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Results for “Classifcation”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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5 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Identification of Urine Metabolites as Biomarkers of Early Lyme Disease

Metabolites detectible in human biofuids are attractive biomarkers for the diagnosis of early Lyme disease (ELD), a vector-borne infectious disease. Urine represents an easily obtained clinical sample that can be applied for diagnostic purposes. However, few studies have explored urine for biomarkers of ELD. In this study, metabolomics approaches were applied to evaluate small molecule metabolites in urine from patients with ELD (n=14), infectious mononucleosis (n=14) and healthy controls (n=14). Metabolic biosignatures for ELD versus healthy controls and ELD versus infectious mononucleosis were generated using untargeted metabolomics. Pathway analyses and metabolite identifcation revealed the dysregulation of several metabolic processes in ELD as compared to healthy controls or mononucleosis, including metabolism of tryptophan. Linear discriminant analyses demonstrated that individual metabolic biosignatures can correctly discriminate ELD from the other patient groups with accuracies of 71 to 100%. These data provide proof-of-concept for use of urine metabolites as biomarkers for diagnostic classifcation of ELD.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

A coevolution-guided model for the rotor of the bacterial flagellar motor

The Salmonella typhimurium trans-membrane FliF MS ring templates assembly of the rotary bacterial fagellar motor, which also contains a cytoplasmic C-ring. A full-frame fusion of FliF with the rotor protein FliG assembles rings in non-motile expression hosts. 3D electron microscopy reconstructions of these FliFFliG rings show three high electron-density sub-volumes. 3D-classifcation revealed heterogeneity of the assigned cytoplasmic volume consistent with FliG lability. We used residue coevolution to construct homodimer building blocks for ring assembly, with X-ray crystal structures from other species and injectisome analogs. The coevolution signal validates folds and, importantly, indicates strong homodimer contacts for three ring building motifs (RBMs), initially identified in injectisome structures. It also indicates that the cofolded domains of the FliG N-terminal domain (FliG_N) with embedded α-helical FliF carboxy-terminal tail homo-oligomerize. The FliG middle and C-terminal domains (FliG_MC) have a weak signal for homo-dimerization but have coevolved to conserve their stacking contact. The homodimers and their ring models ft well into the 3D reconstruction. We hypothesize that a stable FliF periplasmic hub provides a platform for FliG ring self-assembly, but the FliG_MC ring has only limited stability without the C-ring. We also present a mechanical model for torque transmission in the FliFFliG ring.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

MalGen: Malware Generation with Specific Behaviors to Improve Machine Learning-based Detectors

In recent years, infections and damage caused by malware have increased at exponential rates. At the same time, machine learning (ML) techniques have shown tremendous promise in many domains, often out performing human efforts by learning from large amounts of data. Results in the open literature suggest that ML is able to provide similar results for malware detection, achieving greater than 99% classifcation accuracy [49]. However, the same detection rates when applied in deployed settings have not been achieved. Malware is distinct from many other domains in which ML has shown success in that (1) it purposefully tries to hide, leading to noisy labels and (2) often its behavior is similar to benign software only differing in intent, among other complicating factors. This report details the reasons for the diffcultly of detecting novel malware by ML methods and offers solutions to improve the detection of novel malware.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Deep active learning for classifying cancer pathology reports

Abstract Background Automated text classification has many important applications in the clinical setting; however, obtaining labelled data for training machine learning and deep learning models is often difficult and expensive. Active learning techniques may mitigate this challenge by reducing the amount of labelled data required to effectively train a model. In this study, we analyze the effectiveness of 11 active learning algorithms on classifying subsite and histology from cancer pathology reports using a Convolutional Neural Network as the text classification model. Results We compare the performance of each active learning strategy using two differently sized datasets and two different classification tasks. Our results show that on all tasks and dataset sizes, all active learning strategies except diversity-sampling strategies outperformed random sampling, i.e., no active learning. On our large dataset (15K initial labelled samples, adding 15K additional labelled samples each iteration of active learning), there was no clear winner between the different active learning strategies. On our small dataset (1K initial labelled samples, adding 1K additional labelled samples each iteration of active learning), marginal and ratio uncertainty sampling performed better than all other active learning techniques. We found that compared to random sampling, active learning strongly helps performance on rare classes by focusing on underrepresented classes. Conclusions Active learning can save annotation cost by helping human annotators efficiently and intelligently select which samples to label. Our results show that a dataset constructed using effective active learning techniques requires less than half the amount of labelled data to achieve the same performance as a dataset constructed using random sampling.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A robust approach to 3D neuron shape representation for quantification and classification

We consider the problem of finding an accurate representation of neuron shapes, extracting sub-cellular features, and classifying neurons based on neuron shapes. In neuroscience research, the skeleton representation is often used as a compact and abstract representation of neuron shapes. However, existing methods are limited to getting and analyzing “curve” skeletons which can only be applied for tubular shapes. This paper presents a 3D neuron morphology analysis method for more general and complex neuron shapes. First, we introduce the concept of skeleton mesh to represent general neuron shapes and propose a novel method for computing mesh representations from 3D surface point clouds. A skeleton graph is then obtained from skeleton mesh and is used to extract sub-cellular features. Finally, an unsupervised learning method is used to embed the skeleton graph for neuron classification. Extensive experiment results are provided and demonstrate the robustness of our method to analyze neuron morphology.

3D neuron morphology↗