@inproceedings{IravaniConrad2019, author = {Iravani, Sahar and Conrad, Tim}, title = {Deep Learning for Proteomics Data for Feature Selection and Classification}, volume = {11713}, booktitle = {Machine Learning and Knowledge Extraction. CD-MAKE 2019}, editor = {Holzinger, A. and Kieseberg, P. and Tjoa, A. and Weippl, E.}, publisher = {Springer, Cham}, doi = {10.1007/978-3-030-29726-8_19}, year = {2019}, language = {en} } @phdthesis{Iravani2022, author = {Iravani, Sahar}, title = {Interpretable Deep Learning Approaches for Biomarker Detection from High-Dimensional Biomedical Data}, year = {2022}, language = {en} } @article{EigenLadenburgerBrenceetal.2025, author = {Eigen, Lennart and Ladenburger, Pius and Brence, Blaž and Shubitidze, Ani and Baum, Daniel and Hildebrandt, Thomas and Brecht, Michael}, title = {Elephant trunk tip musculature reflects species differences in grasping behavior}, volume = {8}, journal = {Communications Biology}, doi = {10.1038/s42003-025-08998-6}, year = {2025}, abstract = {Elephants use their trunks, muscular hydrostats, to perform a plethora of tasks. Trunk tip morphology as well as grasping behavior differ between elephant species. While African savanna elephants (Loxodonta africana) use their dorsal and ventral finger for pinching movements, Asian elephants (Elephas maximus) prefer to wrap around objects with their one dorsal finger and ventral bulb trunk tip lip. Moreover, E. maximus can flip their ventral bulb backwards to clamp objects behind the trunk tip. Whether trunk tip musculature differs between elephant species and muscle architecture is reflected by preferred grasping behavior is, however, not clear. In this study, we performed dense muscle fascicle reconstruction of three L. africana and three E. maximus hemi-trunk tips using a combination of manual and automated segmentation of high-resolution microfocus tomography (microCT) scans. We distinguish three types of muscle fascicles: longitudinal (bending and shortening), radial (elongating) and transversal muscle fascicles (elongating). We found that trunk tips of L. africana consist to one third of longitudinal and two thirds radial/transversal muscle fascicles, likely aiding in their grasping behavior, while E. maximus trunk tips consist to two thirds of longitudinal and one third radial/transversal muscle fascicles, which is advantageous for their wrapping and backward clamping behavior.}, language = {en} } @article{ZhangHartmannvonKleist2018, author = {Zhang, Wei and Hartmann, Carsten and von Kleist, Max}, title = {Optimal control of Markov jump processes: Asymptotic analysis, algorithms and applications to the modeling of chemical reaction systems}, journal = {Communications in Mathematical Sciences}, doi = {10.4310/CMS.2018.v16.n2.a1}, pages = {293 -- 331}, year = {2018}, abstract = {Markov jump processes are widely used to model natural and engineered processes. In the context of biological or chemical applications one typically refers to the chemical master equation (CME), which models the evolution of the probability mass of any copy-number combination of the interacting particles. When many interacting particles ("species") are considered, the complexity of the CME quickly increases, making direct numerical simulations impossible. This is even more problematic when one aims at controlling the Markov jump processes defined by the CME. In this work, we study both open loop and feedback optimal control problems of the Markov jump processes in the case that the controls can only be switched at fixed control stages. Based on Kurtz's limit theorems, we prove the convergence of the respective control value functions of the underlying Markov decision problem as the copy numbers of the species go to infinity. In the case of the optimal control problem on a finite time-horizon, we propose a hybrid control policy algorithm to overcome the difficulties due to the curse of dimensionality when the copy number of the involved species is large. Two numerical examples demonstrate the suitability of both the analysis and the proposed algorithms.}, language = {en} } @article{SharmaZhang2021, author = {Sharma, Upanshu and Zhang, Wei}, title = {Non-reversible sampling schemes on submanifolds}, volume = {59}, journal = {SIAM Journal on Numerical Analysis}, number = {6}, arxiv = {http://arxiv.org/abs/2011.02835}, doi = {10.1137/20M1378752}, pages = {2989 -- 3031}, year = {2021}, abstract = {Calculating averages with respect to probability measures on submanifolds is often necessary in various application areas such as molecular dynamics, computational statistical mechanics and Bayesian statistics. In recent years, various numerical schemes have been proposed in the literature to study this problem based on appropriate reversible constrained stochastic dynamics. In this paper we present and analyse a non-reversible generalisation of the projection-based scheme developed by one of the authors [ESAIM: M2AN, 54 (2020), pp. 391-430]. This scheme consists of two steps - starting from a state on the submanifold, we first update the state using a non-reversible stochastic differential equation which takes the state away from the submanifold, and in the second step we project the state back onto the manifold using the long-time limit of a ordinary differential equation. We prove the consistency of this numerical scheme and provide quantitative error estimates for estimators based on finite-time running averages. Furthermore, we present theoretical analysis which shows that this scheme outperforms its reversible counterpart in terms of asymptotic variance. We demonstrate our findings on an illustrative test example.}, language = {en} } @article{Zhang2019, author = {Zhang, Wei}, title = {Ergodic SDEs on submanifolds and related numerical sampling schemes}, journal = {ESAIM: Mathematical Modelling and Numerical Analysis}, arxiv = {http://arxiv.org/abs/1702.08064}, year = {2019}, abstract = {In many applications, it is often necessary to sample the mean value of certain quantity with respect to a probability measure \$\mu\$ on the level set of a smooth function ξ:R^d→R^k, 1≤k