An Optimal Algorithm for Online Prize-Collecting Node-Weighted Steiner Forest

2018 ◽  
pp. 214-223
Author(s):  
Christine Markarian
Keyword(s):  
Optimal Algorithm ◽  
Steiner Forest ◽  
Prize Collecting

A Primal-Dual Algorithm for the Generalized Prize-Collecting Steiner Forest Problem

2017 ◽  
Vol 5 (2) ◽  
pp. 219-231
Author(s):  
Lu Han ◽  
Da-Chuan Xu ◽  
Dong-Lei Du ◽  
Chen-Chen Wu
Keyword(s):  
Dual Algorithm ◽  
Steiner Forest ◽  
Primal Dual ◽  
Prize Collecting

scholarly journals ST-Steiner: a spatio-temporal gene discovery algorithm

2019 ◽  
Vol 35 (18) ◽  
pp. 3433-3440 ◽  
Author(s):  
Utku Norman ◽  
A Ercument Cicek
Keyword(s):  
Gene Discovery ◽  
Gene Interaction ◽  
Autism Spectrum ◽  
Supplementary Information ◽  
Temporal Dimension ◽  
Cascading Effect ◽  
Gene Interaction Networks ◽  
Steiner Forest ◽  
Prize Collecting ◽  
Spatio Temporal

AbstractMotivationWhole exome sequencing (WES) studies for autism spectrum disorder (ASD) could identify only around six dozen risk genes to date because the genetic architecture of the disorder is highly complex. To speed the gene discovery process up, a few network-based ASD gene discovery algorithms were proposed. Although these methods use static gene interaction networks, functional clustering of genes is bound to evolve during neurodevelopment and disruptions are likely to have a cascading effect on the future associations. Thus, approaches that disregard the dynamic nature of neurodevelopment are limited.ResultsHere, we present a spatio-temporal gene discovery algorithm, which leverages information from evolving gene co-expression networks of neurodevelopment. The algorithm solves a prize-collecting Steiner forest-based problem on co-expression networks, adapted to model neurodevelopment and transfer information from precursor neurodevelopmental windows. The decisions made by the algorithm can be traced back, adding interpretability to the results. We apply the algorithm on ASD WES data of 3871 samples and identify risk clusters using BrainSpan co-expression networks of early- and mid-fetal periods. On an independent dataset, we show that incorporation of the temporal dimension increases the predictive power: predicted clusters are hit more and show higher enrichment in ASD-related functions compared with the state-of-the-art.Availability and implementationThe code is available at http://ciceklab.cs.bilkent.edu.tr/st-steiner.Supplementary informationSupplementary data are available at Bioinformatics online.

scholarly journals Simultaneous Reconstruction of Multiple Signaling Pathways via the Prize-Collecting Steiner Forest Problem

2012 ◽  
pp. 287-301 ◽  
Author(s):  
Nurcan Tuncbag ◽  
Alfredo Braunstein ◽  
Andrea Pagnani ◽  
Shao-Shan Carol Huang ◽  
Jennifer Chayes ◽  
...  
Keyword(s):  
Signaling Pathways ◽  
Simultaneous Reconstruction ◽  
Steiner Forest ◽  
Prize Collecting ◽  
Multiple Signaling

scholarly journals Simultaneous Reconstruction of Multiple Signaling Pathways via the Prize-Collecting Steiner Forest Problem

2013 ◽  
Vol 20 (2) ◽  
pp. 124-136 ◽  
Author(s):  
Nurcan Tuncbag ◽  
Alfredo Braunstein ◽  
Andrea Pagnani ◽  
Shao-Shan Carol Huang ◽  
Jennifer Chayes ◽  
...  
Keyword(s):  
Signaling Pathways ◽  
Simultaneous Reconstruction ◽  
Steiner Forest ◽  
Prize Collecting ◽  
Multiple Signaling

scholarly journals Euclidean Prize-Collecting Steiner Forest

Algorithmica ◽  
2011 ◽  
Vol 62 (3-4) ◽  
pp. 906-929 ◽  
Author(s):  
MohammadHossein Bateni ◽  
MohammadTaghi Hajiaghayi
Keyword(s):  
Steiner Forest ◽  
Prize Collecting

scholarly journals Euclidean Prize-Collecting Steiner Forest

2010 ◽  
pp. 503-514
Author(s):  
MohammadHossein Bateni ◽  
MohammadTaghi Hajiaghayi
Keyword(s):  
Steiner Forest ◽  
Prize Collecting

scholarly journals A Fast Prize-Collecting Steiner Forest Algorithm for Functional Analyses in Biological Networks

2017 ◽  
pp. 263-276 ◽  
Author(s):  
Murodzhon Akhmedov ◽  
Alexander LeNail ◽  
Francesco Bertoni ◽  
Ivo Kwee ◽  
Ernest Fraenkel ◽  
...  
Keyword(s):  
Biological Networks ◽  
Steiner Forest ◽  
Prize Collecting ◽  
Functional Analyses

DEVELOPMENT OF A DEVICE FOR CONTROL OF THE STATE OF THE ORGANISM BASED ON PHOTOPLETISMOGRAPHY USING TECHNOLOGIES OF DIGITAL ADAPTIVE FILTRATION

2020 ◽  
pp. 100-107
Author(s):  
Алексей Дмитриевич Акишин ◽  
Иван Павлович Семчук ◽  
Александр Петрович Николаев
Keyword(s):  
Heart Rate ◽  
Cardiovascular System ◽  
Adaptive Filtering ◽  
Least Squares Method ◽  
Optimal Algorithm ◽  
Heart Diseases ◽  
Cardiac Activity ◽  
Motion Artifacts ◽  
The Body ◽  
Advantages And Disadvantages

Постоянно растущий интерес к разработке новых неинвазивных и безманжетных методов измерения параметров сердечной деятельности, использование которых давало бы возможность непрерывного и удаленного контроля сердечно-сосудистой системы, обуславливает актуальность данной работы. В многочисленных публикациях продолжаются обсуждения преимуществ и недостатков различных методов ранней диагностики сердечно-сосудистых заболеваний. Однако артефакты движения являются сильной помехой, мешающей точной оценке показателей функционирования сердечно-сосудистой системы. Одним из перспективных методов контроля является метод оценки физиологических параметров с использованием фотоплетизмографии. Данная статья посвящена разработке устройства для фотоплетизмографических исследований и алгоритмических методов обработки регистрируемых сигналов для обеспечения мониторинга сердечного ритма с заданной точностью. В работе используются технологии цифровой адаптивной фильтрации полученных сигналов для мониторинга сердечного ритма в условиях внешних механических и электрических помеховых воздействий, ухудшающих точностные характеристики системы, а также разработана архитектура системы и изготовлен макет устройства, который позволил провести измерения для определения оптимального алгоритма цифровой обработки сигналов. При использовании устройства применялись методы адаптивной фильтрации на основе фильтров Винера, фильтров на основе метода наименьших квадратов и Калмановской фильтрации. Разработанное устройство для фотоплетизмографических исследований обеспечило возможность мониторинга сердечного ритма с заданной точностью, контроля текущего состояния организма и может быть использовано в качестве средства диагностики заболеваний сердца The constantly growing interest in the development of new non-invasive and cuff-free methods for measuring the parameters of cardiac activity, the use of which would give the possibility of continuous and remote monitoring of the cardiovascular system, determines the relevance of this work. Numerous publications continue to discuss the advantages and disadvantages of various methods of early diagnosis of cardiovascular disease. However, motion artifacts are a strong hindrance to the accurate assessment of the performance of the cardiovascular system. One of the promising control methods is the method for assessing physiological parameters using photoplethysmography. This article is devoted to the development of a device for photoplethysmographic studies and algorithmic methods for processing recorded signals to ensure monitoring of the heart rate with a given accuracy. The work uses technologies of digital adaptive filtering of the received signals to monitor the heart rate in conditions of external mechanical and electrical interference, which worsen the accuracy characteristics of the system, as well as the architecture of the system and a prototype of the device, which made it possible to carry out measurements to determine the optimal algorithm for digital signal processing. When using the device, the methods of adaptive filtering based on Wiener filters, filters based on the least squares method and Kalman filtering were used. The developed device for photoplethysmographic studies provided the ability to monitor the heart rate with a given accuracy, control the current state of the body and can be used as a means of diagnosing heart diseases

1/(1-1/k)-Optimal Algorithm for Regular Expression Grouping

2012 ◽  
Vol 23 (9) ◽  
pp. 2261-2272 ◽  
Author(s):  
Ting-Wen LIU ◽  
Yong SUN ◽  
Dong-Bo BU ◽  
Li GUO ◽  
Bin-Xing FANG
Keyword(s):  
Regular Expression ◽  
Optimal Algorithm
Sign in / Sign up

Export Citation Format

Share Document