scholarly journals All fingers are not the same: Handling variable-length sequences in a discriminative setting using conformal multi-instance kernels

2017 ◽  
Author(s):  
Sarvesh Nikumbh ◽  
Peter Ebert ◽  
Nico Pfeifer
Keyword(s):  
Binary Classification ◽  
Positional Information ◽  
Weight Vector ◽  
Variable Length ◽  
Genomic Sequences ◽  
String Kernels ◽  
Promoter Sequences ◽  
Novel Approach ◽  
The Individual ◽  
Visualization Techniques

AbstractMost string kernels for comparison of genomic sequences are generally tied to using (absolute) positional information of the features in the individual sequences. This poses limitations when comparing variable-length sequences using such string kernels. For example, profiling chromatin interactions by 3C-based experiments results in variable-length genomic sequences (restriction fragments). Here, exact position-wise occurrence of signals in sequences may not be as important as in the scenario of analysis of the promoter sequences, that typically have a transcription start site as reference. Existing position-aware string kernels have been shown to be useful for the latter scenario.In this work, we propose a novel approach for sequence comparison that enables larger positional freedom than most of the existing approaches, can identify a possibly dispersed set of features in comparing variable-length sequences, and can handle both the aforementioned scenarios. Our approach, CoMIK, identifies not just the features useful towards classification but also their locations in the variable-length sequences, as evidenced by the results of three binary classification experiments, aided by recently introduced visualization techniques. Furthermore, we show that we are able to efficiently retrieve and interpret the weight vector for the complex setting of multiple multi-instance kernels.

HOW MULTIPLE AUXIN RESPONSIVE ELEMENTS MAY INTERACT IN PLANT PROMOTERS: A REVERSE PROBLEM SOLUTION

2013 ◽  
Vol 11 (01) ◽  
pp. 1340011 ◽  
Author(s):  
VICTORIA V. MIRONOVA ◽  
NADYA A. OMELYANCHUK ◽  
MARIA S. SAVINA ◽  
PETR M. PONOMARENKO ◽  
MIKHAIL P. PONOMARENKO ◽  
...  
Keyword(s):  
Additive Model ◽  
Multiplicative Model ◽  
Problem Solution ◽  
Reverse Problem ◽  
Promoter Sequences ◽  
Novel Approach ◽  
Plant Genes ◽  
Plant Promoters ◽  
The Individual ◽  
The Impact

Plant hormone auxin is a key regulator of growth and development. Auxin affects gene expression through ARF transcription factors, which bind specifically auxin responsive elements (AuxREs). Auxin responsive genes usually have more than one AuxRE, for example, a widely used auxin sensor DR5 contains seven AuxREs. Auxin responsive regions of several plant genes have been studied using sets of transgenic constructions in which the activity of one or several AuxREs were abolished. Here we present the method for analysis of the datasets on promoter activity assays having promoter sequences, namely, number and sequences of AuxREs, altogether with their measured auxin induction level. The method for a reverse problem solution considers two extreme models of AuxRE cooperation. Additive model describes auxin induction level of a gene as a sum of the individual AuxREs impacts. Multiplicative model considers pure cooperation between the AuxREs, where the combined effect is the multiplication of the individual AuxRE impacts. The reverse problem solution allows estimating the impact of an individual AuxRE into the induction level and the model for their cooperation. For promoters of three genes belonging to different plant species we showed that the multiplicative model fits better than additive. The reverse problem solution also suggests repressive state of auxin responsive promoters before auxin induction. The developed method provides possibility to investigate AuxRE structure-activity relationship and may be used as the basis for a novel approach for AuxRE recognition.

scholarly journals A Meta Classification Model for Stegoanalysis using Generic NN

2019 ◽  
Vol 8 (2) ◽  
pp. 736-743
Keyword(s):  
Binary Classification ◽  
Weight Vector ◽  
Classification Model ◽  
Pass Filter ◽  
Feature Representations ◽  
Preparation Phase ◽  
Core Idea ◽  
The Individual ◽  
High Pass ◽  
Blind Steganalysis

The core idea behind deep learning is that comprehensive feature representations can be efficiently learned with the deep architectures which are collected of stacked layer of trainable non linear operation. However, because of the diversity of image content, it is hard to learn effective feature representations directly from images for steGAnalysis. SteGAnalysis may be generally figured as binary classification issue. This technique, which is called a universal/blind steGAnalysis, will become the principle stream around current steGAnalytic algorithms. In the preparation phase, effective features which are sensitive with message embedding are concentrated on highlight possibility control by steGAnographier. Then, a binary classifier will be discovered looking into pairs from claiming blanket pictures and their relating stegos pointing with Figure a limit on recognize steGAnography. On testing phase, those prepared classifier is used to anticipate labels from claiming new enter pictures. Past exploration indicated that it will be rather critical to power spread Characteristics Also stego offers to be paired, i. e. SteGAnalytic offers from claiming spread pictures And their stego pictures ought further bolstering be safeguarded in the preparing situated. Otherwise, breaking cover-stego pairs in distinctive sets might present biased error and prompt to a suboptimal execution. Proposed approaches have to fix the kernel of first layer as the HPF (high-pass filter). It is so-called pre-processing layer. We suggested another technic with characteristic decrease done which characteristic Choice and extraction And classifier preparation need aid performed at the same time utilizing a generic calculation. That generic calculation optimizes An characteristic weight vector used to scale the individual features in the unique example vectors. A masker vector may be likewise utilized to concurrent Choice of a characteristic subset. We utilize this technobabble clinched alongside mix with those RESNET, and look at the outcomes with established characteristic Choice and extraction systems.

Using hidden Markov models to find discrete targets in continuous sociophonetic data

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Daniel Duncan
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Abstract Representation ◽  
Language Variation And Change ◽  
Novel Approach ◽  
Single Target ◽  
The Individual ◽  
Individual Speaker

Abstract Advances in sociophonetic research resulted in features once sorted into discrete bins now being measured continuously. This has implied a shift in what sociolinguists view as the abstract representation of the sociolinguistic variable. When measured discretely, variation is variation in selection: one variant is selected for production, and factors influencing language variation and change are influencing the frequency at which variants are selected. Measured continuously, variation is variation in execution: speakers have a single target for production, which they approximate with varying success. This paper suggests that both approaches can and should be considered in sociophonetic analysis. To that end, I offer the use of hidden Markov models (HMMs) as a novel approach to find speakers’ multiple targets within continuous data. Using the lot vowel among whites in Greater St. Louis as a case study, I compare 2-state and 1-state HMMs constructed at the individual speaker level. Ten of fifty-two speakers’ production is shown to involve the regular use of distinct fronted and backed variants of the vowel. This finding illustrates HMMs’ capacity to allow us to consider variation as both variant selection and execution, making them a useful tool in the analysis of sociophonetic data.

scholarly journals HPM: A Hybrid Model for User’s Behavior Prediction Based on N-Gram Parsing and Access Logs

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Sonia Setia ◽  
Verma Jyoti ◽  
Neelam Duhan
Keyword(s):  
Web Mining ◽  
Contextual Information ◽  
Hybrid Approach ◽  
Web Pages ◽  
Continuous Growth ◽  
Novel Approach ◽  
Content Mining ◽  
Long Access ◽  
N Gram ◽  
The Individual

The continuous growth of the World Wide Web has led to the problem of long access delays. To reduce this delay, prefetching techniques have been used to predict the users’ browsing behavior to fetch the web pages before the user explicitly demands that web page. To make near accurate predictions for users’ search behavior is a complex task faced by researchers for many years. For this, various web mining techniques have been used. However, it is observed that either of the methods has its own set of drawbacks. In this paper, a novel approach has been proposed to make a hybrid prediction model that integrates usage mining and content mining techniques to tackle the individual challenges of both these approaches. The proposed method uses N-gram parsing along with the click count of the queries to capture more contextual information as an effort to improve the prediction of web pages. Evaluation of the proposed hybrid approach has been done by using AOL search logs, which shows a 26% increase in precision of prediction and a 10% increase in hit ratio on average as compared to other mining techniques.

scholarly journals Design of a Didactical Activity for the Analysis of Uncertainties in Thermography through the Use of Robust Statistics as Teacher-Oriented Approach

2021 ◽  
Vol 13 (3) ◽  
pp. 402
Author(s):  
Pablo Rodríguez-Gonzálvez ◽  
Manuel Rodríguez-Martín
Keyword(s):  
Open Source ◽  
Robust Statistics ◽  
Three Dimensional ◽  
Learning Material ◽  
Novel Approach ◽  
Learning Programs ◽  
Institutional Learning ◽  
Thermographic Image ◽  
Visualization Techniques ◽  
Free Open Source

The thermography as a methodology to quantitative data acquisition is not usually addressed in the degrees of university programs. The present manuscript proposes a novel approach for the acquisition of advanced competences in engineering courses associated with the use of thermographic images via free/open-source software solutions. This strategy is established from a research based on the statistical and three-dimensional visualization techniques over thermographic imagery to improve the interpretation and comprehension of the different sources of error affecting the measurements and, thereby, the conclusions and analysis arising from them. The novelty is focused on the detection of non-normalities in thermographic images, which is illustrates in the experimental section. Additionally, the specific workflow for the generation of learning material related with this aim is raised for asynchronous and e-learning programs. These virtual materials can be easily deployed in an institutional learning management system, allowing the students to work with the models by means of free/open-source solutions easily. Subsequently, the present approach will give new tools to improve the application of professional techniques, will improve the students’ critical sense to know how to interpret the uncertainties in thermography using a single thermographic image, therefore they will be better prepared to face future challenges with more critical thinking.

scholarly journals A Genetic Algorithm Approach for Group Recommender System Based on Partial Rankings

2018 ◽  
Vol 29 (1) ◽  
pp. 653-663 ◽  
Author(s):  
Ritu Meena ◽  
Kamal K. Bharadwaj
Keyword(s):  
Genetic Algorithm ◽  
Recommender Systems ◽  
Rank Aggregation ◽  
Partial Ranking ◽  
Novel Approach ◽  
Kendall Tau Distance ◽  
Ultimate Objective ◽  
Aggregation Technique ◽  
The Individual ◽  
Group Recommender

Abstract Many recommender systems frequently make suggestions for group consumable items to the individual users. There has been much work done in group recommender systems (GRSs) with full ranking, but partial ranking (PR) where items are partially ranked still remains a challenge. The ultimate objective of this work is to propose rank aggregation technique for effectively handling the PR problem. Additionally, in real applications, most of the studies have focused on PR without ties (PRWOT). However, the rankings may have ties where some items are placed in the same position, but where some items are partially ranked to be aggregated may not be permutations. In this work, in order to handle problem of PR in GRS for PRWOT and PR with ties (PRWT), we propose a novel approach to GRS based on genetic algorithm (GA) where for PRWOT Spearman foot rule distance and for PRWT Kendall tau distance with bucket order are used as fitness functions. Experimental results are presented that clearly demonstrate that our proposed GRS based on GA for PRWOT (GRS-GA-PRWOT) and PRWT (GRS-GA-PRWT) outperforms well-known baseline GRS techniques.

scholarly journals Identification of promoter sequences using archaeal genomic sequences.

2003 ◽  
Vol 79B (5) ◽  
pp. 131-136 ◽  
Author(s):  
Naoki AMANO ◽  
Kuniaki TSUJI ◽  
Masashi SUZUKI
Keyword(s):  
Genomic Sequences ◽  
Promoter Sequences

scholarly journals Effects on Some Therapeutical, Biochemical, and Immunological Parameters of Honey Bee (Apis mellifera) Exposed to Probiotic Treatments, in Field and Laboratory Conditions

Insects ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 638
Author(s):  
Ivana Tlak Gajger ◽  
Josipa Vlainić ◽  
Petra Šoštarić ◽  
Janez Prešern ◽  
Jernej Bubnič ◽  
...  
Keyword(s):  
Honey Bee ◽  
Honey Bees ◽  
Immunological Parameters ◽  
Individual Level ◽  
Sugar Syrup ◽  
Novel Approach ◽  
The Individual ◽  
Field And Laboratory Conditions ◽  
Negative Factors ◽  
Bee Colonies

Several negative factors contribute to a decline in the number of insect pollinators. As a novel approach in therapy, we hypothesize that the EM® for bees could potentially have an important therapeutic and immunomodulatory effect on honey bee colonies. The aim of our study was to evaluate its impact on honey bees at the individual and colony level. This is the first appliance of the commercial probiotic mix EM® PROBIOTIC FOR BEES in honey bees as economically important social insects. The sugar syrup with 10% of probiotic was administered by spraying or feeding the honey bee colonies in the field conditions, in order to evaluate the infection levels with spores of Nosema spp. and colonies’ strength. Moreover, in laboratory-controlled conditions, in the hoarding cages, adult workers have been fed with sugar syrup supplemented with 2.5, 5, and 10% of EM® for bees for biochemical and immunological analyses of hemolymph, and with 5 and 10% for measuring the size of hypopharyngeal glands. It was found that following the EM® for bees administration the Nosema spp. spore counts in colonies were significantly reduced, and colonies’ strength was increased. The results at the individual level showed significant positive physiological changes in treated groups of adult bees, revealing at the same time a higher mortality rate when feeding sugar syrup supplemented with the probiotic.

scholarly journals Beyond non-integer Hill coefficients: A novel approach to analyzing binding data, applied to Na+-driven transporters

2015 ◽  
Vol 145 (6) ◽  
pp. 555-563 ◽  
Author(s):  
Silvia Ravera ◽  
Matthias Quick ◽  
Juan P. Nicola ◽  
Nancy Carrasco ◽  
L. Mario Amzel
Keyword(s):  
Hill Equation ◽  
Integer Coefficient ◽  
Novel Approach ◽  
Binding Data ◽  
Na Ions ◽  
Hill Coefficients ◽  
Integer Exponent ◽  
The Individual ◽  
The Hill ◽  
Analyze Data

Prokaryotic and eukaryotic Na+-driven transporters couple the movement of one or more Na+ ions down their electrochemical gradient to the active transport of a variety of solutes. When more than one Na+ is involved, Na+-binding data are usually analyzed using the Hill equation with a non-integer exponent n. The results of this analysis are an overall Kd-like constant equal to the concentration of ligand that produces half saturation and n, a measure of cooperativity. This information is usually insufficient to provide the basis for mechanistic models. In the case of transport using two Na+ ions, an n < 2 indicates that molecules with only one of the two sites occupied are present at low saturation. Here, we propose a new way of analyzing Na+-binding data for the case of two Na+ ions that, by taking into account binding to individual sites, provides far more information than can be obtained by using the Hill equation with a non-integer coefficient: it yields pairs of possible values for the Na+ affinities of the individual sites that can only vary within narrowly bounded ranges. To illustrate the advantages of the method, we present experimental scintillation proximity assay (SPA) data on binding of Na+ to the Na+/I− symporter (NIS). SPA is a method widely used to study the binding of Na+ to Na+-driven transporters. NIS is the key plasma membrane protein that mediates active I− transport in the thyroid gland, the first step in the biosynthesis of the thyroid hormones, of which iodine is an essential constituent. NIS activity is electrogenic, with a 2:1 Na+/I− transport stoichiometry. The formalism proposed here is general and can be used to analyze data on other proteins with two binding sites for the same substrate.

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