Current Linear Methods Research Articles

Single-residue linear and conformational B cell epitopes prediction using random and ESM-2 based projections.

B cell epitope prediction methods are separated into linear sequence-based predictors and conformational epitope predictions that typically use the measured or predicted protein structure. Most linear predictions rely on the translation of the sequence to biologically based representations and the applications of machine learning on these representations. We here present CALIBER 'Conformational And LInear B cell Epitopes pRediction', and show that a bidirectional long short-term memory with random projection produces a more accurate prediction (test set AUC=0.789) than all current linear methods. The same predictor when combined with an Evolutionary Scale Modeling-2 projection also improves on the state of the art in conformational epitopes (AUC = 0.776). The inclusion of the graph of the 3D distances between residues did not increase the prediction accuracy. However, the long-range sequence information was essential for high accuracy. While the same model structure was applicable for linear and conformational epitopes, separate training was required for each. Combining the two slightly increased the linear accuracy (AUC 0.775 versus 0.768) and reduced the conformational accuracy (AUC = 0.769).

GPU enabled XDraw viewshed analysis

Viewshed analysis is an important tool in the study of digital terrain visibility. Current methods rely on the CPU performing computations to linearly calculate visibility for a given position on a portion of digital terrain. The viewshed analysis process can be sped up through the use of a GPU to parallelize the visibility algorithms. This paper presents a novel conversion of the XDraw viewshed analysis algorithm to a parallel context in an effort to increase the speed at which a viewshed can be rendered. The algorithm executed faster than current linear methods and responded well to parallelization. We conclude that XDraw is applicable for GIS applications when rendered in a parallel context.

Age estimation of Norway spruce using incomplete increment cores: Testing new and improved methods

Information on tree age is often vital for dendrochronological studies, especially when the Regional Curve Standardisation technique is used. Several linear and non-linear methods of tree age estimation using partial increment cores (without the presence of pith) were evaluated and modified to provide more accurate estimations than are currently used. To achieve the objective, core samples from 142 Norway spruce trees (Picea abies [L.] Karst.) were collected from an altitudinal gradient in the Western Carpathians. The samples in which the pith was included were then used for further analyses. Several known age estimation approaches were adjusted to combine the advantages of direct increment- and indirect age-diameter-based methods. Inverse differential forms of non-linear growth functions were tested and proposed as a new advanced approach for age estimation. The results show that most of the modified linear methods achieved a mean square error of less than 10% when the length of the partial core exceeded 90% of the stem radius and less than 20% when the length of the core was at least 60% of the stem radius. Using an appropriate differential form of the non-linear growth functions, a mean square error of less than 20% was reached, even when the core length was shorter than 60% of the radius. The results show that current linear methods for age estimation can only be used if the missing part of the core sample is very short, with only a few rings absent. In the case of a large number of missing rings, a differential form of non-linear functions should preferably be used instead.

Comparison of Dimensional Reduction Methods for Detecting and Visualizing Novel Patterns in Human and Marine Microbiome

Using metagenomics to detect the global structure of microbial community remains a significant challenge. The structure of a microbial community and its functions are complicated because of not only the complex interactions among microbes but also their interactions with confounding environmental factors. Recently dimension reduction methods have been employed extensively to investigate the complex structure embedded in metagenomic profiles which summarize the abundance of functional or taxonomic categorizations in metagenomic studies. However, metagenomic profiles are not necessary to meet the "Assumption of Linearity" behind these methods. Therefore it is worth to investigate whether nonlinear methods are appropriate methods which can be utilized in metagenomic analysis. In this paper, we compare the applications of several methods, including two linear methods (Principle component analysis and nonnegative matrix factorization) and a nonlinear manifold learning method--Isomap on visualizing and analyzing metagenomic profiles. These methods are applied and compared on a taxonomic profile from 33 human gut metagenomes and a large-scale Pfam profile which are derived from 45 metagenomes in Global Ocean Sampling expedition. We find that all three methods can discover interesting structures of the taxonomic profile from human gut. Furthermore, Isomap identified a novel nonlinear structure of protein families. The relationships among the identified nonlinear components and environmental factors of global ocean are explored. The results indicate that nonlinear methods could be a complementary technique to current linear methods in analyzing metagenomic dataset.

Computerized segmentation and measurement of malignant pleural mesothelioma.

The current linear method to track tumor progression and evaluate treatment efficacy is insufficient for malignant pleural mesothelioma (MPM). A volumetric method for tumor measurement could improve the evaluation of novel treatments, but a fully manual implementation of volume measurement is too tedious and time-consuming. This manuscript presents a computerized method for the three-dimensional segmentation and volumetric analysis of MPM. The computerized MPM segmentation method segments the lung parenchyma and hemithoracic cavities to define the pleural space. Nonlinear diffusion and a k-means classifier are then implemented to identify MPM in the pleural space. A database of 31 computed tomography scans from 31 patients with pathologically confirmed MPM was retrospectively collected. Three observers independently outlined five randomly selected sections in each scan. The Jaccard similarity coefficient (J) between each of the observers and between the observer-defined and computer-defined segmentations was calculated. The computer-defined and the observer-defined segmentation areas (averaged over all observers) were both calculated for each axial section and compared using Bland-Altman plots. The median J value among observers averaged over all sections was 0.517. The median J between the computer-defined and manual segmentations was 0.484. The difference between these values was not statistically significant. The area delineated by the computerized method demonstrated variability and bias comparable to the tumor area calculated from manual delineations. A computerized method for segmentation and measurement of MPM was developed. This method requires minimal initialization by the user and demonstrated good agreement with manually drawn outlines and area measurements. This method will allow volumetric tracking of tumor progression and may improve the evaluation of novel MPM treatments.

Bayesian Filtering of Myoelectric Signals

Surface electromyography is used in research, to estimate the activity of muscle, in prosthetic design, to provide a control signal, and in biofeedback, to provide subjects with a visual or auditory indication of muscle contraction. Unfortunately, successful applications are limited by the variability in the signal and the consequent poor quality of estimates. I propose to use a nonlinear recursive filter based on Bayesian estimation. The desired filtered signal is modeled as a combined diffusion and jump process and the measured electromyographic (EMG) signal is modeled as a random process with a density in the exponential family and rate given by the desired signal. The rate is estimated on-line by calculating the full conditional density given all past measurements from a single electrode. The Bayesian estimate gives the filtered signal that best describes the observed EMG signal. This estimate yields results with very low short-time variability but also with the capability of very rapid response to change. The estimate approximates isometric joint torque with lower error and higher signal-to-noise ratio than current linear methods. Use of the nonlinear filter significantly reduces noise compared with current algorithms, and it may therefore permit more effective use of the EMG signal for prosthetic control, biofeedback, and neurophysiology research.