scholarly journals Solution of Levinthal’s Paradox and a Physical Theory of Protein Folding Times

Biomolecules ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 250
Author(s):  
Dmitry N. Ivankov ◽  
Alexei V. Finkelstein
Keyword(s):  
Machine Learning ◽  
Free Energy ◽  
Protein Folding ◽  
Physical Theory ◽  
State Of The Art ◽  
Energy Minimum ◽  
Folding Kinetics ◽  
Reasonable Time ◽  
Current State ◽  
Conceptual Aspect

“How do proteins fold?” Researchers have been studying different aspects of this question for more than 50 years. The most conceptual aspect of the problem is how protein can find the global free energy minimum in a biologically reasonable time, without exhaustive enumeration of all possible conformations, the so-called “Levinthal’s paradox.” Less conceptual but still critical are aspects about factors defining folding times of particular proteins and about perspectives of machine learning for their prediction. We will discuss in this review the key ideas and discoveries leading to the current understanding of folding kinetics, including the solution of Levinthal’s paradox, as well as the current state of the art in the prediction of protein folding times.

scholarly journals Evolutionary Machine Learning for Multi-Objective Class Solutions in Medical Deformable Image Registration

Algorithms ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 99 ◽  
Author(s):  
Kleopatra Pirpinia ◽  
Peter A. N. Bosman ◽  
Jan-Jakob Sonke ◽  
Marcel van Herk ◽  
Tanja Alderliesten
Keyword(s):  
Machine Learning ◽  
Image Registration ◽  
State Of The Art ◽  
Deformable Image Registration ◽  
Optimization Approach ◽  
High Quality ◽  
Trade Off ◽  
Multi Objective ◽  
Current State ◽  
Image Artefacts

Current state-of-the-art medical deformable image registration (DIR) methods optimize a weighted sum of key objectives of interest. Having a pre-determined weight combination that leads to high-quality results for any instance of a specific DIR problem (i.e., a class solution) would facilitate clinical application of DIR. However, such a combination can vary widely for each instance and is currently often manually determined. A multi-objective optimization approach for DIR removes the need for manual tuning, providing a set of high-quality trade-off solutions. Here, we investigate machine learning for a multi-objective class solution, i.e., not a single weight combination, but a set thereof, that, when used on any instance of a specific DIR problem, approximates such a set of trade-off solutions. To this end, we employed a multi-objective evolutionary algorithm to learn sets of weight combinations for three breast DIR problems of increasing difficulty: 10 prone-prone cases, 4 prone-supine cases with limited deformations and 6 prone-supine cases with larger deformations and image artefacts. Clinically-acceptable results were obtained for the first two problems. Therefore, for DIR problems with limited deformations, a multi-objective class solution can be machine learned and used to compute straightforwardly multiple high-quality DIR outcomes, potentially leading to more efficient use of DIR in clinical practice.

scholarly journals Detecting Emotions in English and Arabic Tweets

Information ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 98 ◽  
Author(s):  
Tariq Ahmad ◽  
Allan Ramsay ◽  
Hanady Ahmed
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Learning Algorithms ◽  
General Purpose ◽  
Machine Learning Algorithms ◽  
Current State ◽  
Optimal Thresholds ◽  
Alternative Approach

Assigning sentiment labels to documents is, at first sight, a standard multi-label classification task. Many approaches have been used for this task, but the current state-of-the-art solutions use deep neural networks (DNNs). As such, it seems likely that standard machine learning algorithms, such as these, will provide an effective approach. We describe an alternative approach, involving the use of probabilities to construct a weighted lexicon of sentiment terms, then modifying the lexicon and calculating optimal thresholds for each class. We show that this approach outperforms the use of DNNs and other standard algorithms. We believe that DNNs are not a universal panacea and that paying attention to the nature of the data that you are trying to learn from can be more important than trying out ever more powerful general purpose machine learning algorithms.

scholarly journals Ulixes: Facial Recognition Privacy with Adversarial Machine Learning

2021 ◽  
Vol 2022 (1) ◽  
pp. 148-165
Author(s):  
Thomas Cilloni ◽  
Wei Wang ◽  
Charles Walter ◽  
Charles Fleming
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Facial Recognition ◽  
Clustering Methods ◽  
Non Invasive ◽  
Current State ◽  
Social Media Platforms ◽  
Adversarial Examples ◽  
The Cost ◽  
Photo Management

Abstract Facial recognition tools are becoming exceptionally accurate in identifying people from images. However, this comes at the cost of privacy for users of online services with photo management (e.g. social media platforms). Particularly troubling is the ability to leverage unsupervised learning to recognize faces even when the user has not labeled their images. In this paper we propose Ulixes, a strategy to generate visually non-invasive facial noise masks that yield adversarial examples, preventing the formation of identifiable user clusters in the embedding space of facial encoders. This is applicable even when a user is unmasked and labeled images are available online. We demonstrate the effectiveness of Ulixes by showing that various classification and clustering methods cannot reliably label the adversarial examples we generate. We also study the effects of Ulixes in various black-box settings and compare it to the current state of the art in adversarial machine learning. Finally, we challenge the effectiveness of Ulixes against adversarially trained models and show that it is robust to countermeasures.

scholarly journals Rapid discovery of novel prophages using biological feature engineering and machine learning

2020 ◽  
Author(s):  
Kimmo Sirén ◽  
Andrew Millard ◽  
Bent Petersen ◽  
M Thomas P Gilbert ◽  
Martha RJ Clokie ◽  
...  
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Feature Space ◽  
Machine Learning Method ◽  
Biological Feature ◽  
Bacterial Genomes ◽  
Current State ◽  
Starting Point ◽  
Feature Based

ABSTRACTProphages are phages that are integrated into bacterial genomes and which are key to understanding many aspects of bacterial biology. Their extreme diversity means they are challenging to detect using sequence similarity, yet this remains the paradigm and thus many phages remain unidentified. We present a novel, fast and generalizing machine learning method based on feature space to facilitate novel prophage discovery. To validate the approach, we reanalyzed publicly available marine viromes and single-cell genomes using our feature-based approaches and found consistently more phages than were detected using current state-of-the-art tools while being notably faster. This demonstrates that our approach significantly enhances bacteriophage discovery and thus provides a new starting point for exploring new biologies.

Macromolecular conformations and interactions

2018 ◽  
Author(s):  
Dennis Sherwood ◽  
Paul Dalby
Keyword(s):  
Amino Acids ◽  
Free Energy ◽  
Protein Folding ◽  
Energy Minimum ◽  
Huge Number ◽  
Thermodynamics And Kinetics ◽  
Ligand Interactions ◽  
Kinetics Of ◽  
Single Configuration ◽  
Over Time

As a polymer of many amino acids, any given protein can, in principle, adopt a huge number of configurations. In reality, however, the biologically stable protein adopts a single configuration that is stable over time. Thermodynamically, this configuration must represent a Gibbs free energy minimum. This chapter therefore explores how the thermodynamics and kinetics of protein folding and unfolding can be investigated experimentally (using, for example, chaotropes, heating or ligand interactions), and how these measurements can be used to enrich our understanding of protein configurations and stability.

Automatic Detection of Emotion in Music

2009 ◽  
pp. 9-33 ◽  
Author(s):  
Cyril Laurier ◽  
Perfecto Herrera
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Automatic Detection ◽  
Machine Learning Method ◽  
Future Trends ◽  
Learning Method ◽  
Proof Of Concept ◽  
Current State ◽  
Emotions In Music

Creating emotionally sensitive machines will significantly enhance the interaction between humans and machines. In this chapter we focus on enabling this ability for music. Music is extremely powerful to induce emotions. If machines can somehow apprehend emotions in music, it gives them a relevant competence to communicate with humans. In this chapter we review the theories of music and emotions. We detail different representations of musical emotions from the literature, together with related musical features. Then, we focus on techniques to detect the emotion in music from audio content. As a proof of concept, we detail a machine learning method to build such a system. We also review the current state of the art results, provide evaluations and give some insights into the possible applications and future trends of these techniques.

scholarly journals Prediction of Peptide Vascularization Inhibitory Activity in Tumor Tissue as a Possible Target for Cancer Treatment

Proceedings ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 15
Author(s):  
Jose Liñares-Blanco ◽  
Carlos Fernandez-Lozano
Keyword(s):  
Machine Learning ◽  
Cancer Research ◽  
Cancer Treatment ◽  
Tumor Tissue ◽  
Molecular Descriptors ◽  
State Of The Art ◽  
Selection Process ◽  
Angiogenic Activity ◽  
Current State ◽  
Metabolic Activities

The prediction of metabolic activities in silico form is crucial to be able to address all research possibilities without exceeding the experimental costs. In particular, for cancer research, the prediction of certain activities can be of great help in the discovery of different treatments. In this work it has been proposed to predict, through Machine Learning, the anti-angiogenic activity of peptides is currently being used in cancer treatment and is giving hopeful results. From a list of peptide sequences, three types of molecular descriptors were obtained (AAC, DC and TC) that offered the possibility of training different ML algorithms. After a Feature Selection process, different models were obtained with a predictive value that surpassed the current state of the art. These results shown that ML is useful for the classification and prediction of the activity of new peptides, making experimental screening cheaper and faster.

scholarly journals Protein unfolding mechanisms and their effects on folding experiments

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1723 ◽  
Author(s):  
Lisa J Lapidus
Keyword(s):  
Free Energy ◽  
Protein Folding ◽  
Protein Unfolding ◽  
Energy Landscape ◽  
Free Energy Landscape ◽  
Folding Kinetics ◽  
Chemical Heat ◽  
Conformational Ensembles

In this review, I discuss the various methods researchers use to unfold proteins in the lab in order to understand protein folding both in vitro and in vivo. The four main techniques, chemical-, heat-, pressure- and force-denaturation, produce distinctly different unfolded conformational ensembles. Recent measurements have revealed different folding kinetics from different unfolding mechanisms. Thus, comparing these distinct unfolded ensembles sheds light on the underlying free energy landscape of folding.

scholarly journals Interpretable Machine Learning for Genomics

2021 ◽  
Author(s):  
David Watson
Keyword(s):  
Machine Learning ◽  
Cell Function ◽  
State Of The Art ◽  
Future Research ◽  
Computational Statistics ◽  
Close Collaboration ◽  
Current State ◽  
Interpretable Machine Learning ◽  
Underlying Mechanisms ◽  
Generation Sequencing

Abstract High-throughput technologies such as next generation sequencing allow biologists to observe cell function with unprecedented resolution, but the resulting datasets are too large and complicated for humans to understand without the aid of advanced statistical methods. Machine learning (ML) algorithms, which are designed to automatically find patterns in data, are well suited to this task. Yet these models are often so complex as to be opaque, leaving researchers with few clues about underlying mechanisms. Interpretable machine learning (iML) is a burgeoning subdiscipline of computational statistics devoted to making the predictions of ML models more intelligible to end users. This article is a gentle and critical introduction to iML, with an emphasis on genomic applications. I define relevant concepts, motivate leading methodologies, and provide a simple typology of existing approaches. I survey recent examples of iML in genomics, demonstrating how such techniques are increasingly integrated into research workflows. I argue that iML solutions are required to realize the promise of precision medicine. However, several open challenges remain. I examine the limitations of current state of the art tools and propose a number of directions for future research. While the horizon for iML in genomics is wide and bright, continued progress requires close collaboration across disciplines.

scholarly journals Artificial intelligence: From electronics to optics

Photoniques ◽  
2020 ◽  
pp. 49-52
Author(s):  
Sylvain Gigan ◽  
Florent Krzakala ◽  
Laurent Daudet ◽  
Igor Carron
Keyword(s):  
Machine Learning ◽  
High Speed ◽  
State Of The Art ◽  
Optical Computing ◽  
Way Of Life ◽  
Promising Alternative ◽  
Learning Tasks ◽  
Current State ◽  
Recent Emergence ◽  
Integrated Or

Machine Learning and big data are currently revolutionizing our way of life, in particular with the recent emergence of deep learning. Powered by CPU and GPU, they are currently hardware limited and extremely energy intensive. Photonics, either integrated or in free space, offers a very promising alternative for realizing optically machine learning tasks at high speed and low consumption. We here review the history and current state of the art of optical computing and optical machine learning.

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