Seminar

We will discuss about "Learning stable and predictive structures in kinetic systems", Niklas Pfister , Stefan Bauer, and Jonas Peters. PNAS, 2019 Abstract: Learning kinetic systems from data is one of the core challenges in many fields. Identifying stable models is essential for the generalization capabilities of data-driven inference. We introduce a computationally efficient framework, …

We will discuss about "Neural Ordinary Differential Equations", Chen, Ricky TQ, et al., Advances in neural information processing systems 31 (2018). Abstract: We introduce a new family of deep neural network models. Instead of specifying a discrete sequence of hidden layers, we parameterize the derivative of the hidden state using a neural network. The output …

We will discuss about "Molecular convolutional neural networks with DNA regulatory circuits", Pei, Hao, et al., Nature Machine Intelligence (2022): 1-11. Abstract: Complex biomolecular circuits enabled cells with intelligent behaviour to survive before neural brains evolved. Since DNA computing was first demonstrated in the mid-1990s, synthetic DNA circuits in liquid phase have been developed as …

We will discuss about "Inferring Regulatory Networks from Expression Data Using Tree-Based Methods," Huynh-Thu et al., PLoS ONE (2010). Abstract: One of the pressing open problems of computational systems biology is the elucidation of the topology of genetic regulatory networks (GRNs) using high throughput genomic data, in particular microarray gene expression data. The Dialogue for …

We will discuss about "Hidden Markov models for monitoring circadian rhythmicity in telemetric activity data", Huang, Qi, Journal of The Royal Society Interface 15.139 (2018): 20170885. Abstract: Wearable computing devices allow collection of densely sampled real-time information on movement enabling researchers and medical experts to obtain objective and non-obtrusive records of actual activity of a …

We will discuss about "Physics-informed neural networks for PDE-constrained optimization and control", Barry-Straume, Jostein, et al., arXiv preprint arXiv:2205.03377 (2022). Abstract: A fundamental problem of science is designing optimal control policies that manipulate a given environment into producing a desired outcome. Control PhysicsInformed Neural Networks simultaneously solve a given system state, and its respective optimal …

We will discuss about "Cell clustering for spatial transcriptomics data with graph neural networks", Li, J., Chen, S., Pan, X. et al., Nat Comput Sci 2, 399–408 (2022) Abstract: Spatial transcriptomics data can provide high-throughput gene expression profiling and the spatial structure of tissues simultaneously. Most studies have relied on only the gene expression information but …