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Artificial neural networks for molecular sequence analysis

Cathy H. Wu
1997 Computers and Chemistry  
As a technique for computational analysis, neural network technology is very well suited for the analysis of molecular sequence data.  ...  Artificial neural networks provide a unique computing architecture whose potential has attracted interest from researchers across different disciplines.  ...  Design issues of neural network applications for molecular sequence analysis.  ... 
doi:10.1016/s0097-8485(96)00038-1 pmid:9415987 fatcat:ufxpibhykndhhd7kkh4gwuyyci

On the Investigation of Biological Phenomena through Computational Intelligence

Jyotsana Pandey
2014 Computational Biology and Bioinformatics  
This paper also presents an overview on the artificial neural network based computational intelligence technique to infer and analyze biological information from wide spectrum of complex problems .Bioinformatics  ...  This paper also presents an overview on the artificial neural network based computational intelligence technique to infer and analyze biological information from wide spectrum of complex problems .Bioinformatics  ...  Neural Network for Biological in-Formation Analysis Neural networks have several unique characteristics and advantages as tools for the molecular sequence analysis problem.  ... 
doi:10.11648/j.cbb.20140202.11 fatcat:6qe4h4o2m5bo5pk35or5gwet2i

Bioinformatics in neurocomputing framework

S.S. Ray, S. Bandyopadhyay, P. Mitra, S.K. Pal
2005 IEE Proceedings - Circuits Devices and Systems  
Different bioinformatics tasks like gene sequence analysis, gene finding, protein structure prediction and analysis, gene expression with microarray analysis and gene regulatory network analysis are described  ...  The relevance of intelligent systems and neural networks to these problems is mentioned. Different neural network based algorithms to address the aforesaid tasks are then presented.  ...  In [19] the quantitative similarity among tRNA gene sequences was acquired by analysis with an artificial neural network.  ... 
doi:10.1049/ip-cds:20045051 fatcat:mcu56pkgjbbcblspl46ft66ahe

Auto Covariance Combined with Artificial Neural Network for Predicting Protein-Protein Interactions

Juan Juan Li, Yue Hui Chen
2013 Advanced Materials Research  
We proposed an approach, by combining auto covariance with artificial neural network classifier, to predict PPIs.  ...  The artificial neural network (ANN). The neural network is an algorithm simulating the process of human neurons.  ...  The classifier chosen is artificial neural network of three layers.  ... 
doi:10.4028/www.scientific.net/amr.765-767.1622 fatcat:mus2iact4jftto65uryyz5eaca

Seroprevalence of human cytomegalovirus infection in Singapore

H.N. Leong, B.H. Tan, S.H. Lim, K.P. Chan
2010 International Journal of Infectious Diseases  
The current study is designed to find the algorithm for genotype prediction using the restriction site information in combination with artificially created neural network.  ...  The pattern can be used for further correlation analysis with known and unknown viral sequence.  ...  The current study is designed to find the algorithm for genotype prediction using the restriction site information in combination with artificially created neural network.  ... 
doi:10.1016/j.ijid.2010.02.673 fatcat:3szwbthivreblgru57qpoudtay

Neural Networks for Molecular Sequence Classification [chapter]

Cathy H. Wu
1994 The Protein Folding Problem and Tertiary Structure Prediction  
Two artificial neural systems have been implemented on a Cray supercomputer for rapid protein/nucleic acid sequence classifications.  ...  A neural network classification method has been developed as an alternative approach to the search/ organization problem of large molecular databases.  ...  The author also wishes to acknowledge the computer system support of the Center for High Performance Computing of the University of Texas System.  ... 
doi:10.1007/978-1-4684-6831-1_9 fatcat:rbqpp6oumrcgbfidfe3wzocfwy

Neural networks and genetic algorithms in drug design

L TERFLOTH
2001 Drug Discovery Today  
A variety of artificial neural network methods have been developed. Here, we single out some of the more prominent methods. For a list of online resources for neural networks, see Box 1.  ...  In contrast to classical statistical methods such as regression analysis or partial least squares analysis (PLS), artificial neural networks enable the investigation of complex, nonlinear relationships  ...  In contrast to classical statistical methods such as regression analysis or partial least squares analysis (PLS), artificial neural networks enable the investigation of complex, nonlinear relationships  ... 
doi:10.1016/s1359-6446(01)00173-8 fatcat:5jt3r4bgfvbe5b6phd4lidln4a

Emergence of new disease – how can artificial intelligence help?

Yurim Park, Daniel Casey, Indra Joshi, Jiming Zhu, Feng Cheng
2020 Trends in Molecular Medicine  
Advances in artificial intelligence (AI) allow for rapid processing and analysis of massive and complex data.  ...  Advances in artificial intelligence (AI) allow for rapid processing and analysis of massive and complex data.  ...  The program trains a neural network to predict the distances between protein residues within a sequence.  ... 
doi:10.1016/j.molmed.2020.04.007 pmid:32418724 pmcid:PMC7196393 fatcat:fb3mlwkgine4bczrtzcp2jlmje

Relevant Applications of Generative Adversarial Networks in Drug Design and Discovery: Molecular De Novo Design, Dimensionality Reduction, and De Novo Peptide and Protein Design

Eugene Lin, Chieh-Hsin Lin, Hsien-Yuan Lane
2020 Molecules  
A growing body of evidence now suggests that artificial intelligence and machine learning techniques can serve as an indispensable foundation for the process of drug design and discovery.  ...  In this review, we focus on the latest developments for three particular arenas in drug design and discovery research using deep learning approaches, such as generative adversarial network (GAN) frameworks  ...  For example, artificial neural networks can be utilized to establish the hierarchical representation [21, 22] .  ... 
doi:10.3390/molecules25143250 pmid:32708785 fatcat:rrik322g6vbetaubwjb3rtvajm

Application of artificial neural networks to identify equilibration in computer simulations

Mitchell H Leibowitz, Evan D Miller, Michael M Henry, Eric Jankowski
2017 Journal of Physics, Conference Series  
We train a twoneuron artificial network to distinguish the correlated and uncorrelated sequences.  ...  We find that this simple network is good enough for > 98% accuracy in identifying exponentially-decaying energy trajectories from molecular simulations.  ...  The strategy we use here to construct a well-defined heuristic for identifying u * is to train an artificial neural network for automating the decision of whether a sequence of observables is representative  ... 
doi:10.1088/1742-6596/921/1/012013 fatcat:sl46ucapr5do7dhozj3aiulxfy

Analysis of tRNA Gene Sequences by Neural Network

JIAN SUN, WEN-YUAN SONG, LI-HUANG ZHU, RUN-SHENG CHEN
1995 Journal of Computational Biology  
All of our results showed the efficiency of the artificial neural network method in the sequence analysis for biological molecules.  ...  The quantitative similarity among tRNA gene sequences was acquired by analysis with an artificial neural network.  ...  Since 1989, we have conducted research on the use of neural network for structure prediction and sequence analysis of biomacromolecules.  ... 
doi:10.1089/cmb.1995.2.409 pmid:8521271 fatcat:q2ndgbuhbzfbdcijbxawofdk4u

The First International Conference on Intelligent Systems for Molecular Biology

David B. Searls, Jude W. Shavlik, Lawrence Hunter
1994 The AI Magazine  
be predicted with gent Systems for Molecular Biology sequence analysis.  ...  analysis at a wide variety of involved in the Human Genome Pro- The emphasis of the first day was on levels, ranging from neural networks ject).  ... 
doi:10.1609/aimag.v15i1.1082 dblp:journals/aim/SearlsSH94 fatcat:vdvs5hkb7bdr3gfecx6tahn4ke

Learning the Relationship between the Primary Structure of HIV Envelope Glycoproteins and Neutralization Activity of Particular Antibodies by Using Artificial Neural Networks

Cătălin Buiu, Mihai Putz, Speranta Avram
2016 International Journal of Molecular Sciences  
This paper presents a first approach to learning these dependencies using an artificial feedforward neural network which is trained to learn from experimental data.  ...  The results presented here demonstrate that the trained neural network is able to generalize on new viral strains and to predict reliable values of neutralizing activities of given antibodies against HIV  ...  Artificial neural networks (ANN) are prominent machine learning algorithms which are inspired by the structure and functioning of biological neural networks.  ... 
doi:10.3390/ijms17101710 pmid:27727189 pmcid:PMC5085742 fatcat:zqjxxt6xprbk7mkmv6aimfx3ae

Geometric Deep Learning on Molecular Representations [article]

Kenneth Atz, Francesca Grisoni, Gisbert Schneider
2021 arXiv   pre-print
Geometric deep learning (GDL), which is based on neural network architectures that incorporate and process symmetry information, has emerged as a recent paradigm in artificial intelligence.  ...  This review provides an overview of current challenges and opportunities, and presents a forecast of the future of GDL for molecular sciences.  ...  Introduction Recent advances in deep learning, which is an instance of artificial intelligence (AI) based on neural networks [1, 2] , have led to numerous applications in the molecular sciences, e.g.,  ... 
arXiv:2107.12375v4 fatcat:sgxlqdxiavbinly4s3zthysxbq

Peptide design by artificial neural networks and computer-based evolutionary search

G. Schneider, W. Schrodl, G. Wallukat, J. Muller, E. Nissen, W. Ronspeck, P. Wrede, R. Kunze
1998 Proceedings of the National Academy of Sciences of the United States of America  
relationship by an artificial neural network, and (v) de novo design by a computer-based evolutionary search in sequence space using the trained neural network as the fitness function.  ...  A set of 90 peptides was synthesized and tested to provide training data for neural network development.  ...  Karin Karczewski, Monika Wegner, Holle Schmidt, and Manuela Dierenfeld are thanked for excellent experimental support. Uwe Hobohm is thanked for inspiring discussions.  ... 
doi:10.1073/pnas.95.21.12179 pmid:9770460 pmcid:PMC22805 fatcat:df2w37fxi5dzjcqnypimxtbi5q
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