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Prediction of Drosophila melanogaster gene function using Support Vector Machines

Nicholas Mitsakakis, Zak Razak, Michael Escobar, J Timothy Westwood
2013 BioData Mining  
We have applied Support Vector Machines (SVM), a sigmoid fitting function and a stratified cross-validation approach to analyze a large microarray experiment dataset from Drosophila melanogaster in order  ...  For example, for Drosophila melanogaster, approximately 28% of the 14,029 predicted genes have no Gene Ontology (GO) term (either Molecular Function, Biological Process and/or Cellular Component) associated  ...  to parse data from Gene Ontology, Han Yan for making available the data used in the Yan et al. study [10] and William Noble (Departments of Genome Sciences and of  ... 
doi:10.1186/1756-0381-6-8 pmid:23547736 pmcid:PMC3669044 fatcat:vrvpvcyswzd77csa7pdhfy2cy4

Identification of Drosophila Promoter Using Positional Differential Matrix and Support Vector Machine from Sequence Data

Mohammad Shoyaib, Azizul Haque, Firoz Anwar, Taskeed Jabid, Syed Murtuza Baker, Haseena Khan, Mohammad Nurul Islam
2009 Plant Tissue Culture and Biotechnology  
This paper presents an algorithm for identifying Drosophila melanogaster promoter using differential positional frequency matrix between promoter and non-promoter sequences which shows maximum 90.36% tenfold  ...  Promoter region plays an important role in controlling gene expression of any living organism.  ...  Most of the available computational methods for core promoter prediction are based on solid machine learning techniques like probabilistic sequence models, Hidden Markov Model (HMM) or Support Vector Machines  ... 
doi:10.3329/ptcb.v18i2.3394 fatcat:t744epfncrcxhbznpuppnmvweu

iProteinDB: An Integrative Database of Drosophila Post-translational Modifications

Yanhui Hu, Richelle Sopko, Verena Chung, Marianna Foos, Romain A. Studer, Sean D. Landry, Daniel Liu, Leonard Rabinow, Florian Gnad, Pedro Beltrao, Norbert Perrimon
2018 G3: Genes, Genomes, Genetics  
At iProteinDB, scientists can view the PTM landscape for any Drosophila protein and identify predicted functional phosphosites based on a comparative analysis of data from closely-related Drosophila species  ...  Further, iProteinDB enables comparison of PTM data from Drosophila to that of orthologous proteins from other model organisms, including human, mouse, rat, Xenopus tropicalis, Danio rerio, and Caenorhabditis  ...  The DRSC is supported by National Institutes of Health (NIH) National Institute of General Medical Sciences grant R01 GM 067761 (to N.P.).  ... 
doi:10.1534/g3.118.200637 pmid:30397019 pmcid:PMC6325894 fatcat:xad7tcpf6fc3tleyotrbhy7dve

iProteinDB: an integrative database ofDrosophilapost-translational modifications [article]

Yanhui Hu, Richelle Sopko, Verena Chung, Romain A Studer, Sean D Landry, Daniel Liu, Leonard Rabinow, Florian Gnad, Pedro Beltrao, Norbert Perrimon
2018 bioRxiv   pre-print
At iProteinDB, scientists can view the PTM landscape for anyDrosophilaprotein and identify predicted functional phosphosites based on a comparative analysis of data from closely-relatedDrosophilaspecies  ...  to regulation and function.  ...  The DRSC is supported by National Institutes of Health (NIH) National Institute of General Medical Sciences grant R01 GM 067761 (to N.P.).  ... 
doi:10.1101/386268 fatcat:naoynqbaazd57iudu2jxylh6ma

Predicting expression divergence and its evolutionary parameters between single-copy genes in two species [article]

Antara Anika Piya, Michael DeGiorgio, Raquel Assis
2022 bioRxiv   pre-print
In particular, PiXi models gene expression evolution as an Ornstein-Uhlenbeck process, and overlays this model with multi-layer neural network, random forest, and support vector machine architectures for  ...  AbstractPredicting gene expression divergence and its evolutionary parameters is integral to understanding the emergence of new gene functions and associated traits.  ...  Support for multiple classes of local expression clusters in Drosophila melanogaster, but no evidence for gene order conservation. Genome Biol, 12:R23, 2011.  ... 
doi:10.1101/2022.07.13.499803 fatcat:h4wkiqid3vdh5kkjvk6cgtixqi

A Machine Learning Approach for Identifying Novel Cell Type–Specific Transcriptional Regulators of Myogenesis

Brian W. Busser, Leila Taher, Yongsok Kim, Terese Tansey, Molly J. Bloom, Ivan Ovcharenko, Alan M. Michelson, James W. Posakony
2012 PLoS Genetics  
We first assembled a small number of enhancers that are active in Drosophila melanogaster muscle founder cells (FCs) and other mesodermal cell types.  ...  Using phylogenetic profiling, we increased the number of enhancers by incorporating orthologous but divergent sequences from other Drosophila species.  ...  Gisselbrecht for the pWattB-GFP vector; and C. Sonnenbrot for technical assistance. Author Contributions  ... 
doi:10.1371/journal.pgen.1002531 pmid:22412381 pmcid:PMC3297574 fatcat:vid3twaowfdwpfvsjhz6cp6iwm

Discrimination of regulatory DNA by SVM on the basis of over- and under-represented motifs

Rene te Boekhorst, Irina I. Abnizova, Lorenz Wernisch
2008 The European Symposium on Artificial Neural Networks  
Using a new feature representation (the degree by which motifs are over-and under-represented) we demonstrate the remarkable power of this methodology in identifying regulatory regions of Drosophila melanogaster  ...  In this paper we apply three pattern recognition methods (support vector machine, cluster analysis and principal component analysis) to distinguish regulatory regions from coding and non-coding non regulatory  ...  The positive training set is a collection of 60 experimentally verified functional Drosophila melanogaster regulatory regions [8] located far from gene coding sequences and transcription start sites  ... 
dblp:conf/esann/BoekhorstAW08 fatcat:6elpi3pgkfeybky4ok5c2w4fjm

Annotating the Insect Regulatory Genome

Hasiba Asma, Marc S. Halfon
2021 Insects  
Comprehensive annotation of not only genes but also regulatory regions is critical for reaping the full benefits of this sequencing.  ...  We review here the methods being used to identify CRMs in both model and non-model insect species, and focus on two tools that we have developed, REDfly and SCRMshaw.  ...  REDfly and SCRMshaw both make use of the resources of the University at Buffalo Center for  ... 
doi:10.3390/insects12070591 pmid:34209769 pmcid:PMC8305585 fatcat:w56kekjdx5b5lnsbbsmhrmmbtq

Essential gene prediction in Drosophila melanogaster using machine learning approaches based on sequence and functional features

Olufemi Aromolaran, Thomas Beder, Marcus Oswald, Jelili Oyelade, Ezekiel Adebiyi, Rainer Koenig
2020 Computational and Structural Biotechnology Journal  
In this work, we employed machine learning to predict essential genes in Drosophila melanogaster.  ...  Genes are termed to be essential if their loss of function compromises viability or results in profound loss of fitness.  ...  Acknowledgments This work was supported by the Deutsche Forschungsgemeinschaft (https://www.dfg.de/) within the project KO 3678/5-1, and the German Federal Ministry of Education and Research (BMBF) within  ... 
doi:10.1016/j.csbj.2020.02.022 pmid:32257045 pmcid:PMC7096750 fatcat:fqtyq335xbfu7nl6rbdspi23qu

Comprehensive overview and assessment of miRNA target prediction tools in human and drosophila melanogaster [article]

Muniba Faiza, Khushnuma Tanveer, Saman Fatihi, Yonghua Wang, Khalid Raza
2017 arXiv   pre-print
Both Drosophila Melanogaster and Human supported miRNA target prediction tools have been evaluated separately to find out best performing tool for each of these two organisms.  ...  using experimentally validated high confident mature miRNAs and their targets for two organisms Human and Drosophila Melanogaster.  ...  Some part of the manuscript is written during this period.  ... 
arXiv:1711.01632v1 fatcat:hfpalqpqyjgbbhutj6tgvk52mm

A Genome-Wide Gene Function Prediction Resource for Drosophila melanogaster

Han Yan, Kavitha Venkatesan, John E. Beaver, Niels Klitgord, Muhammed A. Yildirim, Tong Hao, David E. Hill, Michael E. Cusick, Norbert Perrimon, Frederick P. Roth, Marc Vidal, Nicholas James Provart
2010 PLoS ONE  
Predicting gene functions by integrating large-scale biological data remains a challenge for systems biology. Here we present a resource for Drosophila melanogaster gene function predictions.  ...  Our model predicted GO terms and KEGG pathway memberships for Drosophila melanogaster genes with high accuracy, as affirmed by cross-validation, supporting literature evidence, and large-scale RNAi screens  ...  and Dr Paul Leyland for technical support on FlyBase data, Changyu Fan for assistance on computational resources, Anne-Ruxandra Carvunis and Samuel Pevzner for reading the manuscript.  ... 
doi:10.1371/journal.pone.0012139 pmid:20711346 pmcid:PMC2920829 fatcat:o2yowefp25cj7esemzczapu7oi

Gender Identification and Classification of Drosophila melanogaster Flies Using Machine Learning Techniques

Channabasava Chola, J. V. Bibal Benifa, D. S. Guru, Abdullah Y. Muaad, J. Hanumanthappa, Mugahed A. Al-antari, Hussain AlSalman, Abdu H. Gumaei, Osamah Ibrahim Khalaf
2022 Computational and Mathematical Methods in Medicine  
Then, machine learning (ML) classifiers such as support vector machines (SVM), Naive Bayes (NB), and K -nearest neighbour (KNN) are used to classify the Drosophila as male or female.  ...  Recently, several investigations have been conducted in Drosophila to study the functions of specific genes exist in the central nervous system, heart, liver, and kidney.  ...  The support extended by the technical staff Computational and Mathematical Methods in Medicine of Studies in Zoology, Manasagangotri, University of Mysore, India, for the creation of drosophila flies dataset  ... 
doi:10.1155/2022/4593330 pmid:35069782 pmcid:PMC8776435 fatcat:evarfltgi5fcbiu4u742pq2kpe

Drosophila melanogaster: A case study of a model genomic sequence and its consequences

M. Ashburner
2005 Genome Research  
We thank Hamid Bolouri for showing us the potential of Cytoscape. We apologize for any oversight in attribution resulting from space limitations.  ...  C.M.B. is supported by a USA Research Fellowship from the Royal Society. Work in M.A.'s laboratory is supported by an MRC Programme Grant to M.A. and Steve Russell.  ...  The Gene Ontology has provided not only a structured language to describe gene "function," but also tools for the prediction of gene function.  ... 
doi:10.1101/gr.3726705 pmid:16339363 fatcat:ejs2jxhp7nhkfhmfk6kwpv7m24

Combined use of feature engineering and machine-learning to predict essential genes in Drosophila melanogaster

Tulio L Campos, Pasi K Korhonen, Andreas Hofmann, Robin B Gasser, Neil D Young
2020 NAR Genomics and Bioinformatics  
Functional genomic investigations of the vinegar fly, Drosophila melanogaster, have unravelled the functions of numerous genes of this model species, but results from phenomic experiments can sometimes  ...  Here, we harnessed comprehensive genomic-phenomic datasets publicly available for D. melanogaster and a machine-learning-based workflow to predict essential genes of this fly.  ...  Normalized features were used to train each of six ML-models (GBM (Gradient Boosting Machine), GLM (Generalized Linear Model), NN (Neural Network--perceptron), Random Forest (RF), SVM (Support-Vector Machine  ... 
doi:10.1093/nargab/lqaa051 pmid:33575603 pmcid:PMC7671374 fatcat:x5sixw7yxvae7hqt2jdtx7izpm

Computational algorithms to predict Gene Ontology annotations

Pietro Pinoli, Davide Chicco, Marco Masseroli
2015 BMC Bioinformatics  
of new gene annotations, are very useful.  ...  Gene function annotations, which are associations between a gene and a term of a controlled vocabulary describing gene functional features, are of paramount importance in modern biology.  ...  In [5] , Barutcuoglu and colleagues used gene expression levels, obtained in microarray experiments, to train a Support Vector Machine (SVM) classifier for each gene annotation to a GO term, and enforced  ... 
doi:10.1186/1471-2105-16-s6-s4 pmid:25916950 pmcid:PMC4416163 fatcat:wr6znhkvr5cdzhzeu6w2mrl25u
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