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The role of pattern recognition in creative problem solving: A case study in search of new mathematics for biology
2013
Progress in Biophysics and Molecular Biology
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Attempts to build mathematical theories for biology in the past century was met with modest and sporadic successes, and only in simple systems. ...
In this article, a qualitative model of humans' high creativity is presented as a starting point to consider whether the gap between soft and hard sciences is bridgeable. ...
The author also takes this opportunity to remember a very special individual: Michael Conrad of Wayne State University. ...
doi:10.1016/j.pbiomolbio.2013.03.017
pmid:23597605
fatcat:y6cnc2scnjcsbh6m3qz72a4kzu
Machine learning applications in bioinformatics
2012
Conference on Theory and Practice of Information Technologies
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Similarly to the domain of pattern mining, recent efforts in the field of molecular classification aim to employ background knowledge. ...
Nevertheless, molecular classifiers based solely on gene expression in most cases cannot be considered useful decision-making tools or decision-supporting tools. ...
dblp:conf/itat/Klema12
fatcat:olxmaiwpgjce3bwbuw45napamy
Logic-Based Machine Learning
[chapter]
2000
Logic-Based Artificial Intelligence
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Acknowledgements Thanks are due to the funders and researchers who helped develop the technology and applications of ILP on the Esprit projects ECOLES (1989)(1990)(1991)(1992), ILP I (1992), ILPnet (1992 ...
In our investigation it was found that the ability to make use of background knowledge from molecular biology, together with the ability to describe structural relations boosted the predictivity for a ...
In Golem was applied to one of the hardest open problems in molecular biology. ...
doi:10.1007/978-1-4615-1567-8_14
fatcat:n4ql555nlvg5pcjtgfae5wetxu
Introduction
1995
Machine Learning
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In fact, arguably the machine learning application with the largest impact on molecular biology is Uberbacher and Mural's (1991) GRAIL system, which uses neural networks to find genes in DNA sequences ...
The field of molecular biology is rich in problems that seem almost tailor-made for machine learning approaches. ...
They also discuss the general problem of using domain knowledge to improve the input representation used in a machine learning task. ...
doi:10.1007/bf00993376
fatcat:lqar2iebbvhsnorwjvix2n6bsm
Learning Constrained Edit State Machines
2009
2009 21st IEEE International Conference on Tools with Artificial Intelligence
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Experimental results are provided on a task in molecular biology, aiming to detect transcription factor binding sites. ...
Although the use of such models has lead to significant improvements of edit distance-based classification tasks, a new challenge has appeared on the horizon: How integrating background knowledge during ...
In order to improve the expressiveness of pair-HMMs, we aim to incorporate knowledge during the learning process in the form of constraints. ...
doi:10.1109/ictai.2009.27
dblp:conf/ictai/BoyerGHPS09
fatcat:7fcuf4hfvrebxhmmh66ui53f5a
WIBL: Workbench for Integrative Biological Learning
2011
Journal of Integrative Bioinformatics
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WIBL combines data integration, visualisation and modelling in a single portal-based workbench providing a comprehensive solution for interdisciplinary systems biology projects. ...
The construction of integrated datasets from potentially hundreds of sources with bespoke formats, and their subsequent visualization and analysis, is a recurring challenge in systems biology. ...
The model uses KEGG data on metabolism to learn from observations in a pilot study of liver toxicity induced by phenobarbital in rat which is being used to develop a model of the differences between rodent ...
doi:10.2390/biecoll-jib-2011-156
pmid:21705808
fatcat:miaxrlorjbhqrgms6xalwoigr4
WIBL: Workbench for Integrative Biological Learning
2011
Journal of Integrative Bioinformatics
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WIBL combines data integration, visualisation and modelling in a single portalbased workbench providing a comprehensive solution for interdisciplinary systems biology projects. ...
SummaryThe construction of integrated datasets from potentially hundreds of sources with bespoke formats, and their subsequent visualization and analysis, is a recurring challenge in systems biology. ...
The model uses KEGG data on metabolism to learn from observations in a pilot study of liver toxicity induced by phenobarbital in rat which is being used to develop a model of the differences between rodent ...
doi:10.1515/jib-2011-156
fatcat:gidd676o6jemflabl3h3xkr524
Digital Learning Material for Model Building in Molecular Biology
2005
Journal of Science Education and Technology
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In order to provide students the opportunity to improve their model building skills, we decided to develop a number of digital cases about developmental biology. ...
However, in molecular biology curricula little attention is generally being paid to the development of this skill. ...
We also thank the other scientists and the students who participated in this study. ...
doi:10.1007/s10956-005-2740-3
fatcat:2xspchn6c5gvxhpdqx22q5tknu
Applications of machine learning and rule induction
1995
Communications of the ACM
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We consider rule induction in greater detail and review some of its recent applications, in each case stating the problem, how rule induction was used, and the status of the resulting expert system. ...
An important area of application for machine learning is in automating the acquisition of knowledge bases required for expert systems. ...
This work was funded in part by Grants N00014-94-1-0505 and N00014-94-1-0746 from the Office of Naval Research and the Advanced Research Projects Agency to the Institute for the Study of Learning and Expertise ...
doi:10.1145/219717.219768
fatcat:5gccbtwejbayzgfpv6t643odoq
A Systems Biology Approach to Learning Autophagy
2006
Autophagy
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data set can be used to define the pathway in full-the information from multiple complementary studies must be integrated in order to recapitulate our present understanding of the pathways mediating autophagy ...
As the data sets used in these class lessons are largely genomic and complementary in content, students will also understand first-hand the advantage of an integrative or systems biology study: No single ...
ACKNOWLEDGEMENTS We would like to thank the U-M Life Sciences Learning Community (supported by a National Science Foundation Director's Award for Distinguished Teaching Scholars to D.J. ...
doi:10.4161/auto.2227
pmid:16874048
fatcat:ecrho65tszgxbgzfuwxy52qoa4
Recursive Feature Generation for Knowledge-based Learning
[article]
2018
arXiv
pre-print
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In this work, we present a novel algorithm for injecting external knowledge into induction algorithms using feature generation. ...
When humans perform inductive learning, they often enhance the process with background knowledge. ...
In this work, we present a novel algorithm that uses a similar approach for enhancing inductive learning with background knowledge through feature generation. ...
arXiv:1802.00050v1
fatcat:kjdpig26lfbohcsiazpblvkisi
Inductive Logic Programming: Issues, results and the challenge of Learning Language in Logic
1999
Artificial Intelligence
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This has allowed successful applications of ILP in areas such as molecular biology and natural language which both have rich sources of background knowledge and both benefit from the use of an expressive ...
Logic programs are used as a single representation for examples, background knowledge and hypotheses. ...
Acknowledgements The author would like to thank the following people who collaborated on research described in this paper: Mike Sternberg, Steve Pulman, Donald Michie, David Haussler, Ross King, David ...
doi:10.1016/s0004-3702(99)00067-3
fatcat:6fbihina7zba3neogm7knq45qe
Generating Explainable and Effective Data Descriptors Using Relational Learning: Application to Cancer Biology
[chapter]
2020
Lecture Notes in Computer Science
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Here we demonstrate the use of relational learning to generate new data descriptors in such semantically complex background knowledge. ...
The key to success in machine learning is the use of effective data representations. ...
We hypothesized that we could improve both ML model explainability, and predictive accuracy, by including additional background knowledge in the learning process using a hybrid RL approach. ...
doi:10.1007/978-3-030-61527-7_25
fatcat:57l4nemeqrdbrc6av7fl2pdlxq
Teaching the bioinformatics of signaling networks: an integrated approach to facilitate multi-disciplinary learning
2013
Briefings in Bioinformatics
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Here, we present an outline of a university bioinformatics course with four sample practices to demonstrate how signaling network studies can integrate biochemistry, genetics, cell biology and network ...
bioinformation can become obsolete in a few years. ...
The structure of the presented course was based on a full-semester course of Eszter Ari and T.K. We thank the technical help of Janos Kubisch in the preparation of the manuscript. ...
doi:10.1093/bib/bbt024
pmid:23640570
fatcat:juk4nabsbrfsfgbulp2wr2mlhi
Learning in Clausal Logic: A Perspective on Inductive Logic Programming
[chapter]
2002
Lecture Notes in Computer Science
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Inductive logic programming is a form of machine learning from examples which employs the representation formalism of clausal logic. ...
, are still in use today. ...
Part of the material in Sections 2-4 is based on a tutorial given by the first author at the First International Conference on Computational Logic (CL-2000). ...
doi:10.1007/3-540-45628-7_17
fatcat:i24bt6wv65crjjfybhcm7uwda4
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