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Graph Representation Learning in Biomedicine
[article]
2022
arXiv
pre-print
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representation learning on graphs, explain its current successes and limitations, and even inform future advancements. ...
We also capture the breadth of ways in which representation learning has dramatically improved the state-of-the-art in biomedical machine learning. ...
Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funders. ...
arXiv:2104.04883v3
fatcat:lrhxlztborbylazvdfmaxk5zem
Taxonomy of Benchmarks in Graph Representation Learning
[article]
2022
arXiv
pre-print
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Consequently, our taxonomy can aid in selection and development of adequate graph benchmarks, and better informed evaluation of future GNN methods. ...
Finally, our approach and implementation in package are extendable to multiple graph prediction task types and future datasets. ...
as benchmarks in graph representation learning. ...
arXiv:2206.07729v4
fatcat:cdyavyceybayvoiahrggiwbyke
Modern Deep Learning in Bioinformatics
2020
Journal of Molecular Cell Biology
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We will focus on modern DL, the ongoing trends and future directions of the principled DL field, and postulate new and major applications in bioinformatics. ...
Deep learning (DL) has shown explosive growth in its application to bioinformatics and has demonstrated thrillingly promising power to mine the complex relationship hidden in large-scale biological and ...
modern DL, the ongoing trends and future directions of the principled DL field, and postulate new and major applications in bioinformatics. ...
doi:10.1093/jmcb/mjaa030
pmid:32573721
pmcid:PMC7883817
fatcat:5oq7sfusyvgsjoznjsrxitd7ha
Application of Sparse Representation in Bioinformatics
2021
Frontiers in Genetics
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This article reviews the development of sparse representation, and explains its applications in bioinformatics, namely the use of low-rank representation matrices to identify and study cancer molecules ...
Inspired by L1-norm minimization methods, such as basis pursuit, compressed sensing, and Lasso feature selection, in recent years, sparse representation shows up as a novel and potent data processing method ...
As well as thanks to the guidance of the tutor and the joint efforts of other authors, the success of this article is the result of everyone's joint efforts. ...
doi:10.3389/fgene.2021.810875
pmid:34976030
pmcid:PMC8715914
fatcat:fgioonmcffd7bftmvpoghbqucy
Bioinformatics and Medicine in the Era of Deep Learning
[article]
2018
arXiv
pre-print
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Deep learning methods are ideally suited to large-scale data and, therefore, they should be ideally suited to knowledge discovery in bioinformatics and biomedicine at large. ...
In this brief paper, we review key aspects of the application of deep learning in bioinformatics and medicine, drawing from the themes covered by the contributions to an ESANN 2018 special session devoted ...
Some future trends and challenges The application of DL methods to problems in biomedicine and bioinformatics is a many-faceted problem. ...
arXiv:1802.09791v1
fatcat:jq54e5rjbfgznkv3tenam35lzq
Network Visualization for Integrative Bioinformatics
[chapter]
2013
Approaches in Integrative Bioinformatics
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Then, the state of the art in network visualization for the life sciences is presented together with a discussion of standards for the graphical representation of cellular networks and biological processes ...
Approaches to investigate biological processes have been of strong interest in the past few years and are the focus of several research areas like systems biology. ...
There are some improvements of the general force-based method to consider application-specific requirements such as the representation of subcellular locations. ...
doi:10.1007/978-3-642-41281-3_7
fatcat:reo437khrndrvmpjs4o5s5rt6m
Graph Neural Networks and Their Current Applications in Bioinformatics
2021
Frontiers in Genetics
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Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space, perform particularly well in various tasks that process graph structure data. ...
We believe that GNNs are potentially an excellent method that solves various biological problems in bioinformatics research. ...
LLiu and M-JT contributed to formal analysis and writing -review and editing. X-MZ contributed to the investigation and data curation. ...
doi:10.3389/fgene.2021.690049
pmid:34394185
pmcid:PMC8360394
fatcat:4p55ap6sivcy7h6dpne5fut6lu
Patient-specific modelling & bioinformatics in PPPM
2011
The EPMA Journal
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is represented in the same way in terms of its attributes such as id, timing, code, value, method and status, as well as standard representation of clinical statements (e.g., observation of gall bladder ...
as a combinatorial optimization problem (as the task on the graph) and was solved, using algorithmic scheme for the method of branches and boundaries using own designed phantoms. ...
doi:10.1007/s13167-011-0123-9
fatcat:5usxhyh3jbanzgitigo4d3kouq
An Online Bioinformatics Curriculum
2012
PLoS Computational Biology
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Online learning initia- ...
in R and more advanced applications to bioinformatics such as microarray analysis. ...
Basic elements of the theory are important in machine learning approaches to data mining and appear frequently in bioinformatics tools and algorithms, including sequence motif analysis and many other applications ...
doi:10.1371/journal.pcbi.1002632
pmid:23028269
pmcid:PMC3441465
fatcat:n2ckzvbcf5fqfnw6vm4t7p3goi
Bioinformatics with soft computing
2006
IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)
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Soft computing is gradually opening up several possibilities in bioinformatics, especially by generating low-cost, lowprecision (approximate), good solutions. ...
Genomic sequence, protein structure, gene expression microarrays, and gene regulatory networks are some of the application areas described. ...
The data objects in KEGG are represented as graphs, and various computational methods are developed to detect graph features that can be related to biological functions. ...
doi:10.1109/tsmcc.2006.879384
fatcat:owim7m6genf6xc7s2bbhjuz7gu
Bioinformatics: perspectives for the future
2004
Genetics and Molecular Research
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I give here a very personal perspective of Bioinformatics and its future, starting by discussing the origin of the term (and area) of bioinformatics and proceeding by trying to foresee the development ...
However, it is expected that some of the questions and trends that are identified will motivate discussions during the IcoBiCoBi round table (with the same name as this article) and perhaps provide a more ...
Such trends, allied to the inherent potential of graphs for representing virtually any discrete structure (including trees, vectors, lists, queues, and so on), as well as the possibility to run the quite ...
pmid:15688322
fatcat:2wjd5z5qtfbc7jfjdh66f2m2lq
A Data Science Approach to Bioinformatics
2021
International Journal for Research in Applied Science and Engineering Technology
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CADD procedures are so much dependent on the tools of bioinformatics, databases & applications. ...
These techniques use machine learning, linear regression, neural nets or other statistical methods to derive predictive binding affinity equations. ...
outliers or activity cliffs. 5) Validation of QSAR model performance. 6) Applicability in a real-world setting. ...
doi:10.22214/ijraset.2021.37221
fatcat:yuibbaqx7zec5plg7bykcwmeau
A Survey of Scholarly Literature Describing the Field of Bioinformatics Education and Bioinformatics Educational Research
2014
CBE - Life Sciences Education
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pedagogical approaches and methods of delivery for conveying bioinformatics concepts and skills; and 4) assessment results on the impact of these programs, approaches, and methods in students' attitudes ...
or learning. ...
a generally low representation of bioinformatics-related content in science standards Student stimulation to learn Increased stimulation on students' activities in bioinformatics learning based on proper ...
doi:10.1187/cbe.13-10-0193
pmid:25452484
pmcid:PMC4255348
fatcat:2uilrbzzzja4nmsetfzw6mbkau
Biological network analysis with deep learning
2020
Briefings in Bioinformatics
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One major trend in the field is to use deep learning for this goal and, more specifically, to use methods that work with networks, the so-called graph neural networks (GNNs). ...
We then discuss domains in bioinformatics in which graph neural networks are frequently being applied at the moment, such as protein function prediction, protein–protein interaction prediction and in silico ...
Funding This work was supported in part from the Alfried Krupp Prize for Young University Teachers of the Alfried Krupp von Bohlen und Halbach-Stiftung (K.B.) and in part from the European Union's Horizon ...
doi:10.1093/bib/bbaa257
pmid:33169146
pmcid:PMC7986589
fatcat:x7salmmidjei3og6ripsizkbam
Neural networks and machine learning in bioinformatics - theory and applications
2006
The European Symposium on Artificial Neural Networks
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Within this rather wide area we focus on neural networks and machine learning related approaches in bioinformatics with particular emphasis on integrative research against the background of the above mentioned ...
Despite of a high number of techniques specifically dedicated to bioinformatics problems as well as many successful applications, we are in the beginning of a process to massively integrate the aspects ...
In addition, the first author wishes to thank Andrea Matros for the valuable support. ...
dblp:conf/esann/SeiffertHKV06
fatcat:jb7m65sxrjcovazeu4fowmv57m
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