AIMC Topic: Sequence Analysis, DNA
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DeepHE: Accurately predicting human essential genes based on deep learning.
PLoS computational biology
Sep 16, 2020
Accurately predicting essential genes using computational methods can greatly reduce the effort in finding them via wet experiments at both time and resource scales, and further accelerate the process of drug discovery. Several computational methods ...
Accurate prediction of DNA N-methylcytosine sites via boost-learning various types of sequence features.
BMC genomics
Sep 11, 2020
BACKGROUND: DNA N4-methylcytosine (4mC) is a critical epigenetic modification and has various roles in the restriction-modification system. Due to the high cost of experimental laboratory detection, computational methods using sequence characteristic...
A machine learning approach to optimizing cell-free DNA sequencing panels: with an application to prostate cancer.
BMC cancer
Aug 28, 2020
BACKGROUND: Cell-free DNA's (cfDNA) use as a biomarker in cancer is challenging due to genetic heterogeneity of malignancies and rarity of tumor-derived molecules. Here we describe and demonstrate a novel machine-learning guided panel design strategy...
A machine learning framework to trace tumor tissue-of-origin of 13 types of cancer based on DNA somatic mutation.
Biochimica et biophysica acta. Molecular basis of disease
Aug 7, 2020
Carcinoma of unknown primary (CUP), defined as metastatic cancers with unknown cancer origin, occurs in 3-5 per 100 cancer patients in the United States. Heterogeneity and metastasis of cancer brings great difficulties to the follow-up diagnosis and ...
Predicting gene regulatory regions with a convolutional neural network for processing double-strand genome sequence information.
PloS one
Jul 23, 2020
With advances in sequencing technology, a vast amount of genomic sequence information has become available. However, annotating biological functions particularly of non-protein-coding regions in genome sequences without experiments is still a challen...
Cross-species regulatory sequence activity prediction.
PLoS computational biology
Jul 20, 2020
Machine learning algorithms trained to predict the regulatory activity of nucleic acid sequences have revealed principles of gene regulation and guided genetic variation analysis. While the human genome has been extensively annotated and studied, mod...
Genetic Variation
Gene Expression Regulation
Genome, Human
Species Specificity
Quantitative Trait Loci
Sequence Analysis, DNA
Models, Statistical
Mutation
Hepatocytes
Computational Biology
Epigenomics
Models, Genetic
Algorithms
Databases, Genetic
Animals
Mice
Humans
Genomics
Neural Networks, Computer
Machine Learning
Software
Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping.
Nature communications
Jul 15, 2020
Predicting effects of gene regulatory elements (GREs) is a longstanding challenge in biology. Machine learning may address this, but requires large datasets linking GREs to their quantitative function. However, experimental methods to generate such d...
A supervised learning framework for chromatin loop detection in genome-wide contact maps.
Nature communications
Jul 9, 2020
Accurately predicting chromatin loops from genome-wide interaction matrices such as Hi-C data is critical to deepening our understanding of proper gene regulation. Current approaches are mainly focused on searching for statistically enriched dots on ...
Evaluation of marker selection methods and statistical models for chronological age prediction based on DNA methylation.
Legal medicine (Tokyo, Japan)
Jul 1, 2020
In forensic investigation, retrieving biological information from DNA evidence is a promising field of interest. One of the applications is on the estimation of the age of the donor based on DNA methylation. A large number of studies focused on age p...
Predicting host taxonomic information from viral genomes: A comparison of feature representations.
PLoS computational biology
May 26, 2020
The rise in metagenomics has led to an exponential growth in virus discovery. However, the majority of these new virus sequences have no assigned host. Current machine learning approaches to predicting virus host interactions have a tendency to focus...
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