Extracting inherent valuable knowledge from omics big data remains as a daunting problem in bioinformatics and computational biology. Deep learning, as an emerging branch from machine learning, has exhibited unprecedented performance in quite a few applications from academia and industry. We highlight the difference and similarity in widely utilized models in deep learning studies, through discussing their basic structures, and reviewing diverse applications and disadvantages. We anticipate the work can serve as a meaningful perspective for further development of its theory, algorithm and application in bioinformatic and computational biology
As a data-driven science, genomics largely utilizes machine learning to capture dependencies in data...
Deep neural networks represent, nowadays, the most effective machine learning technology in biomedic...
Computational analysis of high-throughput omics data, such as gene expressions, copy number alterati...
Many of the current scientific advances in the life sciences have their origin in the intensive use ...
Bioinformatics, an interdisciplinary area of biology and computer science, handles large and complex...
Many of the current scientific advances in the life sciences have their origin in the intensive use ...
Advancements in genomic research such as high-throughput sequencing techniques have driven modern ge...
Deep learning (DL) has shown unstable improvement in its application to bioinformatics and has displ...
Deep learning describes a class of machine learning algorithms that are capable of combining raw inp...
This research aims to review and evaluate the most relevant scientific studies about deep learning (...
Machine learning is a modern approach to problem-solving and task automation. In particular, machine...
: Deep learning has already revolutionised the way a wide range of data is processed in many areas o...
The 21st centuries were deemed to be the era of big data. Data driven research had become a necessit...
Deep learning depicts a class of AI calculations that are fit for joining crude contributions to lay...
In modern biomedicine, the role of computation becomes more crucial in light of the ever-increasing ...
As a data-driven science, genomics largely utilizes machine learning to capture dependencies in data...
Deep neural networks represent, nowadays, the most effective machine learning technology in biomedic...
Computational analysis of high-throughput omics data, such as gene expressions, copy number alterati...
Many of the current scientific advances in the life sciences have their origin in the intensive use ...
Bioinformatics, an interdisciplinary area of biology and computer science, handles large and complex...
Many of the current scientific advances in the life sciences have their origin in the intensive use ...
Advancements in genomic research such as high-throughput sequencing techniques have driven modern ge...
Deep learning (DL) has shown unstable improvement in its application to bioinformatics and has displ...
Deep learning describes a class of machine learning algorithms that are capable of combining raw inp...
This research aims to review and evaluate the most relevant scientific studies about deep learning (...
Machine learning is a modern approach to problem-solving and task automation. In particular, machine...
: Deep learning has already revolutionised the way a wide range of data is processed in many areas o...
The 21st centuries were deemed to be the era of big data. Data driven research had become a necessit...
Deep learning depicts a class of AI calculations that are fit for joining crude contributions to lay...
In modern biomedicine, the role of computation becomes more crucial in light of the ever-increasing ...
As a data-driven science, genomics largely utilizes machine learning to capture dependencies in data...
Deep neural networks represent, nowadays, the most effective machine learning technology in biomedic...
Computational analysis of high-throughput omics data, such as gene expressions, copy number alterati...