Protein structure dynamic prediction: a Machine Learning/Molecular Dynamic approach to investigate the protein conformational sampling

Proteins exist in several different conformations. These structural changes are often associated with fluctuations at the residue level. Recent findings show that co-evolutionary analysis coupled with machine-learning techniques improves the precision by providing quantitative distance predictions between pairs of residues. The predicted statistical distance distribution from Multi Sequence Analysis (MSA) reveals the presence of different local maxima suggesting the flexibility of key residue pairs. Here we investigate the ability of the residue-residue distance prediction to provide insights into the protein conformational ensemble. We combine deep learning approaches with mechanistic modeling to a set of proteins that experimentally showed conformational changes. The predicted protein models were filtered based on energy scores, RMSD clustering, and the centroids selected as the lowest energy structure per cluster. The models were compared to the experimental-Molecular Dynamics (MD) relaxed structure by analyzing the backbone residue torsional distribution and the sidechain orientations. Our pipeline not only allows us to retrieve the global experimental folding but also the experimental structural dynamics. We show the potential correlation between the experimental structure dynamics and the predicted model ensemble demonstrating the susceptibility of the current state-of-the-art methods in protein folding and dynamics prediction and pointing out the areas of improvement.

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Protein Structure Dynamic Prediction: a Machine Learning/Molecular Dynamic Approach to Investigate the Protein Conformational Sampling

10.21203/rs.3.rs-1073712/v1 ◽

2021 ◽

Author(s):

Martina Audagnotto ◽

Werngard Czechtizky ◽

Leonardo De Maria ◽

Helena Käck ◽

Garegin Papoian ◽

...

Keyword(s):

Machine Learning ◽

Conformational Changes ◽

Structural Changes ◽

Mechanistic Modeling ◽

Machine Learning Techniques ◽

Energy Structure ◽

Conformational Sampling ◽

Conformational Ensemble ◽

Evolutionary Analysis ◽

Learning Approaches

Abstract Proteins exist in several different conformations. These structural changes are often associated with fluctuations at the residue level. Recent findings show that co-evolutionary analysis coupled with machine-learning techniques improves the precision by providing quantitative distance predictions between pairs of residues. The predicted statistical distance distribution from Multi Sequence Analysis (MSA) reveals the presence of different local maxima suggesting the flexibility of key residue pairs. Here we investigate the ability of the residue-residue distance prediction to provide insights into the protein conformational ensemble. We combine deep learning approaches with mechanistic modeling to a set of proteins that experimentally showed conformational changes. The predicted protein models were filtered based on energy scores, RMSD clustering, and the centroids selected as the lowest energy structure per cluster. The models were compared to the experimental-Molecular Dynamics (MD) relaxed structure by analyzing the backbone residue torsional distribution and the sidechain orientations. Our pipeline not only allows us to retrieve the global experimental folding but also the experimental structural dynamics. We show the potential correlation between the experimental structure dynamics and the predicted model ensemble demonstrating the susceptibility of the current state-of-the-art methods in protein folding and dynamics prediction and pointing out the areas of improvement.

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Automated computational analysis reveals structural changes in the enteric nervous system of nNOS deficient mice

Scientific Reports ◽

10.1038/s41598-021-96677-x ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Ben R. Cairns ◽

Benjamin Jevans ◽

Atchariya Chanpong ◽

Dale Moulding ◽

Conor J. McCann

Keyword(s):

Machine Learning ◽

Nervous System ◽

Enteric Nervous System ◽

Structural Changes ◽

Computational Analysis ◽

Confocal Imaging ◽

Learning Approaches ◽

Composition And Structure ◽

Oxide Synthase ◽

Computational Image Analysis

AbstractNeuronal nitric oxide synthase (nNOS) neurons play a fundamental role in inhibitory neurotransmission, within the enteric nervous system (ENS), and in the establishment of gut motility patterns. Clinically, loss or disruption of nNOS neurons has been shown in a range of enteric neuropathies. However, the effects of nNOS loss on the composition and structure of the ENS remain poorly understood. The aim of this study was to assess the structural and transcriptional consequences of loss of nNOS neurons within the murine ENS. Expression analysis demonstrated compensatory transcriptional upregulation of pan neuronal and inhibitory neuronal subtype targets within the Nos1−/− colon, compared to control C57BL/6J mice. Conventional confocal imaging; combined with novel machine learning approaches, and automated computational analysis, revealed increased interconnectivity within the Nos1−/− ENS, compared to age-matched control mice, with increases in network density, neural projections and neuronal branching. These findings provide the first direct evidence of structural and molecular remodelling of the ENS, upon loss of nNOS signalling. Further, we demonstrate the utility of machine learning approaches, and automated computational image analysis, in revealing previously undetected; yet potentially clinically relevant, changes in ENS structure which could provide improved understanding of pathological mechanisms across a host of enteric neuropathies.

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Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions

Electronics ◽

10.3390/electronics10212717 ◽

2021 ◽

Vol 10 (21) ◽

pp. 2717

Author(s):

Nusrat Rouf ◽

Majid Bashir Malik ◽

Tasleem Arif ◽

Sparsh Sharma ◽

Saurabh Singh ◽

...

Keyword(s):

Machine Learning ◽

Stock Market ◽

Digital Libraries ◽

Stock Price ◽

Ensemble Methods ◽

Machine Learning Techniques ◽

Learning Approaches ◽

Stock Market Prediction ◽

Research Areas ◽

Recent Developments

With the advent of technological marvels like global digitization, the prediction of the stock market has entered a technologically advanced era, revamping the old model of trading. With the ceaseless increase in market capitalization, stock trading has become a center of investment for many financial investors. Many analysts and researchers have developed tools and techniques that predict stock price movements and help investors in proper decision-making. Advanced trading models enable researchers to predict the market using non-traditional textual data from social platforms. The application of advanced machine learning approaches such as text data analytics and ensemble methods have greatly increased the prediction accuracies. Meanwhile, the analysis and prediction of stock markets continue to be one of the most challenging research areas due to dynamic, erratic, and chaotic data. This study explains the systematics of machine learning-based approaches for stock market prediction based on the deployment of a generic framework. Findings from the last decade (2011–2021) were critically analyzed, having been retrieved from online digital libraries and databases like ACM digital library and Scopus. Furthermore, an extensive comparative analysis was carried out to identify the direction of significance. The study would be helpful for emerging researchers to understand the basics and advancements of this emerging area, and thus carry-on further research in promising directions.

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Machine Learning Approaches to Traffic Accident Analysis and Hotspot Prediction

Computers ◽

10.3390/computers10120157 ◽

2021 ◽

Vol 10 (12) ◽

pp. 157

Author(s):

Daniel Santos ◽

José Saias ◽

Paulo Quaresma ◽

Vítor Beires Nogueira

Keyword(s):

Machine Learning ◽

Predictive Model ◽

Road Accident ◽

Influential Factors ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Economic Losses ◽

Learning Approaches ◽

Road Accidents ◽

Accident Data

Traffic accidents are one of the most important concerns of the world, since they result in numerous casualties, injuries, and fatalities each year, as well as significant economic losses. There are many factors that are responsible for causing road accidents. If these factors can be better understood and predicted, it might be possible to take measures to mitigate the damages and its severity. The purpose of this work is to identify these factors using accident data from 2016 to 2019 from the district of Setúbal, Portugal. This work aims at developing models that can select a set of influential factors that may be used to classify the severity of an accident, supporting an analysis on the accident data. In addition, this study also proposes a predictive model for future road accidents based on past data. Various machine learning approaches are used to create these models. Supervised machine learning methods such as decision trees (DT), random forests (RF), logistic regression (LR), and naive Bayes (NB) are used, as well as unsupervised machine learning techniques including DBSCAN and hierarchical clustering. Results show that a rule-based model using the C5.0 algorithm is capable of accurately detecting the most relevant factors describing a road accident severity. Further, the results of the predictive model suggests the RF model could be a useful tool for forecasting accident hotspots.

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IDS for Industrial Applications: A Federated Learning Approach with Active Personalization

Sensors ◽

10.3390/s21206743 ◽

2021 ◽

Vol 21 (20) ◽

pp. 6743

Author(s):

Vasiliki Kelli ◽

Vasileios Argyriou ◽

Thomas Lagkas ◽

George Fragulis ◽

Elisavet Grigoriou ◽

...

Keyword(s):

Machine Learning ◽

Active Learning ◽

Network Flow ◽

Detection System ◽

Human Life ◽

Industrial Sector ◽

Machine Learning Techniques ◽

Sensitive Information ◽

Learning Approaches ◽

Monitoring And Control

Internet of Things (IoT) is a concept adopted in nearly every aspect of human life, leading to an explosive utilization of intelligent devices. Notably, such solutions are especially integrated in the industrial sector, to allow the remote monitoring and control of critical infrastructure. Such global integration of IoT solutions has led to an expanded attack surface against IoT-enabled infrastructures. Artificial intelligence and machine learning have demonstrated their ability to resolve issues that would have been impossible or difficult to address otherwise; thus, such solutions are closely associated with securing IoT. Classical collaborative and distributed machine learning approaches are known to compromise sensitive information. In our paper, we demonstrate the creation of a network flow-based Intrusion Detection System (IDS) aiming to protecting critical infrastructures, stemming from the pairing of two machine learning techniques, namely, federated learning and active learning. The former is utilized for privately training models in federation, while the latter is a semi-supervised approach applied for global model adaptation to each of the participant’s traffic. Experimental results indicate that global models perform significantly better for each participant, when locally personalized with just a few active learning queries. Specifically, we demonstrate how the accuracy increase can reach 7.07% in only 10 queries.

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Machine Learning Frameworks in Cancer Detection

E3S Web of Conferences ◽

10.1051/e3sconf/202129701073 ◽

2021 ◽

Vol 297 ◽

pp. 01073

Author(s):

Sabyasachi Pramanik ◽

K. Martin Sagayam ◽

Om Prakash Jena

Keyword(s):

Machine Learning ◽

Prediction Models ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Cancer Development ◽

Support Vector ◽

Learning Approaches ◽

Learning Techniques ◽

Fact Finding ◽

Risk Of Cancer

Cancer has been described as a diverse illness with several distinct subtypes that may occur simultaneously. As a result, early detection and forecast of cancer types have graced essentially in cancer fact-finding methods since they may help to improve the clinical treatment of cancer survivors. The significance of categorizing cancer suffers into higher or lower-threat categories has prompted numerous fact-finding associates from the bioscience and genomics field to investigate the utilization of machine learning (ML) algorithms in cancer diagnosis and treatment. Because of this, these methods have been used with the goal of simulating the development and treatment of malignant diseases in humans. Furthermore, the capacity of machine learning techniques to identify important characteristics from complicated datasets demonstrates the significance of these technologies. These technologies include Bayesian networks and artificial neural networks, along with a number of other approaches. Decision Trees and Support Vector Machines which have already been extensively used in cancer research for the creation of predictive models, also lead to accurate decision making. The application of machine learning techniques may undoubtedly enhance our knowledge of cancer development; nevertheless, a sufficient degree of validation is required before these approaches can be considered for use in daily clinical practice. An overview of current machine learning approaches utilized in the simulation of cancer development is presented in this paper. All of the supervised machine learning approaches described here, along with a variety of input characteristics and data samples, are used to build the prediction models. In light of the increasing trend towards the use of machine learning methods in biomedical research, we offer the most current papers that have used these approaches to predict risk of cancer or patient outcomes in order to better understand cancer.

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Environmental Sound Recognition on Embedded Systems: From FPGAs to TPUs

Electronics ◽

10.3390/electronics10212622 ◽

2021 ◽

Vol 10 (21) ◽

pp. 2622

Author(s):

Jurgen Vandendriessche ◽

Nick Wouters ◽

Bruno da Silva ◽

Mimoun Lamrini ◽

Mohamed Yassin Chkouri ◽

...

Keyword(s):

Machine Learning ◽

High Performance ◽

Machine Learning Techniques ◽

Sound Recognition ◽

Learning Approaches ◽

Environmental Sound ◽

Embedded Devices ◽

Power Efficient ◽

Computationally Intensive ◽

Environmental Sound Recognition

In recent years, Environmental Sound Recognition (ESR) has become a relevant capability for urban monitoring applications. The techniques for automated sound recognition often rely on machine learning approaches, which have increased in complexity in order to achieve higher accuracy. Nonetheless, such machine learning techniques often have to be deployed on resource and power-constrained embedded devices, which has become a challenge with the adoption of deep learning approaches based on Convolutional Neural Networks (CNNs). Field-Programmable Gate Arrays (FPGAs) are power efficient and highly suitable for computationally intensive algorithms like CNNs. By fully exploiting their parallel nature, they have the potential to accelerate the inference time as compared to other embedded devices. Similarly, dedicated architectures to accelerate Artificial Intelligence (AI) such as Tensor Processing Units (TPUs) promise to deliver high accuracy while achieving high performance. In this work, we evaluate existing tool flows to deploy CNN models on FPGAs as well as on TPU platforms. We propose and adjust several CNN-based sound classifiers to be embedded on such hardware accelerators. The results demonstrate the maturity of the existing tools and how FPGAs can be exploited to outperform TPUs.

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Machine learning for metagenomics: methods and tools

Metagenomics ◽

10.1515/metgen-2016-0001 ◽

2017 ◽

Vol 1 (1) ◽

Cited By ~ 15

Author(s):

Hayssam Soueidan ◽

Macha Nikolski

Keyword(s):

Machine Learning ◽

Gene Prediction ◽

Full Range ◽

Machine Learning Techniques ◽

Learning Approaches ◽

Unified Framework ◽

Comparative Metagenomics ◽

Learning Techniques ◽

Ngs Data ◽

Modern Machine

AbstractOwing to the complexity and variability of metagenomic studies, modern machine learning approaches have seen increased usage to answer a variety of question encompassing the full range of metagenomic NGS data analysis.We review here the contribution of machine learning techniques for the field of metagenomics, by presenting known successful approaches in a unified framework. This review focuses on five important metagenomic problems:OTU-clustering, binning, taxonomic proffiing and assignment, comparative metagenomics and gene prediction. For each of these problems, we identify the most prominent methods, summarize the machine learning approaches used and put them into perspective of similar methods.We conclude our review looking further ahead at the challenge posed by the analysis of interactions within microbial communities and different environments, in a field one could call “integrative metagenomics”.

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Machine Learning Approaches to Retrieve High-Quality, Clinically Relevant, Evidence from the Biomedical Literature: A Systematic Review (Preprint)

10.2196/preprints.30401 ◽

2021 ◽

Author(s):

Wael Abdelkader ◽

Tamara Navarro ◽

Rick Parrish ◽

Chris Cotoi ◽

Federico Germini ◽

...

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Strong Evidence ◽

Clinical Care ◽

Biomedical Literature ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Learning Approaches ◽

High Quality ◽

Applied Machine Learning

BACKGROUND The rapid growth of the biomedical literature makes identifying strong evidence a time-consuming task. Applying machine learning to the process could be a viable solution that limits effort while maintaining accuracy. OBJECTIVE To summarize the nature and comparative performance of machine learning approaches that have been applied to retrieve high-quality evidence for clinical consideration from the biomedical literature. METHODS We conducted a systematic review of studies that applied machine learning techniques to identify high-quality clinical articles in the biomedical literature. Multiple databases were searched to July 2020. Extracted data focused on the applied machine learning model, steps in the development of the models, and model performance. RESULTS From 3918 retrieved studies, 10 met our inclusion criteria. All followed a supervised machine learning approach and applied, from a limited range of options, a high-quality standard for the training of their model. The results show that machine learning can achieve a sensitivity of 95% while maintaining a high precision of 86%. CONCLUSIONS Applying machine learning to distinguish studies with strong evidence for clinical care has the potential to decrease the workload of manually identifying these. The evidence base is active and evolving. Reported methods were variable across the studies but focused on supervised machine learning approaches. Performance may improve by applying more sophisticated approaches such as active learning, auto-machine learning, and unsupervised machine learning approaches.

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Analysis of Kinase Inhibitors and Druggability of Kinase-Targets Using Machine Learning Techniques

Pattern Discovery Using Sequence Data Mining ◽

10.4018/978-1-61350-056-9.ch009 ◽

2012 ◽

pp. 155-165

Author(s):

S. Prasanthi ◽

S.Durga Bhavani ◽

T. Sobha Rani ◽

Raju S. Bapi

Keyword(s):

Machine Learning ◽

Decision Tree ◽

Kinase Inhibitors ◽

Kinase Inhibitor ◽

Classification Problem ◽

Machine Learning Techniques ◽

Learning Approaches ◽

Decision Tree Classifier ◽

Data Set ◽

Learning Techniques

Vast majority of successful drugs or inhibitors achieve their activity by binding to, and modifying the activity of a protein leading to the concept of druggability. A target protein is druggable if it has the potential to bind the drug-like molecules. Hence kinase inhibitors need to be studied to understand the specificity of a kinase inhibitor in choosing a particular kinase target. In this paper we focus on human kinase drug target sequences since kinases are known to be potential drug targets. Also we do a preliminary analysis of kinase inhibitors in order to study the problem in the protein-ligand space in future. The identification of druggable kinases is treated as a classification problem in which druggable kinases are taken as positive data set and non-druggable kinases are chosen as negative data set. The classification problem is addressed using machine learning techniques like support vector machine (SVM) and decision tree (DT) and using sequence-specific features. One of the challenges of this classification problem is due to the unbalanced data with only 48 druggable kinases available against 509 non-drugggable kinases present at Uniprot. The accuracy of the decision tree classifier obtained is 57.65 which is not satisfactory. A two-tier architecture of decision trees is carefully designed such that recognition on the non-druggable dataset also gets improved. Thus the overall model is shown to achieve a final performance accuracy of 88.37. To the best of our knowledge, kinase druggability prediction using machine learning approaches has not been reported in literature.

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