AIMC Topic:
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Reinforcement Learning for Bioretrosynthesis.

ACS synthetic biology
Metabolic engineering aims to produce chemicals of interest from living organisms, to advance toward greener chemistry. Despite efforts, the research and development process is still long and costly, and efficient computational design tools are requi...

More Agility to Semantic Similarities Algorithm Implementations.

International journal of environmental research and public health
Algorithms for measuring semantic similarity between Gene Ontology (GO) terms has become a popular area of research in bioinformatics as it can help to detect functional associations between genes and potential impact to the health and well-being of ...

SpliceFinder: ab initio prediction of splice sites using convolutional neural network.

BMC bioinformatics
BACKGROUND: Identifying splice sites is a necessary step to analyze the location and structure of genes. Two dinucleotides, GT and AG, are highly frequent on splice sites, and many other patterns are also on splice sites with important biological fun...

iProDNA-CapsNet: identifying protein-DNA binding residues using capsule neural networks.

BMC bioinformatics
BACKGROUND: Since protein-DNA interactions are highly essential to diverse biological events, accurately positioning the location of the DNA-binding residues is necessary. This biological issue, however, is currently a challenging task in the age of ...

IILLS: predicting virus-receptor interactions based on similarity and semi-supervised learning.

BMC bioinformatics
BACKGROUND: Viral infectious diseases are the serious threat for human health. The receptor-binding is the first step for the viral infection of hosts. To more effectively treat human viral infectious diseases, the hidden virus-receptor interactions ...

DeepMF: deciphering the latent patterns in omics profiles with a deep learning method.

BMC bioinformatics
BACKGROUND: With recent advances in high-throughput technologies, matrix factorization techniques are increasingly being utilized for mapping quantitative omics profiling matrix data into low-dimensional embedding space, in the hope of uncovering ins...

JCDB: a comprehensive knowledge base for Jatropha curcas, an emerging model for woody energy plants.

BMC genomics
BACKGROUND: Jatropha curcas is an oil-bearing plant, and has seeds with high oil content (~ 40%). Several advantages, such as easy genetic transformation and short generation duration, have led to the emergence of J. curcas as a model for woody energ...

An automated 3D modeling pipeline for constructing 3D models of MONOGENEAN HARDPART using machine learning techniques.

BMC bioinformatics
BACKGROUND: Studying structural and functional morphology of small organisms such as monogenean, is difficult due to the lack of visualization in three dimensions. One possible way to resolve this visualization issue is to create digital 3D models wh...

High-Throughput Prediction of MHC Class I and II Neoantigens with MHCnuggets.

Cancer immunology research
Computational prediction of binding between neoantigen peptides and major histocompatibility complex (MHC) proteins can be used to predict patient response to cancer immunotherapy. Current neoantigen predictors focus on estimation of MHC binding aff...