Filters








550 Hits in 4.9 sec

In silico prediction of novel therapeutic targets using gene–disease association data

Enrico Ferrero, Ian Dunham, Philippe Sanseau
2017 Journal of Translational Medicine  
On a test set, the neural network classifier achieves over 71% accuracy with an AUC of 0.76 when predicting therapeutic targets in a semi-supervised learning setting.  ...  Computational prediction of successful targets could have a considerable impact on attrition rates in the drug discovery pipeline by significantly reducing the initial search space.  ...  Many feature selection methods rely on assessing the importance of the different features by calculating how related they are with the response variable.  ... 
doi:10.1186/s12967-017-1285-6 pmid:28851378 pmcid:PMC5576250 fatcat:2oahmh2uz5e6bdzwuapjm7b4ey

Accuracy of Prediction by Machine Learning Algorithms

2019 International Journal of Engineering and Advanced Technology  
Sentiment140 dataset was used and performance of each algorithm in terms of training time, prediction time and accuracy of prediction have been documented and compared.  ...  interpretation, design recognition, and a many other commercial purposes and has led to a separate research interest in data mining to identify hidden regularities or irregularities in social data that growing by  ...  Interestingly, in many special instances of learning problematic with additional assumptions, unlabelled data can indeed be warranted to improve the expected accuracy of supervised learning.  ... 
doi:10.35940/ijeat.f1371.0986s319 fatcat:2skxbwh4wzdsfphyi4l2bffsne

Data mining and machine learning approaches for prediction modelling of schistosomiasis disease vectors

Terence Fusco, Yaxin Bi, Haiying Wang, Fiona Browne
2019 International Journal of Machine Learning and Cybernetics  
The proposed incremental transductive ensemble approach model enables the combination of complimentary algorithms to provide labelling for unlabelled vector density instances.  ...  This research presents viable solutions for prediction modelling of schistosomiasis disease based on vector density.  ...  Funding Funding was provided by Department for Employment and Learning, Northern Ireland and European Space Agency.  ... 
doi:10.1007/s13042-019-01029-x pmid:33727985 pmcid:PMC7224118 fatcat:sfmj5gdfszco5hcpltjf4tpee4

Machine Learning Predicts Drug Metabolism and Bioaccumulation by Intestinal Microbiota

Laura E. McCoubrey, Stavriani Thomaidou, Moe Elbadawi, Simon Gaisford, Mine Orlu, Abdul W. Basit
2021 Pharmaceutics  
From this, 11 supervised learning models were developed that could predict drugs' susceptibility to depletion by gut microbiota.  ...  Such variability can lead to significant differences in pharmacokinetics, which may be associated with dosing difficulties and lack of medication response.  ...  This feature provides added control against overfitting and can improve predictive accuracy [50] .  ... 
doi:10.3390/pharmaceutics13122001 pmid:34959282 pmcid:PMC8707855 fatcat:a3ezxxoxjzbhjh7lyxju7qbtgm

Trends in Using IoT with Machine Learning in Health Prediction System

Amani Aldahiri, Bashair Alrashed, Walayat Hussain
2021 Forecasting  
The paper also provides some examples of IoT and machine learning to predict future healthcare system trends.  ...  Depending on the type of IoT dataset, we need to choose an optimal method to predict critical healthcare data.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/forecast3010012 fatcat:2dp6pcnewvgqpkwqqptleemgyq

Graph-Embedded Online Learning for Cell Detection and Tumour Proportion Score Estimation

Jinhao Chen, Yuang Zhu, Zhao Chen
2022 Electronics  
Without enough labelled samples, the accuracy of these methods would drop rapidly. To handle limited annotations and massive unlabelled data, semi-supervised learning methods have been developed.  ...  Trained by both historical data and reliable new samples, the online network can predict nuclear locations for upcoming new images while being optimized.  ...  To avoid introducing errors, GeoNet selects only the most reliable new samples with rigid confidence measured according to morphology features of extracted nuclear instances to optimize the backbone. 2  ... 
doi:10.3390/electronics11101642 fatcat:vc2ngqqoqbccfowqtql2bubvxm

PyRelationAL: A Library for Active Learning Research and Development [article]

Paul Scherer and Thomas Gaudelet and Alison Pouplin and Suraj M S and Jyothish Soman and Lindsay Edwards and Jake P. Taylor-King
2022 arXiv   pre-print
PyRelationAL is maintained using modern software engineering practices - with an inclusive contributor code of conduct - to promote long term library quality and utilisation.  ...  The library is supplemented by an expansive set of tutorials, demos, and documentation to help users get started.  ...  At the k th iteration, we denote by L k the dataset and by U k the set of unlabelled elements.  ... 
arXiv:2205.11117v1 fatcat:kpcjgos5rra3jkpob7l4ts3uuq

A Comparative Analysis of Active Learning for Biomedical Text Mining

Usman Naseem, Matloob Khushi, Shah Khalid Khan, Kamran Shaukat, Mohammad Ali Moni
2021 Applied System Innovation  
The annotation of this data has to be performed by qualified clinical experts, hence, limiting the use of this data due to the high cost of annotation.  ...  An underutilised technique of machine learning that can label new data called Active Learning (AL) is a promising candidate to address the high cost of the label the data.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/asi4010023 fatcat:e37me7znw5fp5enlh6v4ak55zy

Personalized Liver Cancer Risk Prediction Using Big Data Analytics Techniques with Image Processing Segmentation

Anurag Jain, Ahmed Nadeem, Huda Majdi Altoukhi, Sajjad Shaukat Jamal, Henry kwame Atiglah, Haitham Elwahsh, Ahmed A. Ewees
2022 Computational Intelligence and Neuroscience  
The accuracy range of 80 percent using the performance metrics analysis is included in the actual formulation of the medicine that is utilised for liver cancer prediction in this study.  ...  the prediction of liver cancer.  ...  Acknowledgments e authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through a research group program under grant number R. G.  ... 
doi:10.1155/2022/8154523 pmid:35387251 pmcid:PMC8979737 fatcat:zh3gzt4qrbbktcfqsz7gfxxmae

Utilising Graph Machine Learning within Drug Discovery and Development [article]

Thomas Gaudelet, Ben Day, Arian R. Jamasb, Jyothish Soman, Cristian Regep, Gertrude Liu, Jeremy B. R. Hayter, Richard Vickers, Charles Roberts, Jian Tang, David Roblin, Tom L. Blundell (+2 others)
2021 arXiv   pre-print
Herein, we present a multidisciplinary academic-industrial review of the topic within the context of drug discovery and development.  ...  After introducing key terms and modelling approaches, we move chronologically through the drug development pipeline to identify and summarise work incorporating: target identification, design of small  ...  In phage display, selection works by physical capture and elution [192] ; for cell-based display technologies (like yeast display), fluorescence-activated cell sorting (FACS) is utilised for selection  ... 
arXiv:2012.05716v2 fatcat:kre2kx3x4ff43mmuh7khrxmmzy

Artificial intelligence (AI) and big data in cancer and precision oncology

Zodwa Dlamini, Flavia Zita Francies, Rodney Hull, Rahaba Marima
2020 Computational and Structural Biotechnology Journal  
Through these applications of AI, cancer diagnostics and prognostic prediction are enhanced with NGS and medical imaging that delivers high resolution images.  ...  identification of therapeutic targets for novel drug discovery.  ...  Drug resistance is caused by several molecular mechanisms such as apoptosis to evade cytotoxicity induced by drugs, tumour migration, drug metabolism, increased DNA repair and activation of molecular pathways  ... 
doi:10.1016/j.csbj.2020.08.019 pmid:32994889 pmcid:PMC7490765 fatcat:ocli2vys3zfw7mbqeuio3foipq

Representation Transfer for Differentially Private Drug Sensitivity Prediction [article]

Teppo Niinimäki, Mikko Heikkilä, Antti Honkela, Samuel Kaski
2019 arXiv   pre-print
Our results significantly improve over previous state-of-the-art in accuracy of differentially private drug sensitivity prediction.  ...  We solve two machine learning tasks on gene expression of cancer cell lines: cancer type classification, and drug sensitivity prediction.  ...  Funding This work has been supported by the Academy of Finland [Finnish Center for Artificial Intelligence FCAI and grants 292334, 294238, 303815, 303816, 313124].  ... 
arXiv:1901.10227v1 fatcat:d3hr4roojzgj3bdwyehfigrn2a

HelixADMET: a robust and endpoint extensible ADMET system incorporating self-supervised knowledge transfer [article]

Shanzhuo Zhang, Zhiyuan Yan, Yueyang Huang, Lihang Liu, Donglong He, Wei Wang, Xiaomin Fang, Xiaonan Zhang, Fan Wang, Hua Wu, Haifeng Wang
2022 arXiv   pre-print
Accurate ADMET (an abbreviation for "absorption, distribution, metabolism, excretion, and toxicity") predictions can efficiently screen out undesirable drug candidates in the early stage of drug discovery  ...  Our results demonstrate that H-ADMET achieves an overall improvement of 4%, compared with existing ADMET systems on comparable endpoints.  ...  ()) by the feature vector of node 𝑣, i.e., ℎ ! ()) = 𝑥 ! .  ... 
arXiv:2205.08055v1 fatcat:rjvky4n2ebgzvcobznfpsm722e

Twitter mining using semi-supervised classification for relevance filtering in syndromic surveillance

Oduwa Edo-Osagie, Gillian Smith, Iain Lake, Obaghe Edeghere, Beatriz De La Iglesia, Olalekan Uthman
2019 PLoS ONE  
Additionally, we highlight the use of emojis and other special features capturing the tweet's tone to improve the classification performance.  ...  Even with keyword-based data collection, many of the tweets collected are not be relevant because they represent chatter, or talk of awareness instead of an individual suffering a particular condition.  ...  Acknowledgments We acknowledge support from NHS 111 and NHS Digital for their assistance with the NHS 111 system; Out-of-Hours providers submitting data to the GPOOH syndromic surveillance and Advanced  ... 
doi:10.1371/journal.pone.0210689 pmid:31318885 pmcid:PMC6638773 fatcat:2kqtjnjrjvcdxcfqaur6765imu

Using Twitter To Generate Signals For The Enhancement Of Syndromic Surveillance Systems: Semi-Supervised Classification For Relevance Filtering in Syndromic Surveillance [article]

Oduwa Edo-Osagie, Gillian Smith, Iain Lake, Obaghe Edeghere, Beatriz De La Iglesia
2019 bioRxiv   pre-print
We also propose the use of emojis and other special features capturing the tweet's tone to improve the classification performance.  ...  Even with keyword-based data collection, many of the tweets collected are not be relevant because they represent chatter, or talk of awareness instead of suffering a particular condition.  ...  three 330 functions: Choose-Label-Set(U, L, A) selects and returns a new set, R, of unlabelled 331 examples to be labelled; Assign-Labels(R, S, A) generates labels for the instances 332 selected by Choose-Label-Set  ... 
doi:10.1101/511071 fatcat:sqxvehr6s5emznodd4orc7ol4e
« Previous Showing results 1 — 15 out of 550 results