59 research outputs found
New Earthworm Record from Division Muzaffarabad, Azad Kashmir, Pakistan Supported by Molecular Markers
No full textMustafa, Rozina Ghulam, Andleeb, Saiqa, Domínguez, Jorge, Abbasi, Wajid Arshad, Ali, Shaukat, Marchán, Daniel Fernández (2023): New Earthworm Record from Division Muzaffarabad, Azad Kashmir, Pakistan Supported by Molecular Markers. Zootaxa 5255 (1): 93-100, DOI: 10.11646/zootaxa.5255.1.13, URL: http://dx.doi.org/10.11646/zootaxa.5255.1.1
FIGURE 1 in New Earthworm Record from Division Muzaffarabad, Azad Kashmir, Pakistan Supported by Molecular Markers
No full textFIGURE 1: External morphological features of Perelia kaznakovi. A= prostomium, B= chaetal arrangement, C= dorsal pore, D= clitellum, E= male pore, F= tubercula pubertatisPublished as part of Mustafa, Rozina Ghulam, Andleeb, Saiqa, Domínguez, Jorge, Abbasi, Wajid Arshad, Ali, Shaukat & Marchán, Daniel Fernández, 2023, New Earthworm Record from Division Muzaffarabad, Azad Kashmir, Pakistan Supported by Molecular Markers, pp. 93-100 in Zootaxa 5255 (1) on page 96, DOI: 10.11646/zootaxa.5255.1.13, http://zenodo.org/record/774452
FIGURE 2 in New Earthworm Record from Division Muzaffarabad, Azad Kashmir, Pakistan Supported by Molecular Markers
No full textFIGURE 2: Internal anatomy of Perelia kaznakovi. A= Gizzard and intestine, B= S-shaped nephridia, C= seminal vesicles, D= spermathecaePublished as part of Mustafa, Rozina Ghulam, Andleeb, Saiqa, Domínguez, Jorge, Abbasi, Wajid Arshad, Ali, Shaukat & Marchán, Daniel Fernández, 2023, New Earthworm Record from Division Muzaffarabad, Azad Kashmir, Pakistan Supported by Molecular Markers, pp. 93-100 in Zootaxa 5255 (1) on page 96, DOI: 10.11646/zootaxa.5255.1.13, http://zenodo.org/record/774452
FIGURE 3 in New Earthworm Record from Division Muzaffarabad, Azad Kashmir, Pakistan Supported by Molecular Markers
No full textFIGURE 3: Fifty percent majority-rule consensus tree. It shows the phylogenetic relationships of Perelia kaznakovi obtained by Bayesian phylogenetic analysis of the concatenated sequence of molecular markers COI–16S-12S–ND1–28S. Posterior probability support values are shown beside the corresponding nodes. The bottom bar shows the scale of the branch lengths.Published as part of Mustafa, Rozina Ghulam, Andleeb, Saiqa, Domínguez, Jorge, Abbasi, Wajid Arshad, Ali, Shaukat & Marchán, Daniel Fernández, 2023, New Earthworm Record from Division Muzaffarabad, Azad Kashmir, Pakistan Supported by Molecular Markers, pp. 93-100 in Zootaxa 5255 (1) on page 97, DOI: 10.11646/zootaxa.5255.1.13, http://zenodo.org/record/774452
Perelia kaznakovi
No full text<i>Perelia kaznakovi</i> (Michaelsen, 1910) <p> <i>Helodrilus kaznakovi</i> Michaelsen, 1910: 65.</p> <p> <i>Allolobophora kaznakovi</i> (Michaelsen, 1910)</p> <p> <i>Eophila asiatica</i> Malevic, 1949: 1005.</p> <p> <i>Helodrilus (Eophila) kaznakovi</i>: Perel 1976</p> <p> <i>Perelia kaznakovi</i>: Easton 1983</p>Published as part of <i>Mustafa, Rozina Ghulam, Andleeb, Saiqa, Domínguez, Jorge, Abbasi, Wajid Arshad, Ali, Shaukat & Marchán, Daniel Fernández, 2023, New Earthworm Record from Division Muzaffarabad, Azad Kashmir, Pakistan Supported by Molecular Markers, pp. 93-100 in Zootaxa 5255 (1)</i> on page 95, DOI: 10.11646/zootaxa.5255.1.13, <a href="http://zenodo.org/record/7744522">http://zenodo.org/record/7744522</a>
Issues in performance evaluation for host–pathogen protein interaction prediction
No full textThe study of interactions between host and pathogen proteins is important for understanding the underlying mechanisms of infectious diseases and for developing novel therapeutic solutions. Wet-lab techniques for detecting protein–protein interactions (PPIs) can benefit from computational predictions. Machine learning is one of the computational approaches that can assist biologists by predicting promising PPIs. A number of machine learning based methods for predicting host–pathogen interactions (HPI) have been proposed in the literature. The techniques used for assessing the accuracy of such predictors are of critical importance in this domain. In this paper, we question the effectiveness of K-fold cross-validation for estimating the generalization ability of HPI prediction for proteins with no known interactions. K-fold cross-validation does not model this scenario, and we demonstrate a sizable difference between its performance and the performance of an alternative evaluation scheme called leave one pathogen protein out (LOPO) cross-validation. LOPO is more effective in modeling the real world use of HPI predictors, specifically for cases in which no information about the interacting partners of a pathogen protein is available during training. We also point out that currently used metrics such as areas under the precision-recall or receiver operating characteristic curves are not intuitive to biologists and propose simpler and more directly interpretable metrics for this purpose
A novel unsupervised ensemble framework using concept-based linguistic methods and machine learning for Twitter sentiment analysis
No full textConcept-based sentiment analysis (CBSA) methods have gained prominence in natural language
processing in recent years. These methods consider the underlying semantic meanings of text to
perform different tasks such as Twitter sentiment analysis (assigning positive, negative, or neutral
sentiment to Tweets). CBSA is superior to traditional statistical methods for accurately discovering
sentiment labels. Due to a limited knowledge base, these methods are unable to identify the sentiment
polarity of all kinds of text. Therefore, supervised learning techniques are mostly ensembled with
CBSA methods to classify whole text. These techniques require labeled data. It is a tedious and time consuming task due to the manually labeling of large datasets (Such as Twitter datasets). Therefore,
an unsupervised learning mechanism can be a better alternative to solve this problem. In this paper,
a novel unsupervised learning framework based on Concept-based and hierarchical clustering is
proposed for Twitter sentiment analysis. Popular hierarchical clustering methods including single
linkage, complete linkage, and average linkage algorithms are ensembled serially. Two different
feature representation methods including Boolean and Term frequency-inverse document frequency
(TF-IDF) are investigated. We have also experimented with Well-known classifiers (Naïve Bayes,
Neural Network) for a fair comparison. Accuracy measure (proportion of correct predictions) is used
to evaluate the performance of understudied techniques. It is empirically shown that the performance
of unsupervised learning techniques is comparable with supervised learning techniques
A novel unsupervised ensemble framework using concept-based linguistic methods and machine learning for Twitter sentiment analysis
No full textConcept-based sentiment analysis (CBSA) methods have gained prominence in natural language processing in recent years. These methods consider the underlying semantic meanings of text to perform different tasks such as Twitter sentiment analysis (assigning positive, negative, or neutral sentiment to Tweets). CBSA is superior to traditional statistical methods for accurately discovering sentiment labels. Due to a limited knowledge base, these methods are unable to identify the sentiment polarity of all kinds of text. Therefore, supervised learning techniques are mostly ensembled with CBSA methods to classify whole text. These techniques require labeled data. It is a tedious and time consuming task due to the manually labeling of large datasets (Such as Twitter datasets). Therefore, an unsupervised learning mechanism can be a better alternative to solve this problem. In this paper, a novel unsupervised learning framework based on Concept-based and hierarchical clustering is proposed for Twitter sentiment analysis. Popular hierarchical clustering methods including single linkage, complete linkage, and average linkage algorithms are ensembled serially. Two different feature representation methods including Boolean and Term frequency-inverse document frequency (TF-IDF) are investigated. We have also experimented with Well-known classifiers (Naïve Bayes, Neural Network) for a fair comparison. Accuracy measure (proportion of correct predictions) is used to evaluate the performance of understudied techniques. It is empirically shown that the performance of unsupervised learning techniques is comparable with supervised learning techniques
CaMELS : In silicoprediction of calmodulin binding proteins and their binding sites
No full textDue to Ca2+‐dependent binding and the sequence diversity of Calmodulin (CaM) binding proteins, identifying CaM interactions and binding sites in the wet‐lab is tedious and costly. Therefore, computational methods for this purpose are crucial to the design of such wet‐lab experiments. We present an algorithm suite called CaMELS (CalModulin intEraction Learning System) for predicting proteins that interact with CaM as well as their binding sites using sequence information alone. CaMELS offers state of the art accuracy for both CaM interaction and binding site prediction and can aid biologists in studying CaM binding proteins. For CaM interaction prediction, CaMELS uses protein sequence features coupled with a large‐margin classifier. CaMELS models the binding site prediction problem using multiple instance machine learning with a custom optimization algorithm which allows more effective learning over imprecisely annotated CaM‐binding sites during training. CaMELS has been extensively benchmarked using a variety of data sets, mutagenic studies, proteome‐wide Gene Ontology enrichment analyses and protein structures. Our experiments indicate that CaMELS outperforms simple motif‐based search and other existing methods for interaction and binding site prediction. We have also found that the whole sequence of a protein, rather than just its binding site, is important for predicting its interaction with CaM. Using the machine learning model in CaMELS, we have identified important features of protein sequences for CaM interaction prediction as well as characteristic amino acid sub‐sequences and their relative position for identifying CaM binding sites. Python code for training and evaluating CaMELS together with a webserver implementation is available at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#camels
Learning protein binding affinity using privileged information
No full textAbstract Background Determining protein-protein interactions and their binding affinity are important in understanding cellular biological processes, discovery and design of novel therapeutics, protein engineering, and mutagenesis studies. Due to the time and effort required in wet lab experiments, computational prediction of binding affinity from sequence or structure is an important area of research. Structure-based methods, though more accurate than sequence-based techniques, are limited in their applicability due to limited availability of protein structure data. Results In this study, we propose a novel machine learning method for predicting binding affinity that uses protein 3D structure as privileged information at training time while expecting only protein sequence information during testing. Using the method, which is based on the framework of learning using privileged information (LUPI), we have achieved improved performance over corresponding sequence-based binding affinity prediction methods that do not have access to privileged information during training. Our experiments show that with the proposed framework which uses structure only during training, it is possible to achieve classification performance comparable to that which is obtained using structure-based features. Evaluation on an independent test set shows improved performance over the PPA-Pred2 method as well. Conclusions The proposed method outperforms several baseline learners and a state-of-the-art binding affinity predictor not only in cross-validation, but also on an additional validation dataset, demonstrating the utility of the LUPI framework for problems that would benefit from classification using structure-based features. The implementation of LUPI developed for this work is expected to be useful in other areas of bioinformatics as well
- …
