25 papers · ranked by Valyu relevance
Swati Jain, Jonathan D. Jou, Ivelin S. Georgiev, Bruce R. Donald + 1 more
'Patrick Aloy'] Protein design algorithms enumerate a combinatorial number of candidate structures to compute the Global Minimum Energy Conformation (GMEC). To efficiently find the GMEC, protein design algorithms must methodically reduce the conformational search space. By applying distance and energy cutoffs, the…
Sahir R Bhatnagar, Tianyuan Lu, Amanda Lovato, David L Olds + 5 more
A conceptual paradigm for onset of a new disease is often considered to be the result of changes in entire biological networks whose states are affected by a complex interaction of genetic and environmental factors. However, when modelling a relevant phenotype as a function of high dimensional measurements, power to…
Julen Mendieta-Esteban, Marco Di Stefano, David Castillo, Irene Farabella + 1 more
Chromosome Conformation Capture (3C) technologies measure the interaction frequency between pairs of chromatin regions within the nucleus in a cell or a population of cells. Some of these 3C technologies retrieve interactions involving non-contiguous sets of loci, resulting in sparse interaction matrices. One of such…
Daniel M. Busiello, Samir Suweis, Jorge Hidalgo, Amos Maritan
The increasing volume of ecologically and biologically relevant data has revealed a wide collection of emergent patterns in living systems. Analysing different data sets, ranging from metabolic gene-regulatory to species interaction networks, we find that these networks are sparse, i.e. the percentage of the active…
Stav Marcus, Ari M. Turner, Guy Bunin
Interactions in natural communities can be highly heterogeneous, with any given species interacting appreciably with only some of the others, a situation commonly represented by sparse interaction networks. We study the consequences of sparse competitive interactions, in a theoretical model of a community assembled…
Cheng Yong Tang, Ethan X. Fang, Yuexiao Dong
In statistical learning framework with regressions, interactions are the contributions to the response variable from the products of the explanatory variables. In high-dimensional problems, detecting interactions is challenging due to combinatorial complexity and limited data information. We consider detecting…
Daniel Maria Busiello, Samir Suweis, Jorge Hidalgo, Amos Maritan
The increasing volume of ecologically and biologically relevant data has revealed a wide collection of emergent patterns in living systems. Analyzing different datasets, ranging from metabolic gene-regulatory to species interaction networks, we find that these networks are sparse, i.e. the percentage of the active…
Duc Anh Nguyen, Canh Hao Nguyen, Peter Petschner, Hiroshi Mamitsuka
4.2.3#### Case studies: interpretation of top 10 unknown predictions SPARSE is an SBM with latent features for drugs, side effects, and interactions. In particular, the model has connections between latent drug features and latent interactions. Thus from the trained model, we can extract the drug features, which are…
Niklas Brunn, Maren Hackenberg, Tanja Vogel, Harald Binder
Several approaches have been proposed to reconstruct interactions between groups of cells or individual cells from single-cell transcriptomics data, leveraging prior information about known ligand-receptor interactions. To enhance downstream analyses, we present an end-to-end dimensionality reduction workflow…
Zhi-Qin John Xu, Douglas Zhou, David Cai
Maximum entropy principle (MEP) analysis with few non-zero effective interactions successfully characterizes the distribution of dynamical states of pulse-coupled networks in many fields, e.g., in neuroscience. To better understand the underlying mechanism, we found a relation between the dynamical structure, i.e.…
Christopher P. Weiss-Lehman, Chhaya M. Werner, Catherine H. Bowler, Lauren M. Hallett + 8 more
Modeling species interactions in diverse communities traditionally requires a prohibitively large number of species-interaction coefficients, especially when considering environmental dependence of parameters. We implemented Bayesian variable selection via sparsity-inducing priors on non-linear species abundance models…
Kuo-ching Liang, Ashwini Patil, Kenta Nakai, Niranjan Baisakh
The use of pathways and gene interaction networks for the analysis of differential expression experiments has allowed us to highlight the differences in gene expression profiles between samples in a systems biology perspective. The usefulness and accuracy of pathway analysis critically depend on our understanding of…
Sriganesh Srihari, Wai Yie Leong
Over the last few years, several computational techniques have been devised to recover protein complexes from the protein interaction (PPI) networks of organisms. These techniques model "dense" subnetworks within PPI networks as complexes. However, our comprehensive evaluations revealed that these techniques fail to…
Safura Rashid Shomali, Majid Nili Ahmadabadi, Seyyed Nader Rasuli, Hideaki Shimazaki
An appealing challenge in Neuroscience is to identify network architecture from neural activity. A key requirement is the knowledge of statistical input-output relation of single neurons in vivo. Using a recent exact solution of spike-timing for leaky integrate-and-fire neurons under noisy inputs balanced near…
Shibal Ibrahim, Rahul Mazumder, Peter Radchenko, Emanuel Ben‐David
In this paper we consider the problem of predicting survey response rates using a family of flexible and interpretable nonparametric models. The study is motivated by the US Census Bureau's well-known ROAM application which uses a linear regression model trained on the US Census Planning Database data to identify…
Sanjar Adilov
Machine learning models for molecular-property prediction typically work with molecular representations in the form of fingerprints, descriptors, or graphs. In case of fingerprints and descriptors, molecular representations usually comprise thousands of features, which causes the curse of dimensionality for many…
James T. Morton, Justin Silverman, Gleb Tikhonov, Harri Lähdesmäki + 1 more
Estimating microbe-microbe interactions is critical for understanding the ecological laws governing microbial communities. Rapidly decreasing sequencing costs have promised new opportunities to estimate microbe-microbe interactions across thousands of uncultured, unknown microbes. However, typical microbiome datasets…
Victor Picheny, Rémi Servien, Nathalie Villa‐Vialaneix
We propose a semiparametric framework based on Sliced Inverse Regression (SIR) to address the issue of variable selection in functional regression. SIR is an effective method for dimension reduction which computes a linear projection of the predictors in a lowdimensional space, without loss of information on the…
Sjoerd Hermes, Joost van Heerwaarden, Pariya Behrouzi
Within the statistical literature, a significant gap exists in methods capable of modeling asymmetric multivariate spatial effects that elucidate the relationships underlying complex spatial phenomena. For such a phenomenon, observations at any location are expected to arise from a combination of within- and…
Hamed Firouzi, Alfred O. Hero, Bala Rajaratnam
This paper proposes a general adaptive procedure for budget-limited predictor design in high dimensions called two-stage Sampling, Prediction and Adaptive Regression via Correlation Screening (SPARCS). SPARCS can be applied to high dimensional prediction problems in experimental science, medicine, finance, and…
José Camacho, Age K. Smilde, Edoardo Saccenti, Johan A. Westerhuis
Sparse Principal Component Analysis (sPCA) is a popular matrix factorization approach based on Principal Component Analysis (PCA) that combines variance maximization and sparsity with the ultimate goal of improving data interpretation. When moving from PCA to sPCA, there are a number of implications that the…
David Buterez, Jon Paul Janet, Steven Kiddle, Pietro Liò
We investigate the potential of graph neural networks for transfer learning and improving molecular property prediction on sparse and expensive to acquire high-fidelity data by leveraging low-fidelity measurements as an inexpensive proxy for a targeted property ofinterest. This problem arises in discovery processes…
Authors not listed
Understanding the types and locations of interactions between atoms or molecules within a chemical system is a fundamental concern in chemistry. In the field of theoretical and computational chemistry, wavefunction analysis offers various methods based on functions defined in three-dimensional real space, enabling the…
Authors not listed
Despite advances in crystal engineering, predicting the packing of metal-containing molecules remains a significant challenge due to their increased structural complexity and computational cost relative to purely organic compounds. Yet, predicting solid-state structures is critical for understanding structure-property…
Kelsey Hatzell, Yanjie Zheng
X-ray Computed Tomography (CT) is a non-invasive, non-destructive approach to imaging materials, material systems and engineered components in two- and three- dimensions. Acquisition of 3D images requires the collection of hundreds or thousands of through-thickness X-ray radiographic images from different angles. Such…