Deep transfer learning for automated liver cancer gene recognition using spectrogram images of digitized DNA sequences

2022 ◽  
Vol 72 ◽  
pp. 103317
Author(s):  
Bihter Das ◽  
Suat Toraman
Keyword(s):  
Liver Cancer ◽  
Transfer Learning ◽  
Dna Sequences ◽  
Cancer Gene ◽  
Gene Recognition

scholarly journals Predicting Epigenomic Functions of Genetic Variants in the Context of Neurodevelopment via Deep Transfer Learning

2021 ◽  
Author(s):  
Boqiao Lai ◽  
Sheng Qian ◽  
Hanwen Zhang ◽  
Siwei Zhang ◽  
Alena Kozlova ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Dna Sequences ◽  
Adult Brain ◽  
Training Data ◽  
Candidate Snps ◽  
Learning Framework ◽  
Cellular Models ◽  
Link Type ◽  
Functional Variants ◽  
Genome Wide

AbstractDecoding the regulatory effects of non-coding variants is a key challenge in understanding the mechanisms of gene regulation as well as the genetics of common diseases. Recently, deep learning models have been introduced to predict genome-wide epigenomic profiles and effects of DNA variants, in various cellular contexts, but they were often trained in cell lines or bulk tissues that may not be related to phenotypes of interest. This is particularly a challenge for neuropsychiatric disorders, since the most relevant cell and tissue types are often missing in the training data of such models.To address this issue, we introduce a deep transfer learning framework termed MetaChrom that takes advantage of both a reference dataset - an extensive compendium of publicly available epigenomic data, and epigenomic profiles of cell types related to specific phenotypes of interest. We trained and evaluated our model on a comprehensive set of epigenomic profiles from fetal and adult brain, and cellular models representing early neurodevelopment. MetaChrom predicts these epigenomic features with much higher accuracy than previous methods, and than models without the use of reference epigenomic data for transfer learning. Using experimentally determined regulatory variants from iPS cell-derived neurons, we show that MetaChrom predicts functional variants more accurately than existing non-coding variant scoring tools. By combining genome-wide association study (GWAS) data with MetaChrom predictions, we prioritized 31 SNPs for Schizophrenia (SCZ). These candidate SNPs suggest potential risk genes of SCZ and the biological contexts where they act.In summary, MetaChrom is a general transfer learning framework that can be applied to the study of regulatory functions of DNA sequences and variants in any disease-related cell or tissue types. The software tool is available at https://github.com/bl-2633/MetaChrom and a prediction web server is accessible at https://metachrom.ttic.edu/.

scholarly journals Pro-Frame: similarity-based gene recognition in eukaryotic DNA sequences with errors

2001 ◽  
Vol 17 (1) ◽  
pp. 13-15 ◽  
Author(s):  
A. A. Mironov ◽  
P. S. Novichkov ◽  
M. S. Gelfand
Keyword(s):  
Dna Sequences ◽  
Gene Recognition ◽  
Eukaryotic Dna

scholarly journals Evaluating the Consistency of Gene Methylation in Liver Cancer Using Bisulfite Sequencing Data

2021 ◽  
Vol 9 ◽  
Author(s):  
Xubin Zheng ◽  
Qiong Wu ◽  
Haonan Wu ◽  
Kwong-Sak Leung ◽  
Man-Hon Wong ◽  
...  
Keyword(s):  
Liver Cancer ◽  
Dna Sequences ◽  
Bisulfite Sequencing ◽  
Differentially Methylated Region ◽  
Sequencing Data ◽  
Reduced Representation ◽  
Targeted Bisulfite Sequencing ◽  
Differentially Methylated Genes ◽  
Genome Bisulfite Sequencing ◽  
Bisulfite Sequencing Data

Bisulfite sequencing is considered as the gold standard approach for measuring DNA methylation, which acts as a pivotal part in regulating a variety of biological processes without changes in DNA sequences. In this study, we introduced the most prevalent methods for processing bisulfite sequencing data and evaluated the consistency of the data acquired from different measurements in liver cancer. Firstly, we introduced three commonly used bisulfite sequencing assays, i.e., reduced-representation bisulfite sequencing (RRBS), whole-genome bisulfite sequencing (WGBS), and targeted bisulfite sequencing (targeted BS). Next, we discussed the principles and compared different methods for alignment, quality assessment, methylation level scoring, and differentially methylated region identification. After that, we screened differential methylated genes in liver cancer through the three bisulfite sequencing assays and evaluated the consistency of their results. Ultimately, we compared bisulfite sequencing to 450 k beadchip and assessed the statistical similarity and functional association of differentially methylated genes (DMGs) among the four assays. Our results demonstrated that the DMGs measured by WGBS, RRBS, targeted BS and 450 k beadchip are consistently hypo-methylated in liver cancer with high functional similarity.

scholarly journals Shedding Light on Microbial Dark Matter with A Universal Language of Life

2020 ◽  
Author(s):  
A Hoarfrost ◽  
A Aptekmann ◽  
G Farfañuk ◽  
Y Bromberg
Keyword(s):  
Dark Matter ◽  
Deep Learning ◽  
Open Source ◽  
Transfer Learning ◽  
Dna Sequences ◽  
Biological Data ◽  
Fine Tuning ◽  
Microbial Genomes ◽  
Universal Language ◽  
Reference Databases

AbstractThe majority of microbial genomes have yet to be cultured, and most proteins predicted from microbial genomes or sequenced from the environment cannot be functionally annotated. As a result, current computational approaches to describe microbial systems rely on incomplete reference databases that cannot adequately capture the full functional diversity of the microbial tree of life, limiting our ability to model high-level features of biological sequences. The scientific community needs a means to capture the functionally and evolutionarily relevant features underlying biology, independent of our incomplete reference databases. Such a model can form the basis for transfer learning tasks, enabling downstream applications in environmental microbiology, medicine, and bioengineering. Here we present LookingGlass, a deep learning model capturing a “universal language of life”. LookingGlass encodes contextually-aware, functionally and evolutionarily relevant representations of short DNA reads, distinguishing reads of disparate function, homology, and environmental origin. We demonstrate the ability of LookingGlass to be fine-tuned to perform a range of diverse tasks: to identify novel oxidoreductases, to predict enzyme optimal temperature, and to recognize the reading frames of DNA sequence fragments. LookingGlass is the first contextually-aware, general purpose pre-trained “biological language” representation model for short-read DNA sequences. LookingGlass enables functionally relevant representations of otherwise unknown and unannotated sequences, shedding light on the microbial dark matter that dominates life on Earth.AvailabilityThe pretrained LookingGlass model and the transfer learning-derived models demonstrated in this paper are available in the LookingGlass release v1.01. The open source fastBio Github repository and python package provides classes and functions for training and fine tuning deep learning models with biological data2. Code for reproducing analyses presented in this paper are available as an open source Github repository3.

Research Findings

2003 ◽  
Vol 07 (15) ◽  
pp. 901-906
Keyword(s):  
Liver Cancer ◽  
Cancer Treatment ◽  
Human Gene ◽  
Cancer Gene ◽  
Transformation Mechanism ◽  
Zebrafish Model ◽  
Research Findings ◽  
Gene Study

Major Breakthrough in Cancer Treatment. Zebrafish Model for Leukemia Research. Singapore Scientists Embark on Human Gene Study with the Zebrafish. Singapore May Soon Have the Answer to the Cure for SARS. Novel Liver Cancer Gene Identified. Orchid’s Unique Nutrient Transformation Mechanism Revealed.

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