scholarly journals A Computational Framework for Pattern Detection on Unaligned Sequences: An Application on SARS-CoV-2 Data

2021 ◽  
Vol 12 ◽  
Author(s):  
Nikolaos Pechlivanis ◽  
Anastasios Togkousidis ◽  
Maria Tsagiopoulou ◽  
Stefanos Sgardelis ◽  
Ilias Kappas ◽  
...  
Keyword(s):  
Sequence Data ◽  
Time Frame ◽  
Difficult Problem ◽  
Biological Data ◽  
Pattern Detection ◽  
Multiple Sequence ◽  
Computational Framework ◽  
Input Dataset ◽  
Reasonable Time Frame ◽  
Characteristic Features

The exponential growth of genome sequences available has spurred research on pattern detection with the aim of extracting evolutionary signal. Traditional approaches, such as multiple sequence alignment, rely on positional homology in order to reconstruct the phylogenetic history of taxa. Yet, mining information from the plethora of biological data and delineating species on a genetic basis, still proves to be an extremely difficult problem to consider. Multiple algorithms and techniques have been developed in order to approach the problem multidimensionally. Here, we propose a computational framework for identifying potentially meaningful features based on k-mers retrieved from unaligned sequence data. Specifically, we have developed a process which makes use of unsupervised learning techniques in order to identify characteristic k-mers of the input dataset across a range of different k-values and within a reasonable time frame. We use these k-mers as features for clustering the input sequences and identifying differences between the distributions of k-mers across the dataset. The developed algorithm is part of an innovative and much promising approach both to the problem of grouping sequence data based on their inherent characteristic features, as well as for the study of changes in the distributions of k-mers, as the k-value is fluctuating within a range of values. Our framework is fully developed in Python language as an open source software licensed under the MIT License, and is freely available at https://github.com/BiodataAnalysisGroup/kmerAnalyzer.

scholarly journals Clinical correlates of anti-SARS-CoV-2 antibody profiles in Spanish COVID-19 patients from a high incidence region

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Robert Markewitz ◽  
Antje Torge ◽  
Klaus-Peter Wandinger ◽  
Daniela Pauli ◽  
Andre Franke ◽  
...  
Keyword(s):  
Case Fatality ◽  
Time Frame ◽  
Epidemic Outbreak ◽  
Sample Collection ◽  
Rt Pcr ◽  
High Incidence ◽  
Duration Of Hospitalization ◽  
Reasonable Time Frame ◽  
Support Treatment ◽  
Diagnostic And Prognostic Value

AbstractLaboratory testing for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) consists of two pillars: the detection of viral RNA via rt-PCR as the diagnostic gold standard in acute cases, and the detection of antibodies against SARS-CoV-2. However, concerning the latter, questions remain about their diagnostic and prognostic value and it is not clear whether all patients develop detectable antibodies. We examined sera from 347 Spanish COVID-19 patients, collected during the peak of the epidemic outbreak in Spain, for the presence of IgA and IgG antibodies against SARS-CoV-2 and evaluated possible associations with age, sex and disease severity (as measured by duration of hospitalization, kind of respiratory support, treatment in ICU and death). The presence and to some degree the levels of anti-SARS-CoV-2 antibodies depended mainly on the amount of time between onset of symptoms and the collection of serum. A subgroup of patients did not develop antibodies at the time of sample collection. Compared to the patients that did, no differences were found. The presence and level of antibodies was not associated with age, sex, duration of hospitalization, treatment in the ICU or death. The case-fatality rate increased exponentially with older age. Neither the presence, nor the levels of anti-SARS-CoV-2 antibodies served as prognostic markers in our cohort. This is discussed as a possible consequence of the timing of the sample collection. Age is the most important risk factor for an adverse outcome in our cohort. Some patients appear not to develop antibodies within a reasonable time frame. It is unclear, however, why that is, as these patients differ in no respect examined by us from those who developed antibodies.

scholarly journals Reasonable time frame in bankruptcy proceedings: Analysis of cases from the judicial practice of the Commercial Court in Niš

2021 ◽  
Vol 60 (90) ◽  
pp. 97-118
Author(s):  
Aleksandar Mojašević ◽  
Aleksandar Jovanović
Keyword(s):  
Case Law ◽  
Time Frame ◽  
Fair Trial ◽  
Fundamental Rights ◽  
Reasonable Time ◽  
Judicial Practice ◽  
Reasonable Time Frame ◽  
The Subject ◽  
The Republic ◽  
The Right

The Act on the Protection of the Right to a Trial within a Reasonable Time, which took effect in 2016, has created the conditions in our legal system for the protection of the right to a trial within a reasonable time, as one of the fundamental rights guaranteed by the Constitution of the Republic of Serbia and related international documents. Although the legislator does not explicitly provide for the application of this Act in the context of bankruptcy proceedings, it has been used in judicial practice as a mean for the bankruptcy creditors to obtain just satisfaction in cases involving lengthy bankruptcy proceedings and a violation of the right to a fair trial within a reasonable time. The subject matter of analysis in this paper is the right to a trial within a reasonable time in bankruptcy cases. For that purpose, the authors examine the case law of the Commercial Court in Niš in the period from the beginning of 2016 to the end of 2019, particularly focusing on the bankruptcy cases in which complaints (objections) were filed for the protection of the right to a fair trial within a reasonable time. The aim of the research is to examine whether the objection, as an initial act, is a suitable instrument for increasing the efficiency of the bankruptcy proceeding, or whether it only serves to satisfy the interests of creditors. The authors have also examined whether this remedy affects the overall costs and duration of the bankruptcy proceeding. The main finding is that there is an increasing number of objections in the Commercial Court in Niš, which still does not affect the length and costs of bankruptcy. This trend is not only the result of inactivity of the court and the complexity of certain cases but also of numerous external factors, the most prominent of which is the work of some state bodies.

scholarly journals Machine learning can differentiate venom toxins from other proteins having non-toxic physiological functions

2016 ◽  
Vol 2 ◽  
pp. e90 ◽  
Author(s):  
Ranko Gacesa ◽  
David J. Barlow ◽  
Paul F. Long
Keyword(s):  
Machine Learning ◽  
Sequence Data ◽  
Biological Data ◽  
Biological Databases ◽  
Web Based ◽  
Physiological Functions ◽  
Link Type ◽  
Venom Toxins ◽  
Venomous Animals ◽  
Toxin Protein

Ascribing function to sequence in the absence of biological data is an ongoing challenge in bioinformatics. Differentiating the toxins of venomous animals from homologues having other physiological functions is particularly problematic as there are no universally accepted methods by which to attribute toxin function using sequence data alone. Bioinformatics tools that do exist are difficult to implement for researchers with little bioinformatics training. Here we announce a machine learning tool called ‘ToxClassifier’ that enables simple and consistent discrimination of toxins from non-toxin sequences with >99% accuracy and compare it to commonly used toxin annotation methods. ‘ToxClassifer’ also reports the best-hit annotation allowing placement of a toxin into the most appropriate toxin protein family, or relates it to a non-toxic protein having the closest homology, giving enhanced curation of existing biological databases and new venomics projects. ‘ToxClassifier’ is available for free, either to download (https://github.com/rgacesa/ToxClassifier) or to use on a web-based server (http://bioserv7.bioinfo.pbf.hr/ToxClassifier/).

scholarly journals Three-dimensional Printing of Customized Bioresorbable Airway Stents

2020 ◽  
Author(s):  
Nevena Paunović ◽  
Yinyin Bao ◽  
Fergal Brian Coulter ◽  
Kunal Masania ◽  
Anna Karoline Geks ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Standard Of Care ◽  
Time Frame ◽  
Psychological Burden ◽  
Three Dimensional Printing ◽  
Life Threatening ◽  
Reasonable Time Frame ◽  
Printed Materials ◽  
Airway Stents

AbstractCentral airway obstruction is a life-threatening disorder causing a high physical and psychological burden to patients due to severe breathlessness and impaired quality of life. Standard-of-care airway stents are silicone tubes, which cause immediate relief, but are prone to migration, especially in growing patients, and require additional surgeries to be removed, which may cause further tissue damage. Customized airway stents with tailorable bioresorbability that can be produced in a reasonable time frame would be highly needed in the management of this disorder. Here, we report poly(D,L-lactide-co-ε-caprolactone) methacrylate blends-based biomedical inks and their use for the rapid fabrication of customized and bioresorbable airway stents. The 3D printed materials are cytocompatible and exhibit silicone-like mechanical properties with suitable biodegradability. In vivo studies in healthy rabbits confirmed biocompatibility and showed that the stents stayed in place for 7 weeks after which they became radiographically invisible. The developed biomedical inks open promising perspectives for the rapid manufacturing of the customized medical devices for which high precision, tuneable elasticity and predictable degradation are sought-after.

Prediction of Compound-Protein Interactions with Machine Learning Methods

2011 ◽  
pp. 304-317
Author(s):  
Yoshihiro Yamanishi ◽  
Hisashi Kashima
Keyword(s):  
Machine Learning ◽  
Protein Interactions ◽  
Chemical Structure ◽  
Genomic Sequence ◽  
Sequence Data ◽  
Binary Classification ◽  
Biological Data ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods

In silico prediction of compound-protein interactions from heterogeneous biological data is critical in the process of drug development. In this chapter the authors review several supervised machine learning methods to predict unknown compound-protein interactions from chemical structure and genomic sequence information simultaneously. The authors review several kernel-based algorithms from two different viewpoints: binary classification and dimension reduction. In the results, they demonstrate the usefulness of the methods on the prediction of drug-target interactions and ligand-protein interactions from chemical structure data and genomic sequence data.

VisFeature: a stand-alone program for visualizing and analyzing statistical features of biological sequences

2019 ◽  
Author(s):  
Jun Wang ◽  
Pu-Feng Du ◽  
Xin-Yu Xue ◽  
Guang-Ping Li ◽  
Yuan-Ke Zhou ◽  
...  
Keyword(s):  
Sequence Data ◽  
Software Tool ◽  
Data Retrieval ◽  
Supplementary Information ◽  
Statistical Features ◽  
Biological Sequence ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Source Codes ◽  
Multiple Sequence Alignments

Abstract Summary Many efforts have been made in developing bioinformatics algorithms to predict functional attributes of genes and proteins from their primary sequences. One challenge in this process is to intuitively analyze and to understand the statistical features that have been selected by heuristic or iterative methods. In this paper, we developed VisFeature, which aims to be a helpful software tool that allows the users to intuitively visualize and analyze statistical features of all types of biological sequence, including DNA, RNA and proteins. VisFeature also integrates sequence data retrieval, multiple sequence alignments and statistical feature generation functions. Availability and implementation VisFeature is a desktop application that is implemented using JavaScript/Electron and R. The source codes of VisFeature are freely accessible from the GitHub repository (https://github.com/wangjun1996/VisFeature). The binary release, which includes an example dataset, can be freely downloaded from the same GitHub repository (https://github.com/wangjun1996/VisFeature/releases). Contact [email protected] or [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

CFD Challenge: Application of the Commercial Finite Element Solver FIDAP in a Clinical Setting

2012 ◽  
Author(s):  
Merih Cibis ◽  
Jolanda J. Wentzel ◽  
Frank J. H. Gijsen
Keyword(s):  
Finite Element ◽  
Shear Stress ◽  
Carotid Arteries ◽  
Time Frame ◽  
Academic Hospital ◽  
Close Collaboration ◽  
Desktop Computer ◽  
Reasonable Time ◽  
Finite Element Software ◽  
Reasonable Time Frame

The Rotterdam group mainly focuses on the influence of shear stress on plaque localization and progression in human coronary and carotid arteries. Since we are in an academic hospital, we always have been working in close collaboration with cardiologists and radiologists. Since clinicians do not have the time or the sources that academic engineering groups have, we limited ourselves to perform the simulations on a standard desktop computer (Intel Xeon six core processor, 2.40 GHz CPU and 12 GB RAM) using commercial finite element software (FIDAP 8.7.4 with GAMBIT 2.4.6) within a reasonable time-frame (the weekend). The simulations were carried out by our PhD student Merih Cibis.

Prediction of mutation effects using a deep temporal convolutional network

2019 ◽  
Vol 36 (7) ◽  
pp. 2047-2052 ◽  
Author(s):  
Ha Young Kim ◽  
Dongsup Kim
Keyword(s):  
Latent Variable ◽  
Sequence Data ◽  
Generative Model ◽  
Supplementary Information ◽  
Biological Research ◽  
Sequence Alignments ◽  
Variable Model ◽  
Convolutional Network ◽  
Direct Optimization ◽  
Multiple Sequence

Abstract Motivation Accurate prediction of the effects of genetic variation is a major goal in biological research. Towards this goal, numerous machine learning models have been developed to learn information from evolutionary sequence data. The most effective method so far is a deep generative model based on the variational autoencoder (VAE) that models the distributions using a latent variable. In this study, we propose a deep autoregressive generative model named mutationTCN, which employs dilated causal convolutions and attention mechanism for the modeling of inter-residue correlations in a biological sequence. Results We show that this model is competitive with the VAE model when tested against a set of 42 high-throughput mutation scan experiments, with the mean improvement in Spearman rank correlation ∼0.023. In particular, our model can more efficiently capture information from multiple sequence alignments with lower effective number of sequences, such as in viral sequence families, compared with the latent variable model. Also, we extend this architecture to a semi-supervised learning framework, which shows high prediction accuracy. We show that our model enables a direct optimization of the data likelihood and allows for a simple and stable training process. Availability and implementation Source code is available at https://github.com/ha01994/mutationTCN. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Contextual Multiple Sequence Alignment

2005 ◽  
Vol 2005 (2) ◽  
pp. 124-131 ◽  
Author(s):  
Anna Gambin ◽  
Rafał Otto
Keyword(s):  
Pairwise Alignment ◽  
Multiple Alignment ◽  
Biological Data ◽  
Context Dependency ◽  
Alignment Algorithm ◽  
Orphan Genes ◽  
Multiple Sequence ◽  
New Family ◽  
Substitution Matrices ◽  
Global And Local

In a recently proposed contextual alignment model, efficient algorithms exist for global and local pairwise alignment of protein sequences. Preliminary results obtained for biological data are very promising. Our main motivation was to adopt the idea of context dependency to the multiple-alignment setting. To this aim the relaxation of the model was developed (we call this new modelaveraged contextual alignment) and a new family of amino acids substitution matrices are constructed. In this paper we present a contextual multiple-alignment algorithm and report the outcomes of experiments performed for the BAliBASE test set. The contextual approach turned out to give much better results for the set of sequences containing orphan genes.

scholarly journals Trauma exposure and refugee status as predictors of mental health outcomes in treatment-seeking refugees

2015 ◽  
Vol 39 (4) ◽  
pp. 178-182 ◽  
Author(s):  
Jeroen W. Knipscheer ◽  
Marieke Sleijpen ◽  
Trudy Mooren ◽  
F. Jackie June ter Heide ◽  
Niels van der Aa
Keyword(s):  
Mental Health ◽  
Symptom Severity ◽  
Health Centre ◽  
Traumatic Events ◽  
Trauma Exposure ◽  
Time Frame ◽  
Post Traumatic Stress ◽  
Refugee Status ◽  
Traumatic Memories ◽  
Reasonable Time Frame

Aims and methodThis study aimed to identify predictors of symptom severity for post-traumatic stress disorder (PTSD) and depression in asylum seekers and refugees referred to a specialised mental health centre. Trauma exposure (number and domain of event), refugee status and severity of PTSD and depression were assessed in 688 refugees.ResultsSymptom severity of PTSD and depression was significantly associated with lack of refugee status and accumulation of traumatic events. Four domains of traumatic events (human rights abuse, lack of necessities, traumatic loss, and separation from others) were not uniquely associated with symptom severity. All factors taken together explained 11% of variance in PTSD and depression.Clinical implicationsTo account for multiple predictors of symptom severity including multiple traumatic events, treatment for traumatised refugees may need to be multimodal and enable the processing of multiple traumatic memories within a reasonable time-frame.

Sign in / Sign up

Export Citation Format

Share Document