10 papers · ranked by Valyu relevance
Lena-Marie Woelk, Sukanya A. Kannabiran, Valerie Brock, Christine E. Gee + 4 more
Live cell Ca^2+^ fluorescence microscopy is a cornerstone of cellular signaling analysis and imaging. The demand for high spatial and temporal imaging resolution is, however, intrinsically linked to a low signal-to-noise ratio (SNR) of the acquired spatio-temporal image data, which impedes subsequent image analysis.…
Haobo Wang, Shihao Feng, Sirui Liu, Sergey Ovchinnikov
The rise in the number of protein sequences in the post-genomic era has led to a major breakthrough in fitting generative sequence models for contact prediction, protein design, alignment, and homology search. Despite this success, the interpretability of the modeled pairwise parameters continues to be limited due to…
I. Muukkonen, V.R. Salmela
To reduce the redundancy in the input, the human visual system employs efficient coding. Therefore, images with varying entropy (amount of information) should elicit distinct brain responses. Here, we show that a simple entropy model outperforms all current models, including many deep neural networks, in predicting…
Taufik A Valiante
Neuronal size has often been used to explain the “superiority” of the human brain. By deriving the Shannon entropy of different statistical distributions, we show that the entropy of different distributions is solely accounted for by the variance of the distribution. We use total dendritic length of neurons from…
Amitava Roy, Vishwesh Venkatraman, Tibra Ali
Inspired by black hole thermodynamics, the area law that entropy is proportional to horizon area has been proposed in quantum entanglement entropy and has largely maintained its validity. This article shows that the area law is also valid for the thermodynamic entropy of molecules. We showed that the gas-phase entropy…
Guanghui Zhang, Xinran Wang, Steven J. Luck
Regularization has been extensively used in multivariate pattern classification (MVPA; decoding) of EEG data to mitigate the risk of overfitting. N-fold cross-validation is also used to mitigate this risk, and it is often combined with averaging across trials to improve the signal-to-noise ratio. However, the impact of…
Benjamin Kuznets-Speck, Jaekwon Jung, Pornchanan Pholraksa, Adrianne Zhong + 4 more
Classification and regression are cornerstones of computational biology and science at large, from identifying cell types to stratifying patients by disease state. Current deep learning classifiers provide accurate predictions but offer neither uncertainty estimates nor insight into which features matter most. On the…
Xiaolu Wang, Peter Dayan, Paul M Bays
The activity of neural populations typically encodes more information about sensory or motor variables than can be captured by point estimates of the variables. We present and compare two approaches to quantifying this additional or ancillary information and its relationship to uncertainty: the mutual information…
Griffin S. Hampton, Ryan Neff, Zezheng Song, Mustapha Bouhrara + 2 more
Myelin water fraction (MWF) mapping in the central nervous system is a topic of intense research activity. One framework for this requires parameter estimation from a decaying biexponential signal. However, this is often an ill-posed nonlinear problem resulting in unreliable parameter estimates. For linear…
Giovanna Maria Dimitri
In this work, done in collaboration with Prof. Michelangelo Diligenti (department of Engineering and Mathematics, University of Siena) we present the use of Semantic Based Regularization Kernel based machine learning method to predict protein function. We initially build the protein functions ontology, given an initial…