2019-1-31 · Gene prediction by computational methods for finding the location of protein coding regions is one of the essential issues in bioinformatics. Gene prediction basically means locating genes along a genome. Also called gene finding it refers to the process of identifying the regions of genomic DNA that encode genes.
2020-11-30 · The homology-based essential gene prediction methods rely on the fact that essential genes are less likely to evolve tend to remain conserved and are often shared by distantly related organisms. Essential genes have been identified by comparative genomic analysis in different bacterial species such as Mycoplasma 9 Liberibacter 10 Plasmodium falciparum 11 and Brucella spp .
2013-9-5 · Computational gene prediction is a prerequisite for detailed functional annotation of genes and genomes. The process includes detection of the location of open reading frames (ORFs) and delineation of the structures of introns as well as exons if the genes of interest are of eukaryotic origin. The ultimate goal is to describe all the genes
2019-1-1 · In this context alternative informatic methods for essential gene prediction using features derived directly from sequence data would be advantageous given the increasing availability of genomes and predicted proteomes. Therefore showing that it is possible to predict essential genes within and among model species using ML algorithms
2020-1-1 · In general essential gene prediction can base on intrinsic features from nucleotide or protein sequences (e.g. GC content codon usage protein length) and combinations of both . Intrinsic in this context denotes features which can be directly derived from DNA and protein sequences.
2021-3-16 · 109 essential genes in insects may reduce the time and resources spent to detect and characterize genes 110 playing roles in core processes of insect biology. Furthermore the prediction of essential genes that 111 occur only in insects or in specific groups within insects such as lineages containing insect pests and
2012-6-29 · Identification of Essential Genes and Operons in B. cenocepacia. While bacterial strains of the same genus often differ greatly in the composition of their genomes they usually share a set of well-conserved essential genes .We therefore reasoned that the core genome identified should mainly consist of genes that are essential for growth and survival of members of the Burkholderiales.
2012-6-29 · Identification of Essential Genes and Operons in B. cenocepacia. While bacterial strains of the same genus often differ greatly in the composition of their genomes they usually share a set of well-conserved essential genes .We therefore reasoned that the core genome identified should mainly consist of genes that are essential for growth and survival of members of the Burkholderiales.
2016-3-8 · Prediction methods for essential genes and proteins often use supervised classification methods to build a model based on a variety of features related to gene and protein essentiality. Usually most of the known essential and nonessential genes and proteins in an organism are used as training data and some genes and proteins are left out as
Essential genes are absolutely required for the survival of an organism and are considered as the foundation of life. Geptop is a webserver which first provides a platform to detect essential gene sets over bacterial species by comparing orthology and phylogeny of query protein sets with essential gene datasets determined experimentally (from DEG database).
2017-1-31 · With respect to essential gene prediction in bacteria we integrated the orthology and phylogenetic information and subsequently developed a universal tool named Geptop (Wei et al. 2013) which has shown the highest accuracy among all state-of-the-art algorithms. Some studies have focused on essential gene prediction in eukaryotic genomes.
Investigation of essential genes is significant to comprehend the minimal gene sets of cell and discover potential drug targets. In this study a novel approach based on multiple homology mapping and machine learning method was introduced to predict essential genes. We focused on 25 bacteria which have characterized essential genes. The predictions yielded the highest area under receiver
2021-1-1 · The concordance of a gene s abundance change pattern between the transcriptional and translational levels during spermatogenesis was estimated by Pearson correlation which was based on the scaled CPMs and LFQs in each corresponding substage. Machine Learning for the Prediction of Meiosis-Essential Proteins
2020-8-18 · DeepHE is a deep learning framework for essential gene prediction based on sequence data and PPI network. Requirements. The code is based on python 3. It depends on several python packages such as numpy keras scikit-learn networkx pandas tensorflow or theano node2vec gensim.
Essential gene prediction helps to find minimal genes indispensable for the survival of any organism. Machine learning (ML) algorithms have been useful for the prediction of gene essentiality. However currently available ML pipelines perform poorly for organisms with limited experimental data. The objective is the development of a new ML pipeline to help in the annotation of essential genes
2017-1-31 · With respect to essential gene prediction in bacteria we integrated the orthology and phylogenetic information and subsequently developed a universal tool named Geptop (Wei et al. 2013) which has shown the highest accuracy among all state-of-the-art algorithms. Some studies have focused on essential gene prediction in eukaryotic genomes.
Machine learning approach to gene essentiality prediction a review. Essential genes are critical for the growth and survival of any organism. The machine learning approach complements the experimental methods to minimize the resources required for essentiality assays.
Towards the prediction of essential genes by integration of network topology cellular localization and biological process information. BMC Bioinformatics 2009. Ney Lemke. Download PDF. Download Full PDF Package. This paper. A short summary of this paper. 37 Full PDFs related to this paper.
2021-1-1 · The concordance of a gene s abundance change pattern between the transcriptional and translational levels during spermatogenesis was estimated by Pearson correlation which was based on the scaled CPMs and LFQs in each corresponding substage. Machine Learning for the Prediction of Meiosis-Essential Proteins
2013-11-9 · Essential genes are indispensable for the survival of living entities. They are the cornerstones of synthetic biology and are potential candidate targets for antimicrobial and vaccine design. Here we describe the Cluster of Essential Genes (CEG) database which contains clusters of orthologous essential genes. Based on the size of a cluster users can easily decide whether an essential gene
2021-1-1 · The concordance of a gene s abundance change pattern between the transcriptional and translational levels during spermatogenesis was estimated by Pearson correlation which was based on the scaled CPMs and LFQs in each corresponding substage. Machine Learning for the Prediction of Meiosis-Essential Proteins
Systematic comparison of the CRISPR-Cas9 and shRNA gene essentiality profiles showed the limitation of relying on a single technique to identify cancer essential genes. The CES method provides an integrated framework to leverage both genetic screening techniques as well as molecular feature data to determine gene essentiality more accurately for cancer cells.
2021-3-1 · At present essential gene prediction based on machine learning methods can achieve an average the area under the receiving operating characteristics curve (AUC) of 0.75–0.9 for intra-organism and an average AUC of 0.69–0.8 for cross-organism prediction.These results show that prediction methods based on machine learning have the ability
2009-11-28 · Essential gene prediction by MHS was validated through a jackknife methodology. For each organism within DEG the ability of the MHS to place experimentally validated essential genes at the top of a ranked genome was evaluated. All graphs correspond to the schematic found in the upper left. The X-axis represents the ranked genome of the
Conclusions FWM can remarkably improve the accuracy of essential gene prediction and may be used as an alternative method for other classification work. This method can contribute substantially to the knowledge of the minimum gene sets required for living organisms and the discovery of new drug targets.
2017-1-31 · With respect to essential gene prediction in bacteria we integrated the orthology and phylogenetic information and subsequently developed a universal tool named Geptop (Wei et al. 2013) which has shown the highest accuracy among all state-of-the-art algorithms. Some studies have focused on essential gene prediction in eukaryotic genomes.
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