scholarly journals Protein sequence design with a learned potential

2020 ◽  
Author(s):  
Namrata Anand-Achim ◽  
Raphael R. Eguchi ◽  
Alexander Derry ◽  
Russ B. Altman ◽  
Po-Ssu Huang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Amino Acid ◽  
Protein Design ◽  
Protein Sequence ◽  
State Of The Art ◽  
The State ◽  
Energy Functions ◽  
Sequence Design

AbstractThe primary challenge of fixed-backbone protein design is to find a distribution of sequences that fold to the backbone of interest. This task is central to nearly all protein engineering problems, as achieving a particular backbone conformation is often a prerequisite for hosting specific functions. In this study, we investigate the capability of a deep neural network to learn the requisite patterns needed to design sequences. The trained model serves as a potential function defined over the space of amino acid identities and rotamer states, conditioned on the local chemical environment at each residue. While most deep learning based methods for sequence design only produce amino acid sequences, our method generates full-atom structural models, which can be evaluated using established sequence quality metrics. Under these metrics we are able to produce realistic and variable designs with quality comparable to the state-of-the-art. Additionally, we experimentally test designs for a de novo TIM-barrel structure and find designs that fold, demonstrating the algorithm’s generalizability to novel structures. Overall, our results demonstrate that a deep learning model can match state-of-the-art energy functions for guiding protein design.SignificanceProtein design tasks typically depend on carefully modeled and parameterized heuristic energy functions. In this study, we propose a novel machine learning method for fixed-backbone protein sequence design, using a learned neural network potential to not only design the sequence of amino acids but also select their side-chain configurations, or rotamers. Factoring through a structural representation of the protein, the network generates designs on par with the state-of-the-art, despite having been entirely learned from data. These results indicate an exciting future for protein design driven by machine learning.

scholarly journals Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes (Preprint)

2018 ◽  
Author(s):  
Ashenafi Zebene Woldaregay ◽  
Eirik Årsand ◽  
Taxiarchis Botsis ◽  
David Albers ◽  
Lena Mamykina ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Type 1 Diabetes ◽  
Blood Glucose ◽  
State Of The Art ◽  
The State ◽  
Self Management ◽  
Machine Learning Applications ◽  
Anomaly Classification

BACKGROUND Diabetes mellitus is a chronic metabolic disorder that results in abnormal blood glucose (BG) regulations. The BG level is preferably maintained close to normality through self-management practices, which involves actively tracking BG levels and taking proper actions including adjusting diet and insulin medications. BG anomalies could be defined as any undesirable reading because of either a precisely known reason (normal cause variation) or an unknown reason (special cause variation) to the patient. Recently, machine-learning applications have been widely introduced within diabetes research in general and BG anomaly detection in particular. However, irrespective of their expanding and increasing popularity, there is a lack of up-to-date reviews that materialize the current trends in modeling options and strategies for BG anomaly classification and detection in people with diabetes. OBJECTIVE This review aimed to identify, assess, and analyze the state-of-the-art machine-learning strategies and their hybrid systems focusing on BG anomaly classification and detection including glycemic variability (GV), hyperglycemia, and hypoglycemia in type 1 diabetes within the context of personalized decision support systems and BG alarm events applications, which are important constituents for optimal diabetes self-management. METHODS A rigorous literature search was conducted between September 1 and October 1, 2017, and October 15 and November 5, 2018, through various Web-based databases. Peer-reviewed journals and articles were considered. Information from the selected literature was extracted based on predefined categories, which were based on previous research and further elaborated through brainstorming. RESULTS The initial results were vetted using the title, abstract, and keywords and retrieved 496 papers. After a thorough assessment and screening, 47 articles remained, which were critically analyzed. The interrater agreement was measured using a Cohen kappa test, and disagreements were resolved through discussion. The state-of-the-art classes of machine learning have been developed and tested up to the task and achieved promising performance including artificial neural network, support vector machine, decision tree, genetic algorithm, Gaussian process regression, Bayesian neural network, deep belief network, and others. CONCLUSIONS Despite the complexity of BG dynamics, there are many attempts to capture hypoglycemia and hyperglycemia incidences and the extent of an individual’s GV using different approaches. Recently, the advancement of diabetes technologies and continuous accumulation of self-collected health data have paved the way for popularity of machine learning in these tasks. According to the review, most of the identified studies used a theoretical threshold, which suffers from inter- and intrapatient variation. Therefore, future studies should consider the difference among patients and also track its temporal change over time. Moreover, studies should also give more emphasis on the types of inputs used and their associated time lag. Generally, we foresee that these developments might encourage researchers to further develop and test these systems on a large-scale basis.

scholarly journals EvoEF2: accurate and fast energy function for computational protein design

2019 ◽  
Vol 36 (4) ◽  
pp. 1135-1142 ◽  
Author(s):  
Xiaoqiang Huang ◽  
Robin Pearce ◽  
Yang Zhang
Keyword(s):  
Protein Design ◽  
Energy Function ◽  
Interaction Design ◽  
Protein Sequence ◽  
De Novo ◽  
Supplementary Information ◽  
Energy Functions ◽  
Sequence Design ◽  
Physical Energy ◽  
Benchmark Datasets

Abstract Motivation The accuracy and success rate of de novo protein design remain limited, mainly due to the parameter over-fitting of current energy functions and their inability to discriminate incorrect designs from correct designs. Results We developed an extended energy function, EvoEF2, for efficient de novo protein sequence design, based on a previously proposed physical energy function, EvoEF. Remarkably, EvoEF2 recovered 32.5%, 47.9% and 22.3% of all, core and surface residues for 148 test monomers, and was generally applicable to protein–protein interaction design, as it recapitulated 30.9%, 42.4%, 31.3% and 21.4% of all, core, interface and surface residues for 88 test dimers, significantly outperforming EvoEF on the native sequence recapitulation. We further used I-TASSER to evaluate the foldability of the 148 designed monomer sequences, where all of them were predicted to fold into structures with high fold- and atomic-level similarity to their corresponding native structures, as demonstrated by the fact that 87.8% of the predicted structures shared a root-mean-square-deviation less than 2 Å to their native counterparts. The study also demonstrated that the usefulness of physical energy functions is highly correlated with the parameter optimization processes, and EvoEF2, with parameters optimized using sequence recapitulation, is more suitable for computational protein sequence design than EvoEF, which was optimized on thermodynamic mutation data. Availability and implementation The source code of EvoEF2 and the benchmark datasets are freely available at https://zhanglab.ccmb.med.umich.edu/EvoEF. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals A State-of-the-Art Survey on Deep Learning Theory and Architectures

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 292 ◽  
Author(s):  
Md Zahangir Alom ◽  
Tarek M. Taha ◽  
Chris Yakopcic ◽  
Stefan Westberg ◽  
Paheding Sidike ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Reinforcement Learning ◽  
Language Processing ◽  
Large Scale ◽  
Medical Information ◽  
State Of The Art ◽  
Generative Models ◽  
Learning Approaches

In recent years, deep learning has garnered tremendous success in a variety of application domains. This new field of machine learning has been growing rapidly and has been applied to most traditional application domains, as well as some new areas that present more opportunities. Different methods have been proposed based on different categories of learning, including supervised, semi-supervised, and un-supervised learning. Experimental results show state-of-the-art performance using deep learning when compared to traditional machine learning approaches in the fields of image processing, computer vision, speech recognition, machine translation, art, medical imaging, medical information processing, robotics and control, bioinformatics, natural language processing, cybersecurity, and many others. This survey presents a brief survey on the advances that have occurred in the area of Deep Learning (DL), starting with the Deep Neural Network (DNN). The survey goes on to cover Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL). Additionally, we have discussed recent developments, such as advanced variant DL techniques based on these DL approaches. This work considers most of the papers published after 2012 from when the history of deep learning began. Furthermore, DL approaches that have been explored and evaluated in different application domains are also included in this survey. We also included recently developed frameworks, SDKs, and benchmark datasets that are used for implementing and evaluating deep learning approaches. There are some surveys that have been published on DL using neural networks and a survey on Reinforcement Learning (RL). However, those papers have not discussed individual advanced techniques for training large-scale deep learning models and the recently developed method of generative models.

scholarly journals A Recurrent Model for Collective Entity Linking with Adaptive Features

2020 ◽  
Vol 34 (01) ◽  
pp. 329-336
Author(s):  
Xiaoling Zhou ◽  
Yukai Miao ◽  
Wei Wang ◽  
Jianbin Qin
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
State Of The Art ◽  
Learning To Rank ◽  
Parameter Tuning ◽  
Knowledge Bases ◽  
The State ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Free Text

The vast amount of web data enables us to build knowledge bases with unprecedented quality and coverage. Named Entity Disambiguation (NED) is an important task that automatically resolves ambiguous mentions in free text to correct target entries in the knowledge base. Traditional machine learning based methods for NED were outperformed and made obsolete by the state-of-the-art deep learning based models. However, deep learning models are more complex, requiring large amount of training data and lengthy training and parameter tuning time. In this paper, we revisit traditional machine learning techniques and propose a light-weight, tuneable and time-efficient method without using deep learning or deep learning generated features. We propose novel adaptive features that focus on extracting discriminative features to better model similarities between candidate entities and the mention's context. We learn a local ranking model based on traditional and the new adaptive features based on the learning-to-rank framework. While arriving at linking decisions individually via the local model, our method also takes into consideration the correlation between decisions by running multiple recurrent global models, which can be deemed as a learned local search method. Our method attains performances comparable to the state-of-the-art deep learning-based methods on NED benchmark datasets while being significantly faster to train.

scholarly journals p1-FP: Extraction, Classification, and Prediction of Website Fingerprints with Deep Learning

2019 ◽  
Vol 2019 (3) ◽  
pp. 191-209 ◽  
Author(s):  
Se Eun Oh ◽  
Saikrishna Sunkam ◽  
Nicholas Hopper
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Deep Neural Network ◽  
State Of The Art ◽  
Machine Learning Techniques ◽  
Feature Engineering ◽  
Engineering Process ◽  
Learning Techniques ◽  
Wide Range

Abstract Recent advances in Deep Neural Network (DNN) architectures have received a great deal of attention due to their ability to outperform state-of-the-art machine learning techniques across a wide range of application, as well as automating the feature engineering process. In this paper, we broadly study the applicability of deep learning to website fingerprinting. First, we show that unsupervised DNNs can generate lowdimensional informative features that improve the performance of state-of-the-art website fingerprinting attacks. Second, when used as classifiers, we show that they can exceed performance of existing attacks across a range of application scenarios, including fingerprinting Tor website traces, fingerprinting search engine queries over Tor, defeating fingerprinting defenses, and fingerprinting TLS-encrypted websites. Finally, we investigate which site-level features of a website influence its fingerprintability by DNNs.

scholarly journals Tinjauan Pustaka Sistematis: Implementasi Metode Deep Learning pada Prediksi Kinerja Murid

2021 ◽  
Vol 10 (2) ◽  
pp. 131-138
Author(s):  
Muhammad Haris Diponegoro ◽  
Sri Suning Kusumawardani ◽  
Indriana Hidayah
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Data Mining ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
State Of The Art ◽  
Educational Data Mining ◽  
Short Term ◽  
Long Short Term Memory

Pemanfaatan machine learning yang merupakan salah satu implementasi dalam bidang artificial intelligence telah merambah ke berbagai bidang, salah satunya adalah bidang pendidikan. Dengan menggunakan kombinasi teknik machine learning, statistik, dan basis data, dapat dilakukan educational data mining untuk mengetahui pola yang ada dalam suatu dataset tertentu. Salah satu penggunaan educational data mining adalah untuk melakukan prediksi kinerja murid. Hasil dari prediksi kinerja murid dapat digunakan sebagai salah satu instrumen untuk melakukan monitoring dan evaluasi terhadap proses pembelajaran sehingga dapat membantu menentukan langkah-langkah lanjutan dalam rangka meningkatkan proses pembelajaran. Makalah ini bertujuan untuk mengetahui state of the art implementasi deep learning yang merupakan bagian dari machine learning pada konteks educational data mining, khususnya mengenai prediksi kinerja murid. Pada makalah ini disajikan systematic literature review untuk mengetahui variasi teknik atau algoritme deep learning yang digunakan beserta kinerja yang dicapai. Dari dua puluh publikasi ilmiah yang ditelusuri, rata-rata kinerja yang dicapai dalam melakukan prediksi adalah 89,85%. Mayoritas teknik yang digunakan adalah Deep Neural Network (DNN), Recurrent Neural Network (RNN), dan Long Short-Term Memory (LSTM) dengan fitur data demografis, perilaku, dan akademis.

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

scholarly journals Representation Learning for Fine-Grained Change Detection

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4486
Author(s):  
Niall O’Mahony ◽  
Sean Campbell ◽  
Lenka Krpalkova ◽  
Anderson Carvalho ◽  
Joseph Walsh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Model Calibration ◽  
State Of The Art ◽  
Representation Learning ◽  
Machine Intelligence ◽  
The State ◽  
Sensor Data ◽  
Fine Grained ◽  
Learning Techniques

Fine-grained change detection in sensor data is very challenging for artificial intelligence though it is critically important in practice. It is the process of identifying differences in the state of an object or phenomenon where the differences are class-specific and are difficult to generalise. As a result, many recent technologies that leverage big data and deep learning struggle with this task. This review focuses on the state-of-the-art methods, applications, and challenges of representation learning for fine-grained change detection. Our research focuses on methods of harnessing the latent metric space of representation learning techniques as an interim output for hybrid human-machine intelligence. We review methods for transforming and projecting embedding space such that significant changes can be communicated more effectively and a more comprehensive interpretation of underlying relationships in sensor data is facilitated. We conduct this research in our work towards developing a method for aligning the axes of latent embedding space with meaningful real-world metrics so that the reasoning behind the detection of change in relation to past observations may be revealed and adjusted. This is an important topic in many fields concerned with producing more meaningful and explainable outputs from deep learning and also for providing means for knowledge injection and model calibration in order to maintain user confidence.

Sign in / Sign up

Export Citation Format

Share Document