Results 1 to 10 of about 9,695,054 (384)
Explainable Multilayer Graph Neural Network for cancer gene prediction. [PDF]
Bioinformatics, 2023 Motivation The identification of cancer genes is a critical yet challenging problem in cancer genomics research. Existing computational methods, including deep graph neural networks, fail to exploit the multilayered gene–gene interactions or provide ...
Chatzianastasis M +2 more
europepmc +3 more sources
FragGeneScanRs: faster gene prediction for short reads. [PDF]
BMC Bioinformatics, 2022 FragGeneScan is currently the most accurate and popular tool for gene prediction in short and error-prone reads, but its execution speed is insufficient for use on larger data sets. The parallelization which should have addressed this is inefficient. Its
Van der Jeugt F, Dawyndt P, Mesuere B.
europepmc +2 more sources
Evaluation of Different Gene Prediction Tools in Coccidioides immitis [PDF]
Journal of Fungi, 2023 Gene prediction is required to obtain optimal biologically meaningful information from genomic sequences, but automated gene prediction software is imperfect.
Theo N. Kirkland +2 more
doaj +2 more sources
Lung adenocarcinoma-related target gene prediction and drug repositioning [PDF]
Frontiers in Pharmacology, 2022 Lung cancer is the leading cause of cancer deaths globally, and lung adenocarcinoma (LUAD) is the most common type of lung cancer. Gene dysregulation plays an essential role in the development of LUAD.
Rui Xuan Huang +11 more
doaj +2 more sources
An Ovarian Cancer Susceptible Gene Prediction Method Based on Deep Learning Methods [PDF]
Frontiers in Cell and Developmental Biology, 2021 Ovarian cancer (OC) is one of the most fatal diseases among women all around the world. It is highly lethal because it is usually diagnosed at an advanced stage which may reduce the survival rate greatly.
Lu Ye +4 more
doaj +2 more sources
A benchmark study of ab initio gene prediction methods in diverse eukaryotic organisms
BMC Genomics, 2020 Background The draft genome assemblies produced by new sequencing technologies present important challenges for automatic gene prediction pipelines, leading to less accurate gene models.
Nicolas Scalzitti +4 more
doaj +2 more sources
Combining RNA-seq data and homology-based gene prediction for plants, animals and fungi
BMC Bioinformatics, 2018 Background Genome annotation is of key importance in many research questions. The identification of protein-coding genes is often based on transcriptome sequencing data, ab-initio or homology-based prediction.
Jens Keilwagen +4 more
doaj +2 more sources
Recent Advances in Network-based Methods for Disease Gene Prediction [PDF]
Briefings Bioinform., 2020 Disease-gene association through genome-wide association study (GWAS) is an arduous task for researchers. Investigating single nucleotide polymorphisms that correlate with specific diseases needs statistical analysis of associations. Considering the huge
S. Ata +5 more
semanticscholar +1 more source
Journal of Open Source Software, 2022 Improvements in sequencing technologies have seen the amount of available genomic data expand considerably over the last twenty years. One of the key steps for analysing is the prediction of protein-coding regions in genomic sequences, known as Open ...
M. Larralde
semanticscholar +1 more source
Semi-Supervised Pipeline for Autonomous Annotation of SARS-CoV-2 Genomes
Viruses, 2021 SARS-CoV-2 genomic sequencing efforts have scaled dramatically to address the current global pandemic and aid public health. However, autonomous genome annotation of SARS-CoV-2 genes, proteins, and domains is not readily accomplished by existing methods ...
Kristen L. Beck +9 more
doaj +1 more source