27 papers · ranked by Valyu relevance
Ozgur Demir-Kavuk, Mayumi Kamada, Tatsuya Akutsu, Ernst-Walter Knapp
Background Machine learning methods are nowadays used for many biological prediction problems involving drugs, ligands or polypeptide segments of a protein. In order to build a prediction model a so called training data set of molecules with measured target properties is needed. For many such problems the size of the…
Yunfei Huang, Christoph Schell, Tobias B. Huber, Ahmet Nihat Şimşek + 4 more
'Nils Hersch' 'Rudolf Merkel' 'Gerhard Gompper' 'Benedikt Sabass'] Adherent cells exert traction forces on to their environment which allows them to migrate, to maintain tissue integrity, and to form complex multicellular structures during developmental morphogenesis. Traction force microscopy (TFM) enables the…
Holger Mohr, Hannes Ruge
In certain modeling approaches, activation analyses of task-based fMRI data can involve a relatively large number of predictors. For example, in the encoding model approach, complex stimuli are represented in a high-dimensional feature space, resulting in design matrices with many predictors. Similarly, single-trial…
Holger Mohr, Hannes Ruge
In certain modeling approaches, activation analyses of task-based fMRI data can involve a relatively large number of predictors. For example, in the encoding model approach, complex stimuli are represented in a high-dimensional feature space, resulting in design matrices with many predictors. Similarly, single-trial…
Abinash Nayak
Inverse problems arise in a wide spectrum of applications in fields ranging from engineering to scientific computation. Connected with the rise of interest in inverse problems is the development and analysis of regularization methods, such as Tikhonov-type regularization methods or iterative regularization methods…
Alejandro Suñé-Auñón, Alvaro Jorge-Peñas, Rocío Aguilar-Cuenca, Miguel Vicente-Manzanares + 2 more
'Miguel Vicente-Manzanares' 'Hans Van Oosterwyck' 'Arrate Muñoz-Barrutia'] Background Traction Force Microscopy (TFM) is a widespread technique to estimate the tractions that cells exert on the surrounding substrate. To recover the tractions, it is necessary to solve an inverse problem, which is ill-posed and needs…
Ling Xiao, P. D. Brooks
We develop a sparse and outlier-insensitive method for one-dimensional line fitting that can be used as the basis for outlier-insensitive machine learning methods such as principal component analysis. The method is insensitive to outlier observations by formulating procedures as optimization problems seeking the…
Qianru Liu, Rui Wang, Yuesheng Xu, Mingsong Yan
We consider a regularization problem whose objective function consists of a convex fidelity term and a regularization term determined by the `1 norm composed with a linear transform. Empirical results show that the regularization with the `1 norm can promote sparsity of a regularized solution. It is the goal of this…
Zhenqiu Liu, Gang Li
Variable (feature, gene, model, which we use interchangeably) selections for regression with high-dimensional BIGDATA have found many applications in bioinformatics, computational biology, image processing, and engineering. One appealing approach is the L0 regularized regression which penalizes the number of nonzero…
Devis Tuia, Rémi Flamary, Michel Barlaud
— In this paper, we study the effect of different regularizers and their implications in high dimensional image classification and sparse linear unmixing. Although kernelization or sparse methods are globally accepted solutions for processing data in high dimensions, we present here a study on the impact of the form of…
Yong Liang, Cheng Liu, Xin-Ze Luan, Kwong-Sak Leung + 3 more
'Zong-Ben Xu' 'Hai Zhang'] Background Microarray technology is widely used in cancer diagnosis. Successfully identifying gene biomarkers will significantly help to classify different cancer types and improve the prediction accuracy. The regularization approach is one of the effective methods for gene selection in…
Hai-Hui Huang, Xiao-Ying Liu, Yong Liang, Fengfeng Zhou
Cancer classification and feature (gene) selection plays an important role in knowledge discovery in genomic data. Although logistic regression is one of the most popular classification methods, it does not induce feature selection. In this paper, we presented a new hybrid L1/2 +2 regularization (HLR) function, a…
Anastasia Sholokhova, Dmitriy Matyushin, Mikhail Shashkov
Ionic liquids, i.e., organic salts with a low melting point, can be used as gas chromatographic liquid stationary phases. These stationary phases have some advantages such as peculiar selectivity, high polarity, and thermostability. Many previous works are devoted to such stationary phases. However, there are still no…
Bryan Lunt, Saurabh Sinha
Understanding of the gene regulatory activity of enhancers is a major problem in regulatory biology. The nascent field of sequence-to-expression modelling seeks to create quantitative models of gene expression based on regulatory DNA (cis) and cellular environmental (trans) contexts. All quantitative models are defined…
Mianyi Chen, Deling Mi, Peng He, Luzhen Deng + 1 more
Computed tomography (CT) reconstruction with low radiation dose is a significant research point in current medical CT field. Compressed sensing has shown great potential reconstruct high-quality CT images from few-view or sparse-view data. In this paper, we use the sparser L1/2 regularization operator to replace the…
Baptiste Py, Francesco Ciucci
The distribution of relaxation times (DRT) has emerged as a promising method for analyzing electrochemical impedance spectroscopy (EIS) data. The standard approach for reconstructing the DRT from measured impedances consists of regularized regression, which usually leverages the Euclidean norm. In this work, we show…
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…
Authors not listed
Electrochemical impedance spectroscopy (EIS) coupled with distribution of relaxation times (DRT) analysis is a robust framework for characterizing electrochemical systems. However, DRT deconvolution is often plagued by spurious peaks, hindering accurate process identification and quantitative parameter estimation. To…
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…
Marijn van Vliet, Riitta Salmelin
Linear machine learning models “learn” a data transformation by being exposed to examples of input with the desired output, forming the basis for a variety of powerful techniques for analyzing neuroimaging data. However, their ability to learn the desired transformation is limited by the quality and size of the example…
Anwar O. Nunez-Elizalde, Alexander G. Huth, Jack L. Gallant
Predictive models for neural or fMRI data are often fit using regression methods that employ priors on the model parameters. One widely used method is ridge regression, which employs a spherical Gaussian prior that assumes equal and independent variance for all parameters. However, a spherical prior is not always…
R. Jyothi, P. Babu
—In this paper, we consider the problem of ℓ p norm linear regression, which has several applications such as i n sparse recovery, data clustering, and semi-supervised learning. The problem, even though convex, does not enjoy a closed-for m solution. The state-of-the-art algorithms are iterative but suffer from…
Denis Tikhonov
Here, we present a new approach for obtaining radial distribution functions (RDF) from the electron diffraction data using a regularized weighted sine least-squares spectral analysis (rwsLSSA). It allows for explicitly transferring the measured experimental uncertainties in the reduced molecular scattering function to…
Marta Karas, Damian Brzyski, Mario Dzemidzic, Joaquin Goni + 3 more
A challenging problem arising in brain imaging research is principled incorporation of information from different imaging modalities. Frequently each modality is analyzed separately using, for instance, dimensionality reduction techniques which result in a loss of mutual information. We propose a novel regularization…
Charles Eads
This report describes and illustrates a set of automatable multicomponent exponential relaxation analysis protocols that are model-agnostic and suited to extracting information under circumstances when little prior knowledge about the underlying system is used. Methods are illustrated and mathematical and physical…
Lea Duncker, Kiersten M. Ruda, Greg D. Field, Jonathan W. Pillow
An important problem in systems neuroscience is to characterize how a neuron integrates sensory inputs across space and time. The linear receptive field provides a mathematical characterization of this weighting function, and is commonly used to quantify neural response properties and classify cell types. However…
Adeleke Maradesa, Baptiste Py, Ting Hei Wan, Mohammed B. Effat + 1 more
Electrochemical impedance spectroscopy (EIS) is a characterization technique used widely in electrochemistry. Obtaining EIS data is simple when modern electrochemical workstations are used; however, analyzing EIS spectra is still a considerable quandary. The distribution of relaxation times (DRT) has emerged as a…