14 papers · ranked by Valyu relevance
Rong Ma, Xi Li, Jingyuan Hu, Bin Yu
Single-cell sequencing is revolutionizing biology by enabling detailed investigations of cell-state transitions. Many biological processes unfold along continuous trajectories, yet it remains challenging to extract smooth, low-dimensional representations from inherently noisy, highdimensional single-cell data. Neighbor…
Alex Nisthal, Connie Y. Wang, Marie L. Ary, Stephen L. Mayo
The accurate prediction of protein stability upon sequence mutation is an important but unsolved challenge in protein engineering. Large mutational datasets are required to train computational predictors, but traditional methods for collecting stability data are either low-throughput or measure protein stability…
Subrata Saha, Ahmed Soliman, Sanguthevar Rajasekaran
Nowadays we are observing an explosion of gene expression data with phenotypes. It enables researchers to efficiently identify genes responsible for certain medical condition as well as classify them for drug target. Like any other phenotype data in medical domain, gene expression data with phenotypes also suffers from…
Behnam Yousefi, Benno Schwikowski
Clustering plays an important role in a multitude of bioinformatics applications, including protein function prediction, population genetics, and gene expression analysis. The results of most clustering algorithms are sensitive to variations of the input data, the clustering algorithm and its parameters, and individual…
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…
Tamara Vanderwal, Jeffrey Eilbott, Clare Kelly, Todd S. Woodward + 2 more
Patterns of functional connectivity are unique at the individual level, enabling test-retest matching algorithms to identify a subject from among a group using only their functional connectome. Recent findings show that accuracies of these algorithms in children increase with age. Relatedly, the persistence of…
Marcelo Bussotti Reyes, Ramon Huerta, Pedro Valadão Carelli, Reynaldo D. Pinto + 2 more
The stability of rhythmic activity in neural networks is an important aspect in the study of central pattern generators (CPGs). Different from other physiological rhythms, the activity of CPGs has not been fully characterized in terms of its stability, especially using quantitative methods. We propose a method that…
Marcelo Bussotti Reyes, Ramon Huerta, Pedro Valadão Carelli, Reynaldo D. Pinto + 2 more
The stability of rhythmic activity in neural networks is an important aspect in the study of central pattern generators (CPGs). Different from other physiological rhythms, the activity of CPGs has not been fully characterized in terms of its stability, especially using quantitative methods. We propose a method that…
Bikash Sahoo, Adam C. Snyder
The unfolding of neural population activity can be approximated as a dynamical system. Stability in the latent dynamics that characterize neural population activity has been linked with consistency in animal behavior, such as motor control or value-based decision-making. However, whether similar dynamics characterize…
Dan Siegal-Gaskins, Elisa Franco, Tiffany Zhou, Richard M. Murray
Small biomolecular circuits with two distinct and stable steady states have been identified as essential components in a wide range of biological networks, with a variety of mechanisms and topologies giving rise to their important bistable property. Understanding the differences between circuit implementations is an…
Divyansh Mittal, Rishikesh Narayanan
Grid cells in the medial entorhinal cortex manifest multiple firing fields, patterned to tessellate external space with triangles. Although two-dimensional continuous attractor network (CAN) models have offered remarkable insights about grid-patterned activity generation, their functional stability in the presence of…
Hector Zenil, Narsis A. Kiani, Francesco Marabita, Yue Deng + 4 more
It remains fundamentally unclear how to reprogram complex evolving systems. Here, we introduce a conceptual framework and an interventional calculus to steer and manipulate systems based on their intrinsic algorithmic probability using the universal principles of the theory of computability and algorithmic information.…
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…
Prasad U. Bandodkar, Razeen R. Shaikh, Gregory T. Reeves
Model development is essential to gain a mathematical understanding of the underlying phenomena in systems biology. In most models, it is typically hard to estimate the values of the biophysical/phenomenological parameters that characterize the model. The parameters are estimated by minimizing a function that reduces a…