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Machine learning methods in the computational biology of cancer
2014
Proceedings of the Royal Society A
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Correction Cite this article: Vidyasagar M. 2015 Machine learning methods in the computational biology of cancer. Proc. R. Soc. A 471: 20140805. http://dx. ...
There are a couple of unfortunate errors in equations (4.2) and (4.3) of the article. Specifically, the ∞ -norm in both equations should be replaced by the 1 -norm. ...
doi:10.1098/rspa.2014.0805
pmid:25567539
pmcid:PMC4277199
fatcat:hxd7olkzwnhyjocdkxo7sptahu
Machine learning methods in the computational biology of cancer
2014
Proceedings of the Royal Society A
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A new algorithm for sparse feature selection in classification problems is presented, and its validation in endometrial cancer is briefly discussed. Some open problems are also presented. ...
As an illustration of the possibilities, a new algorithm for sparse regression is presented, and is applied to predict the time to tumor recurrence in ovarian cancer. ...
There are a couple of unfortunate errors in equations (4.2) and (4.3) of the article. Specifically, the ∞ -norm in both equations should be replaced by the 1 -norm. ...
doi:10.1098/rspa.2014.0081
pmid:25002826
pmcid:PMC4032557
fatcat:qpn7xmlqtfeulpgziguxkfinym
TVNViewer: An interactive visualization tool for exploring networks that change over time or space
2011
Bioinformatics
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, UAI'08 (2008) -European Conference on Computer Vision, ECCV-08 (2008) -The NIPS workshop on Machine Learning in Computational Biology, NIPS (2007) -Joint Conference on Empirical Methods in Natural Language ...
This course focuses on modern machine learning methodologies for computational problems in molecular biology and genetics. ...
doi:10.1093/bioinformatics/btr273
pmid:21551142
pmcid:PMC3117350
fatcat:xvl5xgon3jah7hkljw2zco6mjm
Integrative Analysis of Next-Generation Sequencing for Next-Generation Cancer Research toward Artificial Intelligence
2021
Cancers
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It opened a new research area incorporating systems biology and machine learning. As large-scale NGS data accumulated, sophisticated data analysis methods became indispensable. ...
In this work, we review novel technologies developed for NGS data analysis, and we describe how these computational methodologies integrate systems biology and omics data. ...
We mainly focus on the following three levels, machine learning, machine learning with systems biology, and more recent approaches in deep learning. ...
doi:10.3390/cancers13133148
pmid:34202427
pmcid:PMC8269018
fatcat:te73wenebne4hkc5p77m2ry7em
Editorial of Special Issue "Deep Learning and Machine Learning in Bioinformatics"
2022
International Journal of Molecular Sciences
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In recent years, deep learning has emerged as a highly active research field, achieving great success in various machine learning areas, including image processing, speech recognition, and natural language ...
processing, and now rapidly becoming a dominant tool in biomedicine [...] ...
Banegas-Luna et al. discussed the interpretability of machine learning/deep learning methods in cancer research [21] . Defresne et al. reviewed deep learning methods used for protein design [22] . ...
doi:10.3390/ijms23126610
pmid:35743052
pmcid:PMC9224509
fatcat:5sb5xciq3bfefkety2cxun37ne
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. ...
Biomedical Imaging through Artificial Intelligence Academics at University of Zurich used various methods in machine learning to improve optoacoustic imaging. ...
doi:10.33140/abbsr.02.04.06
fatcat:hljr7nckwjbsdhevwoifoplt7u
Computational Approaches for Biomarker Discovery
2014
Journal of Intelligent Learning Systems and Applications
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A brief discussion of some necessary preliminaries on machine learning techniques (e.g., clustering and support vector machines-SVM) which are commonly used in many applications to biomarkers discovery ...
In this review, we outline recent progresses of computational biology application in research on biomarkers discovery. ...
Introduction Machine learning is the subfield of artificial intelligence which focuses on methods to construct computer programs that learn from experience with respect to some class of tasks and a performance ...
doi:10.4236/jilsa.2014.64012
fatcat:md5hscyjtnd3zbcbl6rpsba4pq
Guest Editors' Introduction to the Special Issue: Machine Learning for Bioinformatics-Part 1
2005
IEEE/ACM Transactions on Computational Biology & Bioinformatics
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Each of these tasks can be framed as a problem in machine learning. We therefore see a great potential to increase the interaction between machine learning and bioinformatics. ...
Bioinformatics, or computational biology, is the interdisciplinary science of interpreting biological data using information technology and computer science. ...
doi:10.1109/tcbb.2005.25
fatcat:4ks6pssulrfkjfrqsfngrqktiy
ARTIFICIAL INTELLIGENCE AND PATHOLOGY
2021
Hematological Oncology
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In this talk we focus on the recent trends and successes of artificial intelligence in the medical field, with practical examples in which artificial intelligence supports physicians in their work, without ...
information automatically, thus allowing the integration of information of different nature and type, but also techniques usefull to manage uncertainty and lack of data, a phenomenon very often present in ...
USA Machine learning holds the promise to transform cancer science and treatment. ...
doi:10.1002/hon.8_2879
fatcat:w2xc6heruzf6pg4vgavn23ufre
Machine Learning and Network Methods for Biology and Medicine
2015
Computational and Mathematical Methods in Medicine
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In recent years, many computational methods have been proposed to tackle the problems that arise in analyzing various large-scale high dimensional data in biology and medicine. ...
It is therefore necessary to develop more effective and efficient approaches to analyzing such data, requiring more powerful methods like advanced machine learning algorithms and network based methods. ...
In recent years, many computational methods have been proposed to tackle the problems that arise in analyzing various large-scale high dimensional data in biology and medicine. ...
doi:10.1155/2015/915124
pmid:26693251
pmcid:PMC4677004
fatcat:k72cvht4zfecrdclk5uojfga6y
Editorial: Artificial Intelligence (AI) Optimized Systems Modeling for the Deeper Understanding of Human Cancers
2021
Frontiers in Bioengineering and Biotechnology
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Editorial on the Research Topic Artificial Intelligence (AI) Optimized Systems Modeling for the Deeper Understanding of Human Cancers Cancer research in the field of Computational Systems Biology attempts ...
; 2) exploring future-generation interesting and practical biomedical applications in AI, machine learning, big data sciences, knowledge-based system, etc., to provide novel ideas and solutions in mathematical ...
All submitted manuscripts had gone through at least two rounds of revision with reviewers in the related fields, including bioinformatics, computational biology, machine learning, and clinical study, etc ...
doi:10.3389/fbioe.2021.756314
pmid:34708028
pmcid:PMC8542901
fatcat:i2uluzuxzfb3lfxeiulztbbzoy
Scalable Data Mining Algorithms in Computational Biology and Biomedicine
2017
BioMed Research International
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In recent years, computational methods appeared vastly in the biomedicine and bioinformatics research, including medical image analysis, healthcare informatics, and cancer genomics. ...
Since "Precision Medicine" was initially launched by President Obama, it presents a huge challenge and chance for the computational biology and biomedicine. ...
learn the most discriminative features from data for both single-cell and object tracking in computational biology, cell biology, and computer vision. ...
doi:10.1155/2017/5652041
pmid:28337450
pmcid:PMC5350399
fatcat:3mce7hxkyzcgnfqfhl4sie2m7i
AI and Bioinformatics
2004
The AI Magazine
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In particular, we address the issue of how techniques from AI can be applied to many of the open and complex problems of modern-day molecular biology. ...
This article is an editorial introduction to the research discipline of bioinformatics and to the articles in this special issue. ...
In particular, he reviews a method that combines the mining of controlled vocabulary with machine learning to render genomewide annotations of function. ...
doi:10.1609/aimag.v25i1.1743
dblp:journals/aim/GlasgowJR04
fatcat:u2zgevmy6jdfnmkm52kipcytty
Incorporating Machine Learning into Established Bioinformatics Frameworks
2021
International Journal of Molecular Sciences
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The exponential growth of biomedical data in recent years has urged the application of numerous machine learning techniques to address emerging problems in biology and clinical research. ...
We outline the challenges posed for machine learning, and, in particular, deep learning in biomedicine, and suggest unique opportunities for machine learning techniques integrated with established bioinformatics ...
Machine learning (ML) is a field in computer science that studies the use of computers to simulate human learning by exploring patterns in the data and applying self-improvement to continually enhance ...
doi:10.3390/ijms22062903
pmid:33809353
pmcid:PMC8000113
fatcat:ssfoobbtcjhidbaffbkakqbwfe
Exploring Potential of Quantum Computing in Creating Smart Healthcare
2021
The Open Biology Journal
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Machine learning can aid in detecting human body anomalies and quantum computation may be used to evaluate therapy results. ...
In cancer treatment, quantum computing will contribute to improved therapies. ...
Rishabha Malviya is the editorial board member of the journal The Open Biology Journal. ...
doi:10.2174/1874196702109010056
fatcat:xklq3vpxkbbonfkyadgtqnm3eq
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