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ASAP-SML: An antibody sequence analysis pipeline using statistical testing and machine learning
2020
PLoS Computational Biology
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Machine learning and statistical significance testing techniques are applied to antibody sequences and extracted feature fingerprints to identify distinguishing feature values and combinations thereof. ...
We develop a pipeline, Antibody Sequence Analysis Pipeline using Statistical testing and Machine Learning (ASAP-SML), to identify features that distinguish one set of antibody sequences from antibody sequences ...
In the Analysis step, salient features are identified using statistical testing and machine learning techniques. ...
doi:10.1371/journal.pcbi.1007779
pmid:32339164
fatcat:hrfnaeknybhbbj7aplu5qvpgie
TVNViewer: An interactive visualization tool for exploring networks that change over time or space
2011
Bioinformatics
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This course focuses on modern machine learning methodologies for computational problems in molecular biology and genetics. ...
• Statistical modeling and analysis of network and relational data, especially reverse engineering and meta-analysis of temporally evolving social and biological networks • Statistical machine learning ...
doi:10.1093/bioinformatics/btr273
pmid:21551142
pmcid:PMC3117350
fatcat:xvl5xgon3jah7hkljw2zco6mjm
Advancements in Computational Biology: Unraveling the Mysteries of Life
2024
Zenodo
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The core principles of this multidisciplinary domain, including bioinformatics, mathematical modeling, and machine learning, are examined for their roles in organizing, simulating, and extracting knowledge ...
The article envisions a future where the integration of artificial intelligence, deep learning, and personalized medicine further catalyses the impact of Computational Biology on scientific discovery. ...
The marriage of computational prowess and statistical finesse empowers machine learning to contribute to diagnostics, drug discovery, and personalized medicine [5] . ...
doi:10.5281/zenodo.11059376
fatcat:tceynd4jufbwlae7wsn6i2trou
Current Developments in Machine Learning Techniques in Biological Data Mining
2017
Bioinformatics and Biology Insights
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The history of the relationship between machine learning and biology is considered long and complex. ...
Advances in the field of biology have generated massive opportunities to allow the implementation of modern computational and statistical techniques. ...
Parametric and nonparametric machine learning algorithms are emerging computational methods that have increasing applications in the area of bioinformatics and computational biology. ...
doi:10.1177/1177932216687545
pmid:28469415
pmcid:PMC5390918
fatcat:bmxrd3ymqrcmhofko76gl25fka
Preface
[chapter]
2019
Computational Biology
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accomplished by borrowing and applying know-how from other sciences, such as mathematics, statistics, and computer sciences to biology, medicine, and disease analysis. ...
Following this is the approach of using machine learning or deep learning in omics data analysis and precision medicine, as described in Chapter 3: deep learning allows us to identify complex patterns ...
Chapter 9 reviews cheminformatics and computational approaches in metabolomics using data mining methods and bioinformatics tools, including machine learning approaches. ...
doi:10.15586/computationalbiology.2019.pr
fatcat:32fmtabkpbdkfiyxmvtlfjljle
Machine learning methods are useful for Approximate Bayesian Computation in evolution and ecology
2017
Peer Community in Evolutionary Biology
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This preprint has been reviewed and recommended by Peer Community In Evolutionary Biology (http://dx.doi.org/10.24072/pci.evolbiol.100036). ...
trade-off in term of quality of point estimator precision and credible interval estimations for a given computing time. ...
Machine learning methods are useful for Approximate Bayesian Computation in evolution and ecology. ...
doi:10.24072/pci.evolbiol.100036
fatcat:d3sim4yhwrbhbiihc2jai4fmki
Introduction to Machine Learning and Bioinformatics
2008
Journal of Statistical Software
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In summary, in the book under review the authors introduce the reader to machine learning and bioinformatics. ...
The statistical basics are illustrated with well-chosen and popular examples. Every chapter (except Chapter 2) ends with exercises and references. ...
Introduction Machine learning (Hastie et al. 2001 ) is a sub-set of artificial intelligence and deals with techniques to allow computers to learn. ...
doi:10.18637/jss.v028.b02
fatcat:jrm7fqresnarbj45afjmlmeaxa
Ten quick tips for machine learning in computational biology
2017
BioData Mining
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Machine learning has become a pivotal tool for many projects in computational biology, bioinformatics, and health informatics. ...
A machine learning algorithm is a computational method based upon statistics, implemented in software, able to discover hidden non-obvious patterns in a dataset, and moreover to make reliable statistical ...
Availability of data and materials The R code of example images is available upon request.
Ethics approval and consent to participate Not applicable. ...
doi:10.1186/s13040-017-0155-3
pmid:29234465
pmcid:PMC5721660
fatcat:lqhrka4vtrg4feknninu7qujqi
NIPS workshop on New Problems and Methods in Computational Biology
2007
BMC Bioinformatics
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The goal of this workshop was to present emerging problems and machine learning techniques in computational biology, with a particular emphasis on methods for computational learning from heterogeneous ...
Whistler, British Columbia, Canada The field of computational biology has seen dramatic growth over the past few years, both in terms of available data, scientific questions and challenges for learning ...
We gratefully acknowledge financial support from PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning), a European Network of Excellence (NoE). ...
doi:10.1186/1471-2105-8-s10-s1
pmcid:PMC2230502
fatcat:22n3fdez7vf43hdgtzsmhnprvm
Computational Structural Biology: Successes, Future Directions, and Challenges
2019
Molecules
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Computational biology has made powerful advances. ...
We attempt to forecast the areas that computational structural biology will embrace in the future and the challenges that it may face. ...
This research was supported by the National Science Foundation Grant Nos. 1763233 and 1821154 and a Jeffress Memorial Trust Award to AS. ...
doi:10.3390/molecules24030637
fatcat:fld2i6wfzvdnncts6efi45lor4
Selected proceedings of Machine Learning in Systems Biology: MLSB 2016
2016
BMC Bioinformatics
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The burgeoning field of systems biology creates a huge need for methods from machine learning, which find statistical dependencies and patterns in these large-scale datasets and use these to establish ...
MLSB started in 2007 and since 2008 has been colocated with major conferences in computational and systems biology (ECCB 2012(ECCB , 2014 ISMB/ECCB 2011, 2013 ICSB 2010) or machine learning (ECML 2008- ...
, Bayer, Enza Zaden, Philips and RijkZwaan. ...
doi:10.1186/s12859-016-1305-1
pmid:28105910
pmcid:PMC5249013
fatcat:nsxsvbdp3fdghlajbcu3dsg5qu
Artificial Intelligence in Biomedical Science
2019
Advances in Bioengineering and Biomedical Science Research
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It is also aims on relevant sciences that includes but not limited to anatomy, cell biology, biochemistry, microbiology, genetics, molecular biology, immunology, mathematics, statistics and bioinformatics ...
It also includes science disciplines whose fundamental aspect is biology of human health and diseases. ...
It is also aims on relevant sciences that includes but not limited to anatomy, cell biology, biochemistry, microbiology, genetics, molecular biology, immunology, mathematics, statistics and bioinformatics ...
doi:10.33140/abbsr.02.04.06
fatcat:hljr7nckwjbsdhevwoifoplt7u
Applied Topology based deep learning for Biomolecular Data
2017
Figshare
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2bHow can machine learning, artificial intelligence, and applied statistics contribute to our research space and/or open up new areas of research? ...
How should such models be used insitu to adapt computational/mathematical methodologies to architecture and machine state? ...
is theoretical modeling and computational algorithms, which have their roots in mathematics, statistics, and computer science. ...
doi:10.6084/m9.figshare.5336227.v1
fatcat:qhpibinohjacvk36pmjuql6f3y
AI in Natural Sciences: A Primer
2021
Zenodo
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Artificial intelligence (AI) is a field of computer science that enables machines to perform tasks normally requiring human intelligence. ...
AI is being used more and more by natural scientists such as physics, chemists, and biologists to perform various tasks. This paper is a primer on the uses of AI in natural sciences. ...
Quantum systems, statistical mechanics, astrophysics, and particle physics are on the forefront of machine learning. Biology: Biology is one of the most promising beneficiaries of AI. ...
doi:10.5281/zenodo.10574912
fatcat:uqew4es77va6dmmw6b2shanuva
Chemoinformatics, Drug Design, and Systems Biology
2005
Genome Informatics Series
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Acknowledgments Work supported in part by grants from the NIH, NSF, and a Laurel Wilkening Faculty Innovation Award. ...
and statistical machine learning applications [ 14, 20] . ...
Computational methods in chemistry can be organized along a spectrum ranging from Schrodinger equation, to molecular dynamics, to statistical machine learning methods. ...
doi:10.11234/gi1990.16.2_281
fatcat:f6lciuho6rcmze3z7thucm2cuu
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