scholarly journals Robust Group Fused Lasso for Multisample CNV Detection under Uncertainty

2015 ◽  
Author(s):  
Hossein Sharifi Noghabi ◽  
Majid Mohammadi
Keyword(s):  
Biological Data ◽  
Comparative Genomic ◽  
Fused Lasso ◽  
Acgh Data ◽  
Dna Copy Number Variation ◽  
Wide Range ◽  
M Estimator ◽  
Number Variation ◽  
Non Gaussian ◽  
Fitting Error

One of the most important needs in the post-genome era is providing the researchers with reliable and efficient computational tools to extract and analyze this huge amount of biological data, in which DNA copy number variation (CNV) is a vitally important one. Array-based comparative genomic hybridization (aCGH) is a common approach in order to detect CNVs. Most of methods for this purpose were proposed for one-dimensional profile. However, slightly this focus has moved from one- to multi-dimensional signals. In addition, since contamination of these profiles with noise is always an issue, it is highly important to have a robust method for analyzing multisample aCGH data. In this paper, we propose Robust Grouped Fused Lasso (RGFL) which utilizes the Robust Group Total Variations (RGTV). Instead of l2;1 norm, the l1-l2 M-estimator is used which is more robust in dealing with non-Gaussian noise and high corruption. More importantly, Correntropy (Welsch M-estimator) is also applied for fitting error. Extensive experiments indicate that the proposed method outperforms the state-of-the art algorithms and techniques under a wide range of scenarios with diverse noises.

Fault Tolerant Reliable Protocol (FTRP) Performance Evaluation in Wireless Sensor Networks: An Extensitive Study

2019 ◽  
pp. 1-36
Keyword(s):  
Wireless Sensor Networks ◽  
Fault Tolerance ◽  
Sensor Networks ◽  
Fault Tolerant ◽  
Cluster Head ◽  
Biological Data ◽  
Wireless Sensor ◽  
Delivery Ratio ◽  
Good Performer ◽  
Wide Range

Fault Tolerant Reliable Protocol (FTRP) is proposed as a novel routing protocol designed for Wireless Sensor Networks (WSNs). FTRP offers fault tolerance reliability for packet exchange and support for dynamic network changes. The key concept used is the use of node logical clustering. The protocol delegates the routing ownership to the cluster heads where fault tolerance functionality is implemented. FTRP utilizes cluster head nodes along with cluster head groups to store packets in transient. In addition, FTRP utilizes broadcast, which reduces the message overhead as compared to classical flooding mechanisms. FTRP manipulates Time to Live values for the various routing messages to control message broadcast. FTRP utilizes jitter in messages transmission to reduce the effect of synchronized node states, which in turn reduces collisions. FTRP performance has been extensively through simulations against Ad-hoc On-demand Distance Vector (AODV) and Optimized Link State (OLSR) routing protocols. Packet Delivery Ratio (PDR), Aggregate Throughput and End-to-End delay (E-2-E) had been used as performance metrics. In terms of PDR and aggregate throughput, it is found that FTRP is an excellent performer in all mobility scenarios whether the network is sparse or dense. In stationary scenarios, FTRP performed well in sparse network; however, in dense network FTRP’s performance had degraded yet in an acceptable range. This degradation is attributed to synchronized nodes states. Reliably delivering a message comes to a cost, as in terms of E-2-E. results show that FTRP is considered a good performer in all mobility scenarios where the network is sparse. In sparse stationary scenario, FTRP is considered good performer, however in dense stationary scenarios FTRP’s E-2-E is not acceptable. There are times when receiving a network message is more important than other costs such as energy or delay. That makes FTRP suitable for wide range of WSNs applications, such as military applications by monitoring soldiers’ biological data and supplies while in battlefield and battle damage assessment. FTRP can also be used in health applications in addition to wide range of geo-fencing, environmental monitoring, resource monitoring, production lines monitoring, agriculture and animals tracking. FTRP should be avoided in dense stationary deployments such as, but not limited to, scenarios where high application response is critical and life endangering such as biohazards detection or within intensive care units.

scholarly journals Modelling with star-shaped distributions

2020 ◽  
Vol 8 (1) ◽  
pp. 45-69
Author(s):  
Eckhard Liebscher ◽  
Wolf-Dieter Richter
Keyword(s):  
Probabilistic Models ◽  
Arbitrary Dimension ◽  
Probability Density Functions ◽  
Density Functions ◽  
Wide Range ◽  
Gaussian Density ◽  
Spherical Distributions ◽  
Data Points ◽  
Non Gaussian ◽  
General Method

AbstractWe prove and describe in great detail a general method for constructing a wide range of multivariate probability density functions. We introduce probabilistic models for a large variety of clouds of multivariate data points. In the present paper, the focus is on star-shaped distributions of an arbitrary dimension, where in case of spherical distributions dependence is modeled by a non-Gaussian density generating function.

scholarly journals Robust Multiuser Detection in Synchronous DS-CDMA System with MRC Receive Diversity over Nakagami-m Fading Channel

2012 ◽  
Vol 4 ◽  
pp. 43-50 ◽  
Author(s):  
Rao V. Srinivasa ◽  
Kumar P. Vinay ◽  
S. Balaji ◽  
Khan Habibulla ◽  
Kumar T. Anil
Keyword(s):  
Multiuser Detection ◽  
Fading Channel ◽  
Decorrelating Detector ◽  
Probability Of Error ◽  
Receive Diversity ◽  
M Estimator ◽  
Average Probability ◽  
M Channels ◽  
Non Gaussian ◽  
Code Division Multiple

This paper presents the robust multiuser detection in synchronous direct sequence-code division multiple access (DS-CDMA) systems with Maximal Ratio Combiner (MRC) receive diversity over frequency-nonselective, slowly fading Nakagami-m channels in a non-Gaussian environment. Average probability of error is derived for decorrelating detector over single path Nakagami-m fading channel. A new M-estimator proposed to robustify the detector is studied and analyzed. Simulation results show that the new M-estimator outperforms linear decorrelating detector, the Huber, and the Hampel estimator based detectors.

Pharmacologic activities of 5, 6-Diaryl/heteroaryl-3-substituted-1, 2, 4-triazines as a privileged scaffold in drug development

2021 ◽  
Vol 21 ◽  
Author(s):  
Zahra Zakeri Khatir ◽  
Hamid Irannejad
Keyword(s):  
Biological Data ◽  
Pharmacological Activities ◽  
Drug Candidates ◽  
Structure Activity ◽  
Wide Range ◽  
Privileged Structure ◽  
Triazine Derivatives ◽  
Privileged Scaffold ◽  
Substituted 1 ◽  
Anti Hiv

: 1, 2, 4-Triazine derivatives have received much attention due to their multifunctional nature, especially in diverse pharmacological properties as well as a key fragment in many drug candidates. Introduction of a vicinal 5, 6-diaryl/heteroaryl moiety on the 1, 2, 4-triazine ring has attracted plentiful attention in the field of medicinal chemistry. 5, 6-Diaryl/heteroaryl-3-substituted-1, 2, 4-triazine is as a prominent scaffold in many drug candidates which has shown a wide range of pharmacological activities such as anti-diabetic, antifungal, anti-inflammatory, anticancer, anti-HIV, neuroprotective, anticonvulsant, anti- Alzheimer, anti-Parkinson and antioxidant. In this review, we have discussed synthesis, various pharmacological activities of 5, 6-diaryl/heteroaryl-3-substituted-1, 2, 4-triazines, their structure-activity relationship (SAR), pharmacophoric elements and their mechanism of action reported in the published articles during 2000-2019. Evaluation of compounds by PAINS filtering tool was accomplished and showed that this versatile structure could be considered as a privileged structure. Compilation of the biological data confirmed that the position 3 of the 1,2,4-triazine is a key location to determine the affinity and selectivity of the 5,6-diaryl/heteroaryl-3-substituted-1, 2, 4-triazines towards different biologic targets. Specific geometrical and thermodynamic characters of this motif have prompted it as a frequent hitter.

​Re-sequencing of Whole Genome Jordanian Awassi Ewes using Hiseq Sequencing Technology: Major Step Towards Sheep Genomic Selection

2021 ◽  
Author(s):  
Hussein Migdadi ◽  
Nizar Haddad ◽  
Ruba AlOmari ◽  
Mohammad Brake ◽  
Mustafa AlShdaifat ◽  
...  
Keyword(s):  
Production Systems ◽  
Ovis Aries ◽  
Domestic Sheep ◽  
Major Step ◽  
Illumina Hiseq ◽  
Structure Variation ◽  
Awassi Sheep ◽  
Wide Range ◽  
Number Variation ◽  
Paired End Sequencing

Background: Jordanian Awassi sheep (Ovis aries) is the dominant fat tail sheep breed that appeals to customers because of its various production systems, including fiber, meat and milk. This report is the first whole ewe genome sequence (WGS) of O. aries submitted in the NCBI database from Jordan. Methods: 64 Paired-end sequencing libraries were constructed and subjected to Illumina Hiseq 2500 sequencing system. High-quality reads were aligned against the reference sheep genome and detecting comprehensive sources (SNPs, InDels, SV, CNVs) of genetic variations. We have deposited data sequences at the NCBI under SRA (sequence reads archives) under the accession numbers SRR11128863, PRJNA574879. Result: Genome resequencing of Jordanian Awassi ewe was carried out with approximately 93.88 Gb with a mapping rate and effective mapping depths were 99.28% and 36.32. Around 19 million SNPs, 3,6 million InDels, 35,180 Structure variation and 13,524 copy number variation among the Jordanian ewe genome were detected. This wide range of genetic variation provides a framework for further genetic studies that will help understand the molecular basis underlying phenotypic variation of economically important traits in sheep and improve intrinsic defects in domestic sheep breeds.

DNA copy number variation screening in familial hypercholesterolemia-related genes

2018 ◽  
Vol 275 ◽  
pp. e79
Author(s):  
M. Iacocca ◽  
J. Wang ◽  
J. Dron ◽  
H. Cao ◽  
J. Robinson ◽  
...  
Keyword(s):  
Copy Number Variation ◽  
Familial Hypercholesterolemia ◽  
Copy Number ◽  
Dna Copy Number ◽  
Dna Copy Number Variation ◽  
Number Variation

scholarly journals Data framework for efficient management of sequence and microsatellite data in biodiversity studies

2013 ◽  
Vol 56 (1) ◽  
pp. 50-64 ◽  
Author(s):  
C. V. C. Truong ◽  
Z. Duchev ◽  
E. Groeneveld
Keyword(s):  
Information System ◽  
Molecular Data ◽  
General Information ◽  
Biological Data ◽  
Efficient Management ◽  
Data Framework ◽  
Wide Range ◽  
Uniform Solution ◽  
General Data ◽  
General Data Model

Abstract. In recent years, software packages for the management of biological data have rapidly been developing. However, currently, there is no general information system available for managing molecular data derived from both Sanger sequencing and microsatellite genotyping projects. A prerequisite to implementing such a system is to design a general data model which can be deployed to a wide range of labs without modification or customization. Thus, this paper aims to (1) suggest a uniform solution to efficiently store data items required in different labs, (2) describe procedures for representing data streams and data items (3) and construct a formalized data framework. As a result, the data framework has been used to develop an integrated information system for small labs conducting biodiversity studies.

scholarly journals Denoising large-scale biological data using network filters

2021 ◽  
Author(s):  
Andrew J Kavran ◽  
Aaron Clauset
Keyword(s):  
Large Scale ◽  
Synthetic Data ◽  
Interaction Network ◽  
Learning Task ◽  
Biological Data ◽  
Data Sets ◽  
Proteomics Data ◽  
Life History Variation ◽  
Wide Range ◽  
Underlying Processes

Abstract Background: Large-scale biological data sets are often contaminated by noise, which can impede accurate inferences about underlying processes. Such measurement noise can arise from endogenous biological factors like cell cycle and life history variation, and from exogenous technical factors like sample preparation and instrument variation.Results: We describe a general method for automatically reducing noise in large-scale biological data sets. This method uses an interaction network to identify groups of correlated or anti-correlated measurements that can be combined or “filtered” to better recover an underlying biological signal. Similar to the process of denoising an image, a single network filter may be applied to an entire system, or the system may be first decomposed into distinct modules and a different filter applied to each. Applied to synthetic data with known network structure and signal, network filters accurately reduce noise across a wide range of noise levels and structures. Applied to a machine learning task of predicting changes in human protein expression in healthy and cancerous tissues, network filtering prior to training increases accuracy up to 43% compared to using unfiltered data.Conclusions: Network filters are a general way to denoise biological data and can account for both correlation and anti-correlation between different measurements. Furthermore, we find that partitioning a network prior to filtering can significantly reduce errors in networks with heterogenous data and correlation patterns, and this approach outperforms existing diffusion based methods. Our results on proteomics data indicate the broad potential utility of network filters to applications in systems biology.

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