Search · four archives
Search · four archives
14 papers · ranked by Valyu relevance
Stefano Giampiccolo, Federico Reali, Anna Fochesato, Giovanni Iacca + 1 more
Parameter estimation is one of the central problems in computational modeling of biological systems. Typically, scientists must fully specify the mathematical structure of the model, often expressed as a system of ordinary differential equations, to estimate the parameters. This process poses significant challenges due…
Anja Adamov, Christian L. Müller, Nicholas A. Bokulich
Microbiome sequencing datasets are sparse, high-dimensional, compositional, and hierarchically structured. Predictive modelling from these data typically relies on ad hoc choices of feature representation, obscuring their impact on performance and biological interpretation. A standardized, compute-efficient framework…
Masaru Sasaki, Ken Takeda, Kota Abe, Masafumi Oizumi
Gromov-Wasserstein optimal transport (GWOT) has emerged as a versatile method for unsupervised alignment in various research areas, including neuroscience, drawing upon the strengths of optimal transport theory. However, the use of GWOT in various applications has been hindered by the difficulty of finding good optima…
Md Ochiuddin Miah, Umme Habiba, Md Faisal Kabir
Brain-computer interface (BCI) research has gained increasing attention in educational contexts, offering the potential to monitor and enhance students’ cognitive states. Real-time classification of students’ confusion levels using electroencephalogram (EEG) data presents a significant challenge in this domain. Since…
Zhiqian Zhai, Changhu Wang, Chengfeng Jiang, Ziqi Rong + 1 more
Integrating single-cell and spatial transcriptomics data across batches is essential for recovering comparable cell identities—including cell types, subtypes, and states—as a prerequisite for downstream analyses in multi-condition and large-scale studies. This task remains challenging because between-batch variation…
Henry Munroe, Bright Osatohanmwen, Reza Sharifi
Machine learning (ML) models with stochastic and non-deterministic characteristics are increasingly used for genomic prediction in plant breeding, but evaluation often neglects important aspects like prediction stability and ranking performance. This study addresses this gap by evaluating how two hyperparameters of a…
Lucy Xia, Christy Lee, Jingyi Jessica Li
Two-dimensional (2D) embedding methods are crucial for single-cell data visualization. Popular methods such as t-SNE and UMAP are commonly used for visualizing cell clusters; however, it is well known that t-SNE and UMAP’s 2D embedding might not reliably inform the similarities among cell clusters. Motivated by this…
Sumedh S Nagrale, Alik S Widge
The use of Deep Brain Stimulation (DBS) on the ventral capsule/ventral striatum (VCVS) has therapeutic potential for patients with refractory psychiatric disorders, but clinical success is impeded by the need for a time-consuming and trial-and-error process when setting the parameters, this process relying on…
Zoe Kutulakos, Patrick Slade
Assistive robotic devices like exoskeletons offer the promise of improving mobility for millions of people. However, developing devices that improve an objective mobility metric is challenging. Human-in-the-loop optimization is a systematic approach for personalizing robotic assistance to maximize a mobility metric…
Máté Mohácsi, Márk Patrik Török, Sára Sáray, Luca Tar + 1 more
Finding optimal parameters for detailed neuronal models is a ubiquitous challenge in neuroscientific research. Recently, manual model tuning has been replaced by automated parameter search using a variety of different tools and methods. However, using most of these software tools and choosing the most appropriate…
Hua-Dong Xiong, Li Ji-An, Marcelo G. Mattar, Robert C. Wilson
Cognitive modeling provides a formal method to articulate and test hypotheses about cognitive processes. However, accurately and reliably estimating model parameters remains challenging due to common issues in behavioral science, such as limited data, measurement noise, experimental constraints, and model complexity.…
Stephan Grein, David R. Penas, Daniel Weindl, Polina Lakrisenko + 2 more
Dynamic models are central to the computational life sciences but typically contain unknown parameters that must be inferred from experimental data. High-throughput measurements have made this task increasingly challenging, yielding high-dimensional search spaces and non-convex objectives with many local optima. This…
Sina Dabiri, Eric R. Cole, Robert E. Gross
Brain stimulation has become an important treatment option for a variety of neurological and psychiatric diseases. A key challenge in improving brain stimulation is selecting the optimal set of stimulation parameters for each patient, as parameter spaces are too large for brute-force search and their induced effects…
Changin Oh, Kathleen P. Wilkie
We present the Toroidal Search Algorithm (TSA), a novel population-based metaheuristic optimization method inspired by the topology of a torus. Conventional metaheuristics frequently suffer from boundary stagnation, a phenomenon that severely degrades performance in bounded and high-dimensional search spaces. TSA…