scholarly journals Plant and Animal sub cellular component localization prediction using multiple combination of various machine learning approaches

2018 ◽  
Vol 7 (1.9) ◽  
pp. 221
Author(s):  
Bipin Nair B J ◽  
Ashik P.v
Keyword(s):  
Machine Learning ◽  
Subcellular Localization ◽  
Drug Target ◽  
Protein Profile ◽  
Learning Approaches ◽  
Cell Organelles ◽  
Profile Vector ◽  
Highly Correlated ◽  
Living Organisms ◽  
Localization Prediction

Membrane proteins are encoded in the genome and functionally important in the living organisms. Information on subcellular localization of cellular proteins has a significant role in the function of cell organelles. Discovery of drug target and system biology between localization and biological function are highly correlated. Therefore, we are predicting the localization of protein using various machine learning approaches. The prediction system based on the integration of the outcome of five sequence based sub-classifiers. The subcellular localization prediction of the final result is based on protein profile vector, which is a result of the sub-classifiers.

scholarly journals Plant-mSubP: a computational framework for the prediction of single- and multi-target protein subcellular localization using integrated machine-learning approaches

AoB Plants ◽  
2019 ◽  
Vol 12 (3) ◽  
Author(s):  
Sitanshu S Sahu ◽  
Cristian D Loaiza ◽  
Rakesh Kaundal
Keyword(s):  
Machine Learning ◽  
Amino Acid ◽  
Subcellular Localization ◽  
Amino Acid Composition ◽  
Acid Composition ◽  
Learning Approaches ◽  
Pseudo Amino Acid Composition ◽  
Hybrid Features ◽  
Dual Label ◽  
Localization Prediction

Abstract The subcellular localization of proteins is very important for characterizing its function in a cell. Accurate prediction of the subcellular locations in computational paradigm has been an active area of interest. Most of the work has been focused on single localization prediction. Only few studies have discussed the multi-target localization, but have not achieved good accuracy so far; in plant sciences, very limited work has been done. Here we report the development of a novel tool Plant-mSubP, which is based on integrated machine learning approaches to efficiently predict the subcellular localizations in plant proteomes. The proposed approach predicts with high accuracy 11 single localizations and three dual locations of plant cell. Several hybrid features based on composition and physicochemical properties of a protein such as amino acid composition, pseudo amino acid composition, auto-correlation descriptors, quasi-sequence-order descriptors and hybrid features are used to represent the protein. The performance of the proposed method has been assessed through a training set as well as an independent test set. Using the hybrid feature of the pseudo amino acid composition, N-Center-C terminal amino acid composition and the dipeptide composition (PseAAC-NCC-DIPEP), an overall accuracy of 81.97 %, 84.75 % and 87.88 % is achieved on the training data set of proteins containing the single-label, single- and dual-label combined, and dual-label proteins, respectively. When tested on the independent data, an accuracy of 64.36 %, 64.84 % and 81.08 % is achieved on the single-label, single- and dual-label, and dual-label proteins, respectively. The prediction models have been implemented on a web server available at http://bioinfo.usu.edu/Plant-mSubP/. The results indicate that the proposed approach is comparable to the existing methods in single localization prediction and outperforms all other existing tools when compared for dual-label proteins. The prediction tool will be a useful resource for better annotation of various plant proteomes.

scholarly journals HumDLoc: Human Protein Subcellular Localization Prediction Using Deep Neural Network

2020 ◽  
Vol 21 (7) ◽  
pp. 546-557
Author(s):  
Rahul Semwal ◽  
Pritish Kumar Varadwaj
Keyword(s):  
Machine Learning ◽  
Subcellular Localization ◽  
Predictive Model ◽  
Sequence Data ◽  
Predictive Performance ◽  
Protein Subcellular Localization ◽  
Subcellular Localization Prediction ◽  
Human Proteins ◽  
Protein Subcellular Localization Prediction ◽  
Localization Prediction

Aims: To develop a tool that can annotate subcellular localization of human proteins. Background: With the progression of high throughput human proteomics projects, an enormous amount of protein sequence data has been discovered in the recent past. All these raw sequence data require precise mapping and annotation for their respective biological role and functional attributes. The functional characteristics of protein molecules are highly dependent on the subcellular localization/ compartment. Therefore, a fully automated and reliable protein subcellular localization prediction system would be very useful for current proteomic research. Objective: To develop a machine learning-based predictive model that can annotate the subcellular localization of human proteins with high accuracy and precision. Methods: In this study, we used the PSI-CD-HIT homology criterion and utilized the sequence-based features of protein sequences to develop a powerful subcellular localization predictive model. The dataset used to train the HumDLoc model was extracted from a reliable data source, Uniprot knowledge base, which helps the model to generalize on the unseen dataset. Result : The proposed model, HumDLoc, was compared with two of the most widely used techniques: CELLO and DeepLoc, and other machine learning-based tools. The result demonstrated promising predictive performance of HumDLoc model based on various machine learning parameters such as accuracy (≥97.00%), precision (≥0.86), recall (≥0.89), MCC score (≥0.86), ROC curve (0.98 square unit), and precision-recall curve (0.93 square unit). Conclusion: In conclusion, HumDLoc was able to outperform several alternative tools for correctly predicting subcellular localization of human proteins. The HumDLoc has been hosted as a web-based tool at https://bioserver.iiita.ac.in/HumDLoc/.

scholarly journals A comparative chemogenic analysis for predicting Drug-Target Pair via Machine Learning Approaches

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Aman Chandra Kaushik ◽  
Aamir Mehmood ◽  
Xiaofeng Dai ◽  
Dong-Qing Wei
Keyword(s):  
Machine Learning ◽  
Drug Target ◽  
Target Pair ◽  
Learning Approaches

scholarly journals Machine Learning Algorithms to Classify and Quantify Multiple Behaviours in Dairy Calves Using a Sensor: Moving beyond Classification in Precision Livestock

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 88
Author(s):  
Charles Carslake ◽  
Jorge A. Vázquez-Diosdado ◽  
Jasmeet Kaler
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Wearable Sensors ◽  
Window Size ◽  
Machine Learning Algorithms ◽  
Dairy Calves ◽  
Learning Approaches ◽  
True Prevalence ◽  
Play Behaviour ◽  
Highly Correlated

Previous research has shown that sensors monitoring lying behaviours and feeding can detect early signs of ill health in calves. There is evidence to suggest that monitoring change in a single behaviour might not be enough for disease prediction. In calves, multiple behaviours such as locomotor play, self-grooming, feeding and activity whilst lying are likely to be informative. However, these behaviours can occur rarely in the real world, which means simply counting behaviours based on the prediction of a classifier can lead to overestimation. Here, we equipped thirteen pre-weaned dairy calves with collar-mounted sensors and monitored their behaviour with video cameras. Behavioural observations were recorded and merged with sensor signals. Features were calculated for 1–10-s windows and an AdaBoost ensemble learning algorithm implemented to classify behaviours. Finally, we developed an adjusted count quantification algorithm to predict the prevalence of locomotor play behaviour on a test dataset with low true prevalence (0.27%). Our algorithm identified locomotor play (99.73% accuracy), self-grooming (98.18% accuracy), ruminating (94.47% accuracy), non-nutritive suckling (94.96% accuracy), nutritive suckling (96.44% accuracy), active lying (90.38% accuracy) and non-active lying (90.38% accuracy). Our results detail recommended sampling frequencies, feature selection and window size. The quantification estimates of locomotor play behaviour were highly correlated with the true prevalence (0.97; p < 0.001) with a total overestimation of 18.97%. This study is the first to implement machine learning approaches for multi-class behaviour identification as well as behaviour quantification in calves. This has potential to contribute towards new insights to evaluate the health and welfare in calves by use of wearable sensors.

scholarly journals Erratum to: Machine learning approaches and databases for prediction of drug–target interaction: a survey paper

2020 ◽  
Author(s):  
Maryam Bagherian ◽  
Elyas Sabeti ◽  
Kai Wang ◽  
Maureen A Sartor ◽  
Zaneta Nikolovska-Coleska ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Target ◽  
Learning Approaches ◽  
Target Interaction ◽  
Survey Paper

scholarly journals Bird Eye View of Protein Subcellular Localization Prediction

Life ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 347
Author(s):  
Ravindra Kumar ◽  
Sandeep Kumar Dhanda
Keyword(s):  
Subcellular Localization ◽  
Protein Localization ◽  
3D Structure ◽  
Computational Prediction ◽  
Learning Approaches ◽  
Protein Subcellular Localization ◽  
Comprehensive Listing ◽  
Protein Subcellular Localization Prediction ◽  
Structure And Functions ◽  
Localization Prediction

Proteins are made up of long chain of amino acids that perform a variety of functions in different organisms. The activity of the proteins is determined by the nucleotide sequence of their genes and by its 3D structure. In addition, it is essential for proteins to be destined to their specific locations or compartments to perform their structure and functions. The challenge of computational prediction of subcellular localization of proteins is addressed in various in silico methods. In this review, we reviewed the progress in this field and offered a bird eye view consisting of a comprehensive listing of tools, types of input features explored, machine learning approaches employed, and evaluation matrices applied. We hope the review will be useful for the researchers working in the field of protein localization predictions.

scholarly journals How and why actions are selected: action selection and the dark room problem

2016 ◽  
Vol 15 (1) ◽  
pp. 19-45
Author(s):  
Elmarie Venter
Keyword(s):  
Machine Learning ◽  
Brain Function ◽  
Prediction Error ◽  
Action Selection ◽  
Selection Problem ◽  
Predictive Processing ◽  
Learning Approaches ◽  
Living Organisms ◽  
Perception Action ◽  
Dark Room

Abstract In this paper, I examine an evolutionary approach to the action selection problem and illustrate how it helps raise an objection to the predictive processing account. Clark examines the predictive processing account as a theory of brain function that aims to unify perception, action, and cognition, but - despite this aim - fails to consider action selection overtly. He off ers an account of action control with the implication that minimizing prediction error is an imperative of living organisms because, according to the predictive processing account, action is employed to fulfill expectations and reduce prediction error. One way in which this can be achieved is by seeking out the least stimulating environment and staying there (Friston et al. 2012: 2). Bayesian, neuroscientific, and machine learning approaches into a single framework whose overarching principle is the minimization of surprise (or, equivalently, the maximization of expectation. But, most living organisms do not find, and stay in, surprise free environments. This paper explores this objection, also called the “dark room problem”, and examines Clark’s response to the problem. Finally, I recommend that if supplemented with an account of action selection, Clark’s account will avoid the dark room problem.

scholarly journals Machine learning approaches and databases for prediction of drug–target interaction: a survey paper

2020 ◽  
Author(s):  
Maryam Bagherian ◽  
Elyas Sabeti ◽  
Kai Wang ◽  
Maureen A Sartor ◽  
Zaneta Nikolovska-Coleska ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Target ◽  
Future Research ◽  
Learning Approaches ◽  
Research Directions ◽  
Target Interaction ◽  
Survey Paper ◽  
Advantages And Disadvantages ◽  
Efficient Prediction ◽  
Future Research Directions

Abstract The task of predicting the interactions between drugs and targets plays a key role in the process of drug discovery. There is a need to develop novel and efficient prediction approaches in order to avoid costly and laborious yet not-always-deterministic experiments to determine drug–target interactions (DTIs) by experiments alone. These approaches should be capable of identifying the potential DTIs in a timely manner. In this article, we describe the data required for the task of DTI prediction followed by a comprehensive catalog consisting of machine learning methods and databases, which have been proposed and utilized to predict DTIs. The advantages and disadvantages of each set of methods are also briefly discussed. Lastly, the challenges one may face in prediction of DTI using machine learning approaches are highlighted and we conclude by shedding some lights on important future research directions.

scholarly journals Machine Learning for Drug-Target Interaction Prediction

Molecules ◽  
2018 ◽  
Vol 23 (9) ◽  
pp. 2208 ◽  
Author(s):  
Ruolan Chen ◽  
Xiangrong Liu ◽  
Shuting Jin ◽  
Jiawei Lin ◽  
Juan Liu
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Drug Target ◽  
High Efficiency ◽  
Hierarchical Classification ◽  
Computational Prediction ◽  
Learning Approaches ◽  
Comprehensive Overview ◽  
Target Interaction

Identifying drug-target interactions will greatly narrow down the scope of search of candidate medications, and thus can serve as the vital first step in drug discovery. Considering that in vitro experiments are extremely costly and time-consuming, high efficiency computational prediction methods could serve as promising strategies for drug-target interaction (DTI) prediction. In this review, our goal is to focus on machine learning approaches and provide a comprehensive overview. First, we summarize a brief list of databases frequently used in drug discovery. Next, we adopt a hierarchical classification scheme and introduce several representative methods of each category, especially the recent state-of-the-art methods. In addition, we compare the advantages and limitations of methods in each category. Lastly, we discuss the remaining challenges and future outlook of machine learning in DTI prediction. This article may provide a reference and tutorial insights on machine learning-based DTI prediction for future researchers.

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