order to reconstruct an object from its diffraction pattern, the inverse Fourier transform must be computed. This is only possible given the full complex-valued diffraction data, i.e. magnitude and phase. However, in diffractive imaging, generally only magnitudes can be directly measured...
The cleaning of data measured with radio interferometers is an essential task for the scientific use of radio interferometric images. Established methods are often time consuming and require expert knowledge. To generate reproducible images on small time scales, we have developed a prototype deep learning-based reconstruction method. This method takes the incomplete information in Fourier...
Dynamic proton migration along the protein undergoes conformation structural changes being able to promote a folding/unfolding process. Those migration processes have been investigated by challenging near-edge X-ray absorption mass spectrometry (NEXAMS) experiments and computationally expensive calculations at high ab initio theory levels. Therefore, to obtain a solid understanding of...
Since latent disease heterogeneity complicates discovery of biomarkers and elucidation of disease mechanisms, unsupervised stratification based on omics data is an extremely important problem in biomedicine. This problem is traditionally approached by clustering methods which may not be efficient for high-dimensional datasets with multiple overlapping patterns of various sizes. A promising...
The phenomenal growth of computing capabilities have accelerated the ability to combine chemistry, physics and Machine Learning (ML), as a true symbiosis, so as to precisely model and understand complex biomolecular processes at the atomistic scale. However, complexities of proteins and high computational costs of quantum mechanics methods for large systems impose a great challenge in...
Understanding the dynamics such as shape changes observed in oxidation and reduction reactions of metal nanoparticles remains elusive. These processes occur at higher temperatures and under gas exposure on ultrafast time scales of femtoseconds to nanoseconds, and are crucial for understanding of fundamental processes in heterogeneous catalytic reactions. Here we present a detailed study of the...
Almost all areas in the physical or engineering sciences rely on computational models to some extent. The models can be based on fundamental physics processes (physics-based) which typically leads to a set of differential equations. Alternatively, machine learning techniques can be used to infer input-output relations out of very large sets of data. Both approaches come with different...
Small organic molecules that modulate the degradation behavior of magnesium (Mg) constitute benign and useful materials to modify the service environment of light metal materials for specific applications. Particularly Mg—as the lightest structural engineering metal—is promising for advanced technologies that will tackle climate change through improved battery technologies and advanced...
Using hard coherent x-rays, as produced in PETRA III and European XFEL, objects with a size of μm to nm can be imaged with full-field phase contrast, single-pulse imaging. With single-pulse imaging, specifically dynamic processes on the nanosecond-timescales can be investigated. A lens-less imaging setup, which works without an optical instrument between the object and the detector, allows for...
Aggregating transcriptomics data across hospitals can increase sensitivity and robustness of differential expression analyses, yielding deeper clinical insights. As data exchange is often restricted by privacy legislation, meta-analyses are frequently employed to pool local results. However, the accuracy might drop if class labels are inhomogeneously distributed among cohorts. Flimma...
In high energy physics, detailed and time-consuming simulations are used for particle interactions with detectors. For the upcoming High-Luminosity phase of the Large Hadron Collider (HL-LHC), the computational costs of conventional simulation tools exceeds the projected computational resources. Generative machine learning is expected to provide a fast and accurate alternative. The CMS...
Helmholtz Imaging‘s mission is to unlock the potential of imaging in the Helmholtz Association. Image data provide a substantial part of data being generated in scientific research. Helmholtz Imaging is the overarching platform to better leverage and make accessible to everyone the innovative modalities, methodological richness, outstanding expertise and data treasures of the Helmholtz...
Plasma accelerators enable the acceleration of charged particles over short distances due to their multi-GeV/m field gradients, making them a compact alternative to conventional technologies. Despite large progress on beam energy and quality over the last decade, significant progress is still required on beam quality and stability to fill the gap between promising concepts and...
Current noisy intermediate-scale quantum devices suffer from various sources of intrinsic quantum noise. Overcoming the effects of noise is a major challenge, for which different error mitigation and error correction techniques have been proposed.
In this paper, we conduct a first study of the performance of quantum Generative Adversarial Networks (qGANs) in the presence of different types of...
Deep learning is a standard tool in the field of high-energy physics, facilitating considerable sensitivity enhancements for numerous analysis strategies. In particular, in identification of physics objects, such as jet flavor tagging, complex neural network architectures play a major role. However, these methods are reliant on accurate simulations. Mismodeling can lead to non-negligible...
Cryo-EM is a popular technique for understanding the structure of biological molecules. At intermediate resolutions (worse than ~4.5 Å), building and assessing the quality of atomic models derived from cryo-EM data is particularly difficult. At this resolution range, existing X-ray models or models derived from machine-learning based structure prediction approaches such as AlphaFold2 offer...
For optimal operation, accelerators and FELs require precise control of their control parameters. Lasers are of critical importance for photocathode, FEL seeding, and probe lasers. We will show our current results and plans to optimize the performance (pulse parameters, fast set-point tuning, stability) of our photocathode and pump-probe lasers using AI methods.
In an accelerator, the...
Cyber-Physical Systems (CPS) consist of embedded digital devices while interacting with their physical environment. Typical examples range from simple heating systems over robotic subsystems to highly complex control systems, e.g., industrial production systems or particle accelerators and their subsystems. Understanding and modeling these systems is difficult because they consist of multiple,...
With ongoing and future experiments, we are set to enter a more data-driven era in astronomy and astrophysics, for example with interferometric measurements of the 21-cm signal but also with observations in the far-infrared, optical, UV, and beyond. Both larger-scale techniques such as multi-line intensity mapping and higher sensitivity surveys warrant the need for efficient data reduction and...
The Hamburg Leibniz ScienceCampus "Integrative Analysis of pathogen-induced Compartments" InterACt has set itself the goal of better understanding the role of compartments in the course of infection.
InterACt investigates the interaction between pathogens such as viruses, bacteria, and parasites and the affected host. During the cellular infection cycle, pathogens use the existing reaction...
In high-energy particle physics, complex Monte Carlo simulations are needed to connect the theory to measurable quantities. Often, the significant computational cost of these programs becomes a bottleneck in physics analyses.
In this contribution, we evaluate an approach based on a Deep Neural Network to reweight simulations to different models or model parameters, using the full kinematic...
In recent years, serial femtosecond crystallography (SFX) has made remarkable progress for the measurement of macromolecular structures and dynamics using intense femtosecond duration pulses from X-ray Free Electron Laser (FEL). In these experiments, FEL X-ray pulses are fired at a jet of protein crystals, and the resulting diffraction pattern is measured for each pulse. If the pulse hits...
Beam optics matching is a daily routine in the operation of an X-ray free-electron laser facility. Usually, linear optics is employed to conduct the beam matching in the control room. However, the collective effects like space charge dominate the electron bunch in the low-energy region which decreases the accuracy of the existing tool. Therefore, we proposed a scheme to construct a surrogate...
Single particle cryo-electron microscopy (cryo-EM) is an increasingly important method for determining the three-dimensional structure of proteins. As a single particle technique, it allows for the elucidation of large macromolecular complexes, provides information on protein dynamics and gives access to proteins that are difficult to crystallize.
For this purpose, molecules in aqueous...
Experimental protein structures can provide valuable insights into the structural consequences and therefore the biological effect of a mutation. However, often structures for both the wild-type and the mutant are not available for comparison. To address this issue we developed a new tool for database search of mutant structures for a given wild-type structure. Using MicroMiner we can provide...
Finding new indications for approved drugs is a promising alternative to de novo drug development, an often lengthy and costly process. Systems medicine has brought forth several different approaches to tackle this important task. We recently published NeDRex, a network medicine tool for the identification of disease modules and drug repurposing. NeDRex-Web (https://web.nedrex.net) brings...
In recent years, the use of machine learning methods has proved to be capable of considerably speeding up both fundamental and applied research. Accelerator physics applications have also profited from the power of these tools. This implies a wide spectrum of applications from beam measurements to machine performance optimisation.
In this contribution a neural network is used to optimise the...
To date the application of X-ray crystallography has resulted in the largest amount of resolved protein structures. With novel and upcoming sources of radiation, like XFELs, the range of measurable biological molecules increases everyday. Conventional crystallography relies on refining the structure against a set of observed Bragg peaks from the crystallized molecule. However, the given...
The European XFEL is the largest currently operated linear particle accelerator in the world. It provides measurements requiring timing with an error margin in the femtosecond range for most subsystems within the facility. For this purpose, an optical synchronization system is installed at the European XFEL to stabilize critical accelerator components in time.
The main goal of the project is...
DASHH is an interdisciplinary graduate school that offers challenging PhD topics at the interface of the natural sciences, applied mathematics, and computer science. Here, highly talented graduates can do innovative data science research, acquiring and deepening unique insights with our partner institutes, the Deutsches Elektronen-Synchrotron, Universität Hamburg, Hamburg University of...
The simulation of particle showers in calorimeters is a computational demanding process. Deep generative models have been suggested to replace these computations. One of the complexities of this approach is the dimensionality of the data produced by high granularity calorimeters. One possible solution could be progressively growing the GAN to handle this dimensionality. In this study,...
The ProteinsPlus web server (https://proteins.plus)[1] offers modelling support for numerous challenges concerning the in-depth investigation of biomolecules. Its unique tools provide easy access to various structure-based analyses for interdisciplinary researchers through an intuitive user interface. Users can perform numerous computational studies for more than 174,000 three-dimensional...
In High Energy Physics, the interaction of particles with matter at
the detectors are best simulated with the GEANT4 software. Alternatively,
less precise but faster simulations are sometimes preferred to
reach higher statistical precision. We present recent progress of refinement
of fast simulations with ML techniques to enhance the quality of
such fast simulations. We demonstrate the...
Reinforcement learning (RL) has enabled the development of intelligent controllers for complex tasks that previously required human intuition to solve. In the context of particle accelerators, there exist many such tasks and solving them with conventional methods takes away from scarce experiment time and limits the operability of accelerators. We demonstrate how to successfully apply RL to...
Some machine learning algorithms use statistical gradient-based learning methods in a data driven way to solve problems. These methods find correlations in the presented datasets and, thus, also for problems that are difficult to solve with classical algorithms. Lately, so-called artificial neural networks (ANNs) have become one of the most important and indispensable machine learning tools in...
With the development of more intense and coherent X-ray sources there is an ongoing drive to design and develop better X-ray focusing optics, which could focus X-ray beams down to about 1 nanometer [1]. Such optics would have a major impact in the field of X-ray microscopy and various modalities of X-ray imaging that investigate nanostructured materials and biological samples in-situ and...
The state space of a quantum-mechanical system grows exponentially in the number of its classical degrees of freedom. Thus, efficient approximations are crucial for extracting physical information from this vast space. In the variational approach, computations are performed on trial states determined by a tractable number of parameters. Recently, the so-called neural quantum states (NQS) have...
Capillarity-driven flows in pores a few nanometers in diameter play an important role in many natural and technological processes, for example in clay swelling, frost heave, catalysis and transport across artificial nanostructures, bio-membranes and tissues [1]. Here we present molecular dynamics simulations modelling the capillary flow of water into silica nano-pores (MCM-41) of around 3 nm...
Weakly-bound complexes are very appealing for experimental investigations of resonances in dissociation dynamics, which is of vital importance to roaming reactions. Planning and elucidating experiments requires accurate quantum mechanical calculations of (ro-)vibrational energies up to dissociation, which is a challenging task for these systems because of their flexible degrees of freedom and...
Meta-analysis has been established as an effective approach to combining summary statistics of several genome-wide association studies (GWAS). However, the accuracy of meta-analysis can be attenuated in the presence of cross-study heterogeneity. We present sPLINK, a hybrid federated and user-friendly tool, which performs privacy-aware GWAS on distributed datasets while preserving the accuracy...
Introduction: Alternative splicing (AS) drives protein and transcript diversity and is known to play a role in many diseases. The exact mechanisms controlling the AS machinery are currently insufficiently understood. During disease progression or organism development, AS may lead to isoform switches (IS) that follow temporal patterns. Several IS genes occurring at the same time point could...
Nowadays, conducting high-energy physics experiments depend on energetic and multi-spectral lasers. However, a laser is a dangerous light source that could rapidly cause permanent damage to human eyes. Currently, researchers at advanced optics laboratories at particle accelerator facilities use safety goggles based on optical density filters as eye protectors to reduce the amount of laser...
Voting, or more generally taking decision in groups, is seen as a common procedure in our culture. We vote for our representatives, we find available time slots for a group meeting, we answer a survey on our favourite films, or we vote for the best poster. In this poster, we discuss different vote algorithms, their properties, important paradoxes, and concrete implementations. In particular,...
MicroRNAs (miRNAs) are small non-coding RNA molecules that regulate gene expression after transcription. They target specific sequences - binding sites - on mRNAs and silence them by degradation. In complex diseases, such as cancer, epigenetic dysregulation including miRNA dysregulation plays a significant role. Epigenetic therapy targets these regulatory mechanisms, but a better understanding...
Outrunning radiation damage, femtosecond pulses of x-ray free-electron lasers (XFELs) open up the possibility of imaging the structure and dynamics of uncrystallized single-macromolecules, frozen in time at room-temperature, at ultrafast timescales. Imaging light-induced ultrafast dynamics in single-macromolecules in real-time is one of the key applications of XFELs. However, photoactive...
Due to its high sensitivity and resolving power, gas chromatography ion mobility spectrometry (GC-IMS) is an emerging benchtop technique for non-target screening of complex sample materials. Given the wide range of applications, such as food authenticity, custom data analysis workflows are needed. As a common basis, they necessarily share many functionalities such as file input/output,...
