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MethylSight: Taking a wider view of lysine methylation through computer-aided discovery to provide insight into the human methyl-lysine proteome [article]

Kyle K Biggar, Yasser B Ruiz-Blanco, Francois Charih, Qi Fang, Justin Connolly, Kristin Frensemier, Hemanta Adhikary, Shawn S.C. Li, James R Green
2018 bioRxiv   pre-print
We present the development of a machine learning model for predicting lysine methylation sites among human proteins. The model uses fully-alignment-free features encoding sequence-based information.  ...  A total of 57 novel predicted histone methylation sites were selected for evaluation by targeted mass spectrometry, with 51 sites positively re-assigned as true methylated sites, while one site was also  ...  All clusters are differentially coloured and annotated used human GO terms. .  ... 
doi:10.1101/274688 fatcat:5hv4qllhirforhvyi5dm4y75ia

Tools to reverse-engineer multicellular systems: case studies using the fruit fly

Qinfeng Wu, Nilay Kumar, Vijay Velagala, Jeremiah J. Zartman
2019 Journal of Biological Engineering  
Reverse-engineering how complex multicellular systems develop and function is a grand challenge for systems bioengineers.  ...  Here, we survey a selection of these tools including microfluidic devices, imaging and computer vision techniques.  ...  The authors apologize for only being able to cite a subset of relevant examples and reviews due to space constraints.  ... 
doi:10.1186/s13036-019-0161-8 pmid:31049075 pmcid:PMC6480878 fatcat:i3xqiefpy5ecphxtwy4yjl7sfa

Data Mining and Machine Learning Methods for Microarray Analysis [chapter]

Werner Dubitzky, Martin Granzow, Daniel Berrar
2002 Methods of Microarray Data Analysis  
The field of data mining and machine learning provides a wealth of methodologies and tools for analyzing large data sets.  ...  We review two classical machine learning techniques suitable for microarray analysis, namely decision trees and artificial neural networks.  ...  Witten, I.H. and Frank, E., "Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations", Morgan Kaufmann Pub., 2000. Figure 1 . 1 The data mining process.  ... 
doi:10.1007/978-1-4615-0873-1_2 fatcat:746oucovbnbq3otejxtwwgu5zi

Artificial intelligence used in genome analysis studies

Edo D'Agaro
2018 The EuroBiotech Journal  
Recent research has clearly shown that machine learning technologies can efficiently analyse large sets of genomic data and help to identify novel gene functions and regulation regions.  ...  To date, Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNN) have been demonstrated to be the best tools for improving performance in problem solving tasks within the genomic field  ...  In Table 2 are listed the main software tools used for machine learning studies.  ... 
doi:10.2478/ebtj-2018-0012 fatcat:5vorc7y3ajgljcgscjg2wt25bi

Integrative approaches for analysis of mRNA and microRNA high-throughput data

Petr V. Nazarov, Stephanie Kreis
2021 Computational and Structural Biotechnology Journal  
In this review, we will focus on miRNAs as key post-transcriptional regulators summarizing current computational approaches for miRNA:target gene prediction as well as new data-driven methods to tackle  ...  In silico analysis of datasets, representing only one RNA species is well established and a variety of tools and pipelines are available.  ...  Computational prediction of canonical or non-canonical miRNA targets employ different statistical and machine-learning approaches and generally analyse some of the following criteria: i) degree of Watson-Crick  ... 
doi:10.1016/j.csbj.2021.01.029 pmid:33680358 pmcid:PMC7895676 fatcat:cr3dllion5dedhr4hmzooge7wm

Deployment of an aerial platform system for rapid restoration of communications links after a disaster: a machine learning approach

Faris A. Almalki, Marios C. Angelides
2019 Computing  
This paper proposes a physical model that links base stations that are still operational with aerial platforms and then uses a machine learning framework to evolve ground-to-air propagation model for such  ...  All these in addition to environmental issues such as high gas emissions during satellite launches, as well as signals that have no regard for geographical or political boundaries, which might or might  ...  A machine learning framework for evolving an optimal propagation model We propose a machine Learning framework that evolves an optimal propagation model from the initial, non-optimised, propagation model  ... 
doi:10.1007/s00607-019-00764-x fatcat:c4adeqhqandehdqjpfkok6stii

Machine learning at the limit

John Canny, Huasha Zhao, Bobby Jaros, Ye Chen, Jiangchang Mao
2015 2015 IEEE International Conference on Big Data (Big Data)  
We have shown that Kylix approaches the practical network throughput limit for allreduce, a basic primitive for distributed machine learning.  ...  Many systems have been developed for machine learning at scale.  ...  Criteo is a large-scale prediction task from Kaggle. Spark-XX is a Spark cluster with XX cores, and similarly for GraphLab-XXX. Yahoo-1000 is a 1000-node cluster with an unspecified number of cores.  ... 
doi:10.1109/bigdata.2015.7363760 dblp:conf/bigdataconf/CannyZJCM15 fatcat:wrhujzxjcnhkjphusw5lv46rca

EBI metagenomics—a new resource for the analysis and archiving of metagenomic data

Sarah Hunter, Matthew Corbett, Hubert Denise, Matthew Fraser, Alejandra Gonzalez-Beltran, Christopher Hunter, Philip Jones, Rasko Leinonen, Craig McAnulla, Eamonn Maguire, John Maslen, Alex Mitchell (+11 others)
2013 Nucleic Acids Research  
Metagenomics brings with it a number of challenges, including the management, analysis, storage and sharing of data.  ...  Metagenomics is a relatively recently established but rapidly expanding field that uses high-throughput next-generation sequencing technologies to characterize the microbial communities inhabiting different  ...  ACKNOWLEDGEMENTS The authors would like to thank users from the metagenomics community who have volunteered in usability studies and/or provided feedback to us via surveys and emails; they are too numerous  ... 
doi:10.1093/nar/gkt961 pmid:24165880 pmcid:PMC3965009 fatcat:omjatsv6lrhklc5qhkldkhejl4

Data‐Driven Materials Science: Status, Challenges, and Perspectives

Lauri Himanen, Amber Geurts, Adam Stuart Foster, Patrick Rinke
2019 Advanced Science  
Such related tools as materials databases, machine learning, and high-throughput methods are now established as parts of the materials research toolset.  ...  In this field, data is the new resource, and knowledge is extracted from materials datasets that are too big or complex for traditional human reasoning-typically with the intent to discover new or improved  ...  Rousi, Milica Todorovic ′, Sven Bossuyt, Miguel Caro, David Gao, Matthias Scheffler, Bryce Meredig, and Heidi Henrickson for insightful discussions and a careful reading of our manuscript. Computing  ... 
doi:10.1002/advs.201900808 pmid:31728276 pmcid:PMC6839624 fatcat:j6adrk22zfadrm4usoelg3c7p4

Machine learning approach yields epigenetic biomarkers of food allergy: A novel 13-gene signature to diagnose clinical reactivity

Ayush Alag, Jorg Tost
2019 PLoS ONE  
This study presents a purely-computational machine learning approach, conducted using DNA Methylation (DNAm) data, to accurately diagnose food allergies and potentially find epigenetic targets for the  ...  David Casso, The Harker School for their valuable comments on the manuscript.  ...  Materials and methods Weka [20] , a Java-based machine learning toolkit, was used for building the predictive models.  ... 
doi:10.1371/journal.pone.0218253 pmid:31216310 pmcid:PMC6584060 fatcat:ulfzpvbfczaabovucueoxkf2ie

TBD: Benchmarking and Analyzing Deep Neural Network Training [article]

Hongyu Zhu, Mohamed Akrout, Bojian Zheng, Andrew Pelegris, Amar Phanishayee, Bianca Schroeder, Gennady Pekhimenko
2018 arXiv   pre-print
DNN models that cover a wide range of machine learning applications: image classification, machine translation, speech recognition, object detection, adversarial networks, reinforcement learning, and  ...  We also build a new set of tools for memory profiling in all three major frameworks; much needed tools that can finally shed some light on precisely how much memory is consumed by different data structures  ...  All of our experiments are carried out on a 16-machine cluster, where each node is equipped with a Xeon 28-core CPU and one to four NVidia Quadro P4000 GPUs.  ... 
arXiv:1803.06905v2 fatcat:66p3qqvak5axdeudnctsxze6nq

Computational methods, databases and tools for synthetic lethality prediction

Jing Wang, Qinglong Zhang, Junshan Han, Yanpeng Zhao, Caiyun Zhao, Bowei Yan, Chong Dai, Lianlian Wu, Yuqi Wen, Yixin Zhang, Dongjin Leng, Zhongming Wang (+3 others)
2022 Briefings in Bioinformatics  
Then, computational methods including statistical-based methods, network-based methods, classical machine learning methods and deep learning methods for SL prediction are summarized.  ...  Next, representative tools for SL prediction are introduced. Finally, the challenges and future work for SL prediction are discussed.  ...  With the rapid development of machine learning, various algorithms have been applied for SL prediction, including random forest (RF) [23] [24] [25] [26] [27] , matrix factorization [28] [29] [30] and  ... 
doi:10.1093/bib/bbac106 pmid:35352098 pmcid:PMC9116379 fatcat:dyztiwgg5raohohi3lojf4ijt4

A review of deep learning applications in human genomics using next-generation sequencing data

Wardah S. Alharbi, Mamoon Rashid
2022 Human Genomics  
Conclusively, this review is timely for biotechnology or genomic scientists in order to guide them why, when and how to use deep learning methods to analyse human genomic data.  ...  Through the advent of high-throughput data generating technologies in human genomics, we are overwhelmed with the heap of genomic data.  ...  Lamya Alomair for her support during the development of this manuscript.  ... 
doi:10.1186/s40246-022-00396-x pmid:35879805 pmcid:PMC9317091 fatcat:ethd6g7babaytigsnlfsikmufa

Investigation of muscle transcriptomes using gradient boosting machine learning identifies molecular predictors of feed efficiency in growing pigs

Farouk Messad, Isabelle Louveau, Basile Koffi, Hélène Gilbert, Florence Gondret
2019 BMC Genomics  
Pigs (n = 71) from three experiments belonged to generations 6 to 8 of selection, were fed either a diet with a standard composition or a diet rich in fiber and lipids, received feed ad libitum or at restricted  ...  The error of prediction was less than 8% for FCR. Altogether, 56 predictors were common to RFI-BV and FCR. Expression levels of 24 target genes were further measured by qPCR.  ...  Acknowledgments The authors are grateful to Christine Tréfeu and Annie Vincent for their expertise in qPCR analyses.  ... 
doi:10.1186/s12864-019-6010-9 pmid:31419934 pmcid:PMC6697907 fatcat:zwzpxa4rwzdrxgvkhrdjlemexq

The Challenges in ML-Based Security for SDN

Tam N. Nguyen
2018 2018 2nd Cyber Security in Networking Conference (CSNet)  
Previous surveys have been done on either adversarial machine learning or the general vulnerabilities of SDNs but not both.  ...  Sitting at the application layer and communicating with the control layer, machine learning based SDN security models exercise a huge influence on the routing/switching of the entire SDN.  ...  NeuRoute: Predictive dynamic routing for software-defined networks [20] Predictive routing for maximum throughput 95% Supervised training with Short Term Memory Recurrent Neural Net- work algorithm  ... 
doi:10.1109/csnet.2018.8602680 dblp:conf/csnet/Nguyen18 fatcat:hbbd55la3fci5e6gykphv7xusa
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