scholarly journals KSIMC: Predicting Kinase–Substrate Interactions Based on Matrix Completion

2019 ◽  
Vol 20 (2) ◽  
pp. 302 ◽  
Author(s):  
Jingzhong Gan ◽  
Jie Qiu ◽  
Canshang Deng ◽  
Wei Lan ◽  
Qingfeng Chen ◽  
...  
Keyword(s):  
Scientific Literature ◽  
State Of The Art ◽  
Matrix Completion ◽  
Computational Approach ◽  
Computational Method ◽  
Substrate Interactions ◽  
Kinase Substrate ◽  
Cellular Processes ◽  
Substrate Interaction ◽  
The Matrix

Protein phosphorylation is an important chemical modification catalyzed by kinases. It plays important roles in many cellular processes. Predicting kinase–substrate interactions is vital to understanding the mechanism of many diseases. Many computational methods have been proposed to identify kinase–substrate interactions. However, the prediction accuracy still needs to be improved. Therefore, it is necessary to develop an efficient computational method to predict kinase–substrate interactions. In this paper, we propose a novel computational approach, KSIMC, to identify kinase–substrate interactions based on matrix completion. Firstly, the kinase similarity and substrate similarity are calculated by aligning sequence of kinase–kinase and substrate–substrate, respectively. Then, the original association network is adjusted based on the similarities. Finally, the matrix completion is used to predict potential kinase–substrate interactions. The experiment results show that our method outperforms other state-of-the-art algorithms in performance. Furthermore, the relevant databases and scientific literature verify the effectiveness of our algorithm for new kinase–substrate interaction identification.

scholarly journals Toward Preparing a Knowledge Base to Explore Potential Drugs and Biomedical Entities Related to COVID-19: Automated Computational Approach (Preprint)

2020 ◽  
Author(s):  
Junaed Younus Khan ◽  
Md Tawkat Islam Khondaker ◽  
Iram Tazim Hoque ◽  
Hamada R H Al-Absi ◽  
Mohammad Saifur Rahman ◽  
...  
Keyword(s):  
Language Processing ◽  
Scientific Literature ◽  
Treatment Plan ◽  
Scientific Evidence ◽  
Clinical Status ◽  
Confidence Score ◽  
Computational Approach ◽  
Computational Method ◽  
Literature Mining ◽  
Novel Approach

BACKGROUND Novel coronavirus disease 2019 (COVID-19) is taking a huge toll on public health. Along with the non-therapeutic preventive measurements, scientific efforts are currently focused, mainly, on the development of vaccines and pharmacological treatment with existing drugs. Summarizing evidences from scientific literatures on the discovery of treatment plan of COVID-19 under a platform would help the scientific community to explore the opportunities in a systematic fashion. OBJECTIVE The aim of this study is to explore the potential drugs and biomedical entities related to coronavirus related diseases, including COVID-19, that are mentioned on scientific literature through an automated computational approach. METHODS We mined the information from publicly available scientific literature and related public resources. Six topic-specific dictionaries, including human genes, human miRNAs, diseases, Protein Databank, drugs, and drug side effects, were integrated to mine all scientific evidence related to COVID-19. We employed an automated literature mining and labeling system through a novel approach to measure the effectiveness of drugs against diseases based on natural language processing, sentiment analysis, and deep learning. We also applied the concept of cosine similarity to confidently infer the associations between diseases and genes. RESULTS Based on the literature mining, we identified 1805 diseases, 2454 drugs, 1910 genes that are related to coronavirus related diseases including COVID-19. Integrating the extracted information, we developed the first knowledgebase platform dedicated to COVID-19, which highlights potential list of drugs and related biomedical entities. For COVID-19, we highlighted multiple case studies on existing drugs along with a confidence score for their applicability in the treatment plan. Based on our computational method, we found Remdesivir, Statins, Dexamethasone, and Ivermectin could be considered as potential effective drugs to improve clinical status and lower mortality in patients hospitalized with COVID-19. We also found that Hydroxychloroquine could not be considered as an effective drug for COVID-19. The resulting knowledgebase is made available as an open source tool, named COVID-19Base. CONCLUSIONS Proper investigation of the mined biomedical entities along with the identified interactions among those would help the research community to discover possible ways for the therapeutic treatment of COVID-19.

scholarly journals Toward Preparing a Knowledge Base to Explore Potential Drugs and Biomedical Entities Related to COVID-19: Automated Computational Approach

10.2196/21648 ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. e21648
Author(s):  
Junaed Younus Khan ◽  
Md Tawkat Islam Khondaker ◽  
Iram Tazim Hoque ◽  
Hamada R H Al-Absi ◽  
Mohammad Saifur Rahman ◽  
...  
Keyword(s):  
Language Processing ◽  
Scientific Literature ◽  
Treatment Plan ◽  
Scientific Evidence ◽  
Clinical Status ◽  
Confidence Score ◽  
Computational Approach ◽  
Computational Method ◽  
Literature Mining ◽  
Novel Approach

Background Novel coronavirus disease 2019 (COVID-19) is taking a huge toll on public health. Along with the non-therapeutic preventive measurements, scientific efforts are currently focused, mainly, on the development of vaccines and pharmacological treatment with existing drugs. Summarizing evidences from scientific literatures on the discovery of treatment plan of COVID-19 under a platform would help the scientific community to explore the opportunities in a systematic fashion. Objective The aim of this study is to explore the potential drugs and biomedical entities related to coronavirus related diseases, including COVID-19, that are mentioned on scientific literature through an automated computational approach. Methods We mined the information from publicly available scientific literature and related public resources. Six topic-specific dictionaries, including human genes, human miRNAs, diseases, Protein Databank, drugs, and drug side effects, were integrated to mine all scientific evidence related to COVID-19. We employed an automated literature mining and labeling system through a novel approach to measure the effectiveness of drugs against diseases based on natural language processing, sentiment analysis, and deep learning. We also applied the concept of cosine similarity to confidently infer the associations between diseases and genes. Results Based on the literature mining, we identified 1805 diseases, 2454 drugs, 1910 genes that are related to coronavirus related diseases including COVID-19. Integrating the extracted information, we developed the first knowledgebase platform dedicated to COVID-19, which highlights potential list of drugs and related biomedical entities. For COVID-19, we highlighted multiple case studies on existing drugs along with a confidence score for their applicability in the treatment plan. Based on our computational method, we found Remdesivir, Statins, Dexamethasone, and Ivermectin could be considered as potential effective drugs to improve clinical status and lower mortality in patients hospitalized with COVID-19. We also found that Hydroxychloroquine could not be considered as an effective drug for COVID-19. The resulting knowledgebase is made available as an open source tool, named COVID-19Base. Conclusions Proper investigation of the mined biomedical entities along with the identified interactions among those would help the research community to discover possible ways for the therapeutic treatment of COVID-19.

scholarly journals Enhancing Matrix Completion Using a Modified Second-Order Total Variation

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Wendong Wang ◽  
Jianjun Wang
Keyword(s):  
Total Variation ◽  
Optimization Problem ◽  
State Of The Art ◽  
Matrix Completion ◽  
Second Order ◽  
Low Rank ◽  
Superior Performance ◽  
Completion Problem ◽  
Matrix Completion Problem ◽  
The Matrix

In this paper, we propose a new method to deal with the matrix completion problem. Different from most existing matrix completion methods that only pursue the low rank of underlying matrices, the proposed method simultaneously optimizes their low rank and smoothness such that they mutually help each other and hence yield a better performance. In particular, the proposed method becomes very competitive with the introduction of a modified second-order total variation, even when it is compared with some recently emerged matrix completion methods that also combine the low rank and smoothness priors of matrices together. An efficient algorithm is developed to solve the induced optimization problem. The extensive experiments further confirm the superior performance of the proposed method over many state-of-the-art methods.

scholarly journals Matrix Completion of World Trade

2021 ◽  
Author(s):  
Giorgio Gnecco ◽  
Federico Nutarelli ◽  
Massimo Riccaboni
Keyword(s):  
State Of The Art ◽  
Symmetric Matrix ◽  
Matrix Completion ◽  
False Positive Rate ◽  
Relative Advantage ◽  
Binary Classifier ◽  
Economic Complexity ◽  
Machine Learning Methods ◽  
The Matrix ◽  
Positive Rate

Abstract This work applies Matrix Completion (MC) – a class of machine-learning methods commonly used in the context of recommendation systems – to analyze economic complexity. MC is applied to reconstruct the Revealed Comparative Advantage (RCA) matrix, whose elements express the relative advantage of countries in given classes of products, as evidenced by yearly trade flows. A high-accuracy binary classifier is derived from the MC application, with the aim of discriminating between elements of the RCA matrix that are, respectively, higher/lower than one. We introduce a novel Matrix cOmpletion iNdex of Economic complexitY (MONEY) based on MC, and related to the degree of predictability of the RCA entries of different countries (the lower the predictability, the higher the complexity). Differently from previously-developed economic complexity indices, MONEY takes into account several singular vectors of the matrix reconstructed by MC, whereas other indices are based only on one/two eigenvectors of a suitable symmetric matrix, derived from the RCA matrix. Finally, MC is compared with a state-of-the-art economic complexity index (GENEPY), showing that the false positive rate per country of a binary classifier constructed starting from the average entry-wise output of MC is a proxy of GENEPY.

scholarly journals ksrMKL: a novel method for identification of kinase–substrate relationships using multiple kernel learning

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e4182 ◽  
Author(s):  
Minghui Wang ◽  
Tao Wang ◽  
Ao Li
Keyword(s):  
Multiple Kernel Learning ◽  
Computational Method ◽  
Kernel Learning ◽  
Support Vector ◽  
Kinase Substrate ◽  
Cellular Processes ◽  
Multiple Kernel ◽  
Kernel Support Vector Machine ◽  
Independent Test Dataset ◽  
Novel Method

Phosphorylation exerts a crucial role in multiple biological cellular processes which is catalyzed by protein kinases and closely related to many diseases. Identification of kinase–substrate relationships is important for understanding phosphorylation and provides a fundamental basis for further disease-related research and drug design. In this study, we develop a novel computational method to identify kinase–substrate relationships based on multiple kernel learning. The comparative analysis is based on a 10-fold cross-validation process and the dataset collected from the Phospho.ELM database. The results show that ksrMKL is greatly improved in various measures when compared with the single kernel support vector machine. Furthermore, with an independent test dataset extracted from the PhosphoSitePlus database, we compare ksrMKL with two existing kinase–substrate relationship prediction tools, namely iGPS and PKIS. The experimental results show that ksrMKL has better prediction performance than these existing tools.

PLANEJAMENTO FAMILIAR COMO ASSUNTO DE MULHER!? PERFIL DE GÊNERO NA PRODUÇÃO CIENTÍFICA NO BRASIL

2019 ◽  
Vol 8 (1) ◽  
pp. 221-235 ◽  
Author(s):  
Daniella De Paula Chiesa ◽  
Mário Antônio Sanches ◽  
Daiane Priscila Simão-Silva
Keyword(s):  
Family Planning ◽  
Scientific Literature ◽  
State Of The Art ◽  
Health Sciences ◽  
Scientific Production ◽  
The State ◽  
Review Of The Literature ◽  
The Subject

O estudo do Planejamento familiar, no contexto da bioética, abre-se para diversas perspectivas, entre elas a valorização dos seus diferentes atores. Situado neste contexto o artigo tem como objetivo identificar o perfil de gênero na produção científica sobre Planejamento Familiar no Brasil, entre 2000 e 2014, assim como a área de formação e especialização dos autores. Foram utilizadas metodologias que permitiram mapear o estado da arte do tema estudado, a partir de uma revisão da literatura. O resultado da pesquisa identifica que a produção científica sobre Planejamento Familiar no Brasil se compõe de perfil destacadamente feminino (71,76%). Dos 73 artigos analisados, 42 (57,53%) o foco do tema está direcionado à mulher assim como evidencia-se a área de ciências da saúde com maior concentração das publicações do tema.  Este aspecto da pesquisa abre para uma realidade complexa onde se buscam criticamente as razões para a pesquisa em Planejamento Familiar ter ênfase na mulher e ser um tema de relevância nas ciências da saúde.Palavras-chave: Produção científica, Planejamento Familiar, Gênero.  ABSTRACT: The study of Family Planning, in the context of bioethics, opens to diverse perspectives, among them the appreciation of their different agents. Situated in this context the article aims to identify the profile of gender in scientific literature on Family Planning in Brazil, between 2000 and 2014, as well as the area of training and specialization of the authors. Methodologies were used which allowed to map the State of the art of the subject studied, from a review of the literature. The results found identify that the scientific production on Family Planning in Brazil is formed with a outstandingly female profile (71,76%). Of the 73 articles examined, 42 (57.53%) the focus of the topic is directed to women as well as showing the health sciences area with highest concentration of publications. This aspect of the research opens to a complex reality where we seek critically the reasons for Research in Family Planning have emphasis on woman and be a topic of relevance in health sciences.Keywords: Scientific Production, Family Planning, Gender.

Publication of a scientific article in for an english-language journal. Part 2

2019 ◽  
Vol 23 (5) ◽  
pp. 116-121
Author(s):  
A. I. Nevorotin ◽  
I. V. Awsiewitsch ◽  
I. M. Sukhanov
Keyword(s):  
Detailed Analysis ◽  
English Language ◽  
Scientific Literature ◽  
Scientific Article ◽  
Scientific Journals ◽  
English Language Journal ◽  
The Matrix ◽  
Syntactic Relations

This article is the continuation of analysis and discussion from the book by Professor AI Nevorotin "Matrix phraseological collection: a manual for writing a scientific article in English". The Matrix phraseological collection is a kind of catalog of text samples. The samples were from articles selected from the leading English-language scientific journals and were systematized in such away that when writing an article in English, a Russian researchers are able easy to find examples suitable for his/her own work. Furthermore, the selected samples can be transformed accordingly saving the semantic and syntactic relations between the elements and, finally, be inserted into the text. The second part of this work is devoted to the detailed analysis of the English scientific literature and also the section "Legality of the provisions of the problem".

Identification, Prediction and Data Analysis of Noncoding RNAs: A Review

2019 ◽  
Vol 15 (3) ◽  
pp. 216-230 ◽  
Author(s):  
Abbasali Emamjomeh ◽  
Javad Zahiri ◽  
Mehrdad Asadian ◽  
Mehrdad Behmanesh ◽  
Barat A. Fakheri ◽  
...  
Keyword(s):  
Computational Methods ◽  
State Of The Art ◽  
Noncoding Rnas ◽  
Research Field ◽  
Design Strategies ◽  
Structural Prediction ◽  
Computational Costs ◽  
Cellular Processes ◽  
Laboratory Techniques ◽  
Fast Prediction

Background:Noncoding RNAs (ncRNAs) which play an important role in various cellular processes are important in medicine as well as in drug design strategies. Different studies have shown that ncRNAs are dis-regulated in cancer cells and play an important role in human tumorigenesis. Therefore, it is important to identify and predict such molecules by experimental and computational methods, respectively. However, to avoid expensive experimental methods, computational algorithms have been developed for accurately and fast prediction of ncRNAs.Objective:The aim of this review was to introduce the experimental and computational methods to identify and predict ncRNAs structure. Also, we explained the ncRNA’s roles in cellular processes and drugs design, briefly.Method:In this survey, we will introduce ncRNAs and their roles in biological and medicinal processes. Then, some important laboratory techniques will be studied to identify ncRNAs. Finally, the state-of-the-art models and algorithms will be introduced along with important tools and databases.Results:The results showed that the integration of experimental and computational approaches improves to identify ncRNAs. Moreover, the high accurate databases, algorithms and tools were compared to predict the ncRNAs.Conclusion:ncRNAs prediction is an exciting research field, but there are different difficulties. It requires accurate and reliable algorithms and tools. Also, it should be mentioned that computational costs of such algorithm including running time and usage memory are very important. Finally, some suggestions were presented to improve computational methods of ncRNAs gene and structural prediction.

Predicting Kinase-Substrate Interactions in Medulloblastoma Subtypes

2020 ◽  
Author(s):  
Aparna Krishnan ◽  
Kristin Leskoske ◽  
Krystine Garcia-Mansfield ◽  
Ritin Sharma ◽  
Jessica Rusert ◽  
...  
Keyword(s):  
Substrate Interactions ◽  
Kinase Substrate

scholarly journals InstantDL: an easy-to-use deep learning pipeline for image segmentation and classification

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Dominik Jens Elias Waibel ◽  
Sayedali Shetab Boushehri ◽  
Carsten Marr
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Specific Problem ◽  
State Of The Art ◽  
Image Data ◽  
Semantic Segmentation ◽  
Parameter Tuning ◽  
Cellular Processes ◽  
Minimal Effort ◽  
Instance Segmentation

Abstract Background Deep learning contributes to uncovering molecular and cellular processes with highly performant algorithms. Convolutional neural networks have become the state-of-the-art tool to provide accurate and fast image data processing. However, published algorithms mostly solve only one specific problem and they typically require a considerable coding effort and machine learning background for their application. Results We have thus developed InstantDL, a deep learning pipeline for four common image processing tasks: semantic segmentation, instance segmentation, pixel-wise regression and classification. InstantDL enables researchers with a basic computational background to apply debugged and benchmarked state-of-the-art deep learning algorithms to their own data with minimal effort. To make the pipeline robust, we have automated and standardized workflows and extensively tested it in different scenarios. Moreover, it allows assessing the uncertainty of predictions. We have benchmarked InstantDL on seven publicly available datasets achieving competitive performance without any parameter tuning. For customization of the pipeline to specific tasks, all code is easily accessible and well documented. Conclusions With InstantDL, we hope to empower biomedical researchers to conduct reproducible image processing with a convenient and easy-to-use pipeline.

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