Identification of Distinguishing Motifs

Motif identification for DNA sequences has many important applications in biological studies, including diagnostic probe design, locating binding sites and regulatory signals, and potential drug target identification. There are two versions—the Single Group and Two Groups. Here, the occurrences of the motif in the given sequences have errors. Currently, most of existing programs can only handle the case of single group. However, most of the programs do not allow indels (insertions and deletions) in the occurrences of the motif. In this paper, the authors propose a randomized algorithm for the one group problem that can handle indels in the occurrences of the motif. Finally, an algorithm for the two groups’ problem is given along with extensive simulations evaluating algorithms.

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RANDOMIZED ALGORITHMS FOR MOTIF DETECTION

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720005001508 ◽

2005 ◽

Vol 03 (05) ◽

pp. 1039-1052 ◽

Cited By ~ 21

Author(s):

LUSHENG WANG ◽

LIANG DONG

Keyword(s):

Em Algorithm ◽

Dna Sequences ◽

Algorithm Design ◽

Randomized Algorithm ◽

Software Tool ◽

Motif Detection ◽

Biological Studies ◽

Pattern Length ◽

Genetic Probes ◽

Consensus Pattern

Motivation: Motif detection for DNA sequences has many important applications in biological studies, e.g. locating binding sites regulatory signals, designing genetic probes etc. In this paper, we propose a randomized algorithm, design an improved EM algorithm and combine them to form a software tool. Results: (1) We design a randomized algorithm for consensus pattern problem. We can show that with high probability, our randomized algorithm finds a pattern in polynomial time with cost error at most ∊ × l for each string, where l is the length of the motif and ∊ can be any positive number given by the user. (2) We design an improved EM algorithm that outperforms the original EM algorithm. (3) We develop a software tool, MotifDetector, that uses our randomized algorithm to find good seeds and uses the improved EM algorithm to do local search. We compare MotifDetector with Buhler and Tompa's PROJECTION which is considered to be the best known software for motif detection. Simulations show that MotifDetector is slower than PROJECTION when the pattern length is relatively small, and outperforms PROJECTION when the pattern length becomes large. Availability: It is available for free at , subject to copyright restrictions.

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THE EU SUGAR MARKET PROFILE AND ITS MAIN DRIVERS

Proceedings of the 7th International Scientific Conference Rural Development 2015 ◽

10.15544/rd.2015.111 ◽

2015 ◽

Author(s):

Lubos SMUTKA ◽

Irena BENEŠOVÁ ◽

Patrik ROVNÝ ◽

Renata MATYSIK-PEJAS

Keyword(s):

Market Failure ◽

Current System ◽

Quota System ◽

Considerable Debate ◽

Sugar Market ◽

The Common ◽

The One ◽

Production Capacities ◽

The Given ◽

The Eu

Sugar is one of the most important elements in human nutrition. The Common Market Organisation for sugar has been a subject of considerable debate since its establishment in 1968. The European agricultural market has been criticized for its heavy regulations and subsidization. The sugar market is one of the most regulated ones; however, this will change radically in 2017 when the current system of production quotas will end. The current EU sugar market changed is structure during the last several decades. The significant number of companies left the market and EU internal sugar market became more concentrated. The aim of this paper is presentation characteristics of sugar market with respect to the supposed market failure – reduction in competition. The analysis also identifies the main drivers and determinants of the EU especially quota sugar market. In relation to paper’s aim the following results are important. The present conditions of the European sugar market have led to market failure when nearly 75 % (10 million tonnes) of the quota is controlled by five multinational companies only. These multinational alliances (especially German and French one) are also taking control over the production capacities of their subsidiaries. In most countries, this causes serious problems as the given quota is controlled by one or two producers only. This is a significant indicator of market imperfection. The quota system cannot overcome the problem of production quotas on the one hand and the demand on the other; furthermore, it also leads to economic inefficiency. The current EU sugar market is under the control of only Sudzucker, Nordzucker, Pfeifer and Langen, Tereos and ABF.

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Gene knockdown by RNA interference in the cat flea (Ctenocephalides felis): Utilisingde novotranscriptome data for developing drug target identification strategies

10.1603/ice.2016.108132 ◽

2016 ◽

Author(s):

Catriona H McIntosh

Keyword(s):

Rna Interference ◽

Drug Target ◽

Target Identification ◽

Ctenocephalides Felis ◽

Gene Knockdown ◽

Cat Flea ◽

Drug Target Identification

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Quasi-Periodic Control Technique for Minimizing Fuel Consumption During Record Vehicle Competition

Archive of Mechanical Engineering ◽

10.2478/meceng-2013-0012 ◽

2013 ◽

Vol 60 (2) ◽

pp. 185-197 ◽

Cited By ~ 2

Author(s):

Paweł Sulikowski ◽

Ryszard Maronski

Keyword(s):

Fuel Consumption ◽

Optimal Control Theory ◽

Slope Angle ◽

Control Technique ◽

Decision Variables ◽

Aeronautical Engineering ◽

The One ◽

Optimal Driving ◽

The Given ◽

Engine Power

The problem of the optimal driving technique during the fuel economy competition is reconsidered. The vehicle is regarded as a particle moving on a trace with a variable slope angle. The fuel consumption is minimized as the vehicle covers the given distance in a given time. It is assumed that the run consists of two recurrent phases: acceleration with a full available engine power and coasting down with the engine turned off. The most fuel-efficient technique for shifting gears during acceleration is found. The decision variables are: the vehicle velocities at which the gears should be shifted, on the one hand, and the vehicle velocities when the engine should be turned on and off, on the other hand. For the data of students’ vehicle representing the Faculty of Power and Aeronautical Engineering it has been found that such driving strategy is more effective in comparison with a constant speed strategy with the engine partly throttled, as well as a strategy resulting from optimal control theory when the engine is still active.

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Development of a target identification approach using native mass spectrometry

Scientific Reports ◽

10.1038/s41598-021-81859-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Miaomiao Liu ◽

Wesley C. Van Voorhis ◽

Ronald J. Quinn

Keyword(s):

Mass Spectrometry ◽

Protein Interactions ◽

Target Identification ◽

Native Mass Spectrometry ◽

Pharmaceutical Drugs ◽

Ligand Complex ◽

Experimental Conditions ◽

Protein Mixture ◽

Native Ms ◽

Drug Target Identification

AbstractA key step in the development of new pharmaceutical drugs is the identification of the molecular target and distinguishing this from all other gene products that respond indirectly to the drug. Target identification remains a crucial process and a current bottleneck for advancing hits through the discovery pipeline. Here we report a method, that takes advantage of the specific detection of protein–ligand complexes by native mass spectrometry (MS) to probe the protein partner of a ligand in an untargeted method. The key advantage is that it uses unmodified small molecules for binding and, thereby, it does not require labelled ligands and is not limited by the chemistry required to tag the molecule. We demonstrate the use of native MS to identify known ligand–protein interactions in a protein mixture under various experimental conditions. A protein–ligand complex was successfully detected between parthenolide and thioredoxin (PfTrx) in a five-protein mixture, as well as when parthenolide was mixed in a bacterial cell lysate spiked with PfTrx. We provide preliminary data that native MS could be used to identify binding targets for any small molecule.

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IIMLP: integrated information-entropy-based method for LncRNA prediction

BMC Bioinformatics ◽

10.1186/s12859-020-03884-w ◽

2021 ◽

Vol 22 (S3) ◽

Author(s):

Junyi Li ◽

Huinian Li ◽

Xiao Ye ◽

Li Zhang ◽

Qingzhe Xu ◽

...

Keyword(s):

Machine Learning ◽

Dna Sequences ◽

Information Entropy ◽

Area Under The Curve ◽

Prediction Method ◽

Machine Learning Algorithms ◽

Reading Frame ◽

Non Coding Rna ◽

The One ◽

Long Non Coding Rna

Abstract Background The prediction of long non-coding RNA (lncRNA) has attracted great attention from researchers, as more and more evidence indicate that various complex human diseases are closely related to lncRNAs. In the era of bio-med big data, in addition to the prediction of lncRNAs by biological experimental methods, many computational methods based on machine learning have been proposed to make better use of the sequence resources of lncRNAs. Results We developed the lncRNA prediction method by integrating information-entropy-based features and machine learning algorithms. We calculate generalized topological entropy and generate 6 novel features for lncRNA sequences. By employing these 6 features and other features such as open reading frame, we apply supporting vector machine, XGBoost and random forest algorithms to distinguish human lncRNAs. We compare our method with the one which has more K-mer features and results show that our method has higher area under the curve up to 99.7905%. Conclusions We develop an accurate and efficient method which has novel information entropy features to analyze and classify lncRNAs. Our method is also extendable for research on the other functional elements in DNA sequences.

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Drug Target Identification with Machine Learning: How to Choose Negative Examples

International Journal of Molecular Sciences ◽

10.3390/ijms22105118 ◽

2021 ◽

Vol 22 (10) ◽

pp. 5118

Author(s):

Matthieu Najm ◽

Chloé-Agathe Azencott ◽

Benoit Playe ◽

Véronique Stoven

Keyword(s):

Machine Learning ◽

Drug Target ◽

Target Identification ◽

Target Prediction ◽

False Positives ◽

Machine Learning Algorithms ◽

Statistical Bias ◽

Protein Targets ◽

Drug Target Identification ◽

Approved Drugs

Identification of the protein targets of hit molecules is essential in the drug discovery process. Target prediction with machine learning algorithms can help accelerate this search, limiting the number of required experiments. However, Drug-Target Interactions databases used for training present high statistical bias, leading to a high number of false positives, thus increasing time and cost of experimental validation campaigns. To minimize the number of false positives among predicted targets, we propose a new scheme for choosing negative examples, so that each protein and each drug appears an equal number of times in positive and negative examples. We artificially reproduce the process of target identification for three specific drugs, and more globally for 200 approved drugs. For the detailed three drug examples, and for the larger set of 200 drugs, training with the proposed scheme for the choice of negative examples improved target prediction results: the average number of false positives among the top ranked predicted targets decreased, and overall, the rank of the true targets was improved.Our method corrects databases’ statistical bias and reduces the number of false positive predictions, and therefore the number of useless experiments potentially undertaken.

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"THE SYNTHESIS OF WAR AND THEATRE": SOCIO-HUMANITARIAN DISCOURSE ON THE ESSENCE OF TERRORISM

RSUH/RGGU Bulletin. "Literary Theory. Linguistics. Cultural Studies" Series ◽

10.28995/2686-7249-2020-8-10-23 ◽

2020 ◽

pp. 10-23

Author(s):

Irina V Malygina ◽

◽

Anna V Malygina ◽

Keyword(s):

International Terrorism ◽

Complex Nature ◽

Cultural Resources ◽

Self Presentation ◽

The Status ◽

World Cultural Heritage ◽

The One ◽

Strategic Object ◽

The Given

The article reveals the heuristic potential of social and humanitarian knowledge in understanding the complex nature of terrorism. The given research optics allows to expand traditional frameworks of considering terrorism as a phenomenon caused by political, ideological and economic factors; to reveal and substantiate deep cultural and mental reasons of the given phenomenon; make sense of terrorism as a destructive form of cultural identity. The cultural and historical origins of modern terrorism, which is closely connected with radical Islam, are analyzed in the civilizational system of coordinates “West–East”. The system of argumentation is based on scientific concepts and current artistic practices that interpret the causes of inter-civilizational tension resulting in international terrorism. The change of the status of the artist in the “epoch of terrorism” is analyzed; the theme of theatricalization and aestheticization of terrorist actions and the role of media in these processes are problematized. As a newest trend, which has not received any serious theoretical reflection, the text considers the phenomenon conditionally designated as “sublimation of terrorist activity into a symbolic sphere”, which is manifested in the destruction of monuments of world cultural heritage, in the orientation to culture as a new strategic object of terrorist attacks, on the one hand, and the use of cultural resources for self-presentation and promotion of their ideology by terrorist organizations, on the other

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Influence of Food and Larval Age on the Defensive Chemistry of Satumia pyri

Zeitschrift für Naturforschung C ◽

10.1515/znc-2001-1-215 ◽

2001 ◽

Vol 56 (1-2) ◽

pp. 89-94 ◽

Cited By ~ 2

Author(s):

Reinhold Deml

Keyword(s):

Body Fluids ◽

The Other ◽

Larval Diet ◽

Larval Instars ◽

Defensive Chemistry ◽

Crataegus Monogyna ◽

Secondary Compound ◽

Influence Of Food ◽

The One ◽

The Given

Abstract Scolus secretions and hemolymph of caterpillars of Satumia pyri fed with two different foodplants (Crataegus monogyna, Prunus spinosa) were chemically analyzed and their chemical similarities determined. The secondary-compound patterns obtained for the two body fluids showed no significant differences when compared between the two groups of alterna tively fed last-instar larvae. Thus, the composition of these fluids of full-grown caterpillars is not influenced by the larval diet. However, younger larvae on P. spinosa revealed a diversity of compounds differing significantly from that of larger caterpillars fed with either C. monogyna (both body fluids) or P. spinosa (hemolymph only). This indicates that, on the one hand, the hemolymph composition is adapted to the changing physiological requirements of the given instars whereas, on the other hand, the defensive mixtures remain unaltered in the late larval instars due to a constant spectrum of potential enemies.

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