AIMC Topic: Escherichia coli

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Understanding and predicting ciprofloxacin minimum inhibitory concentration in Escherichia coli with machine learning.

Scientific reports
It is important that antibiotics prescriptions are based on antimicrobial susceptibility data to ensure effective treatment outcomes. The increasing availability of next-generation sequencing, bacterial whole genome sequencing (WGS) can facilitate a ...

Accurate prediction of DNA N-methylcytosine sites via boost-learning various types of sequence features.

BMC genomics
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...

Discriminating between sleep and exercise-induced fatigue using computer vision and behavioral genetics.

Journal of neurogenetics
Following prolonged swimming, cycle between active swimming bouts and inactive quiescent bouts. Swimming is exercise for and here we suggest that inactive bouts are a recovery state akin to fatigue. It is known that cGMP-dependent kinase (PKG) acti...

Prediction and analysis of prokaryotic promoters based on sequence features.

Bio Systems
Promoter recognition is an important part of functional genomic annotation but a difficult problem. Many studies have been carried out to address this issue. However, they still cannot meet application needs. Most of the methods exhibit specificity, ...

LogicNet: probabilistic continuous logics in reconstructing gene regulatory networks.

BMC bioinformatics
BACKGROUND: Gene Regulatory Networks (GRNs) have been previously studied by using Boolean/multi-state logics. While the gene expression values are usually scaled into the range [0, 1], these GRN inference methods apply a threshold to discretize the d...

Application of artificial neural networks to prediction of new substances with antimicrobial activity against Escherichia coli.

Journal of applied microbiology
AIMS: This article presents models of artificial neural networks (ANN) employed to predict the biological activity of chemical compounds based of their structure. Regression and classification models were designed to determine antimicrobial propertie...

Rapid detection of microbiota cell type diversity using machine-learned classification of flow cytometry data.

Communications biology
The study of complex microbial communities typically entails high-throughput sequencing and downstream bioinformatics analyses. Here we expand and accelerate microbiota analysis by enabling cell type diversity quantification from multidimensional flo...

Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping.

Nature communications
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...

Synthesis Success Calculator: Predicting the Rapid Synthesis of DNA Fragments with Machine Learning.

ACS synthetic biology
The synthesis and assembly of long DNA fragments has greatly accelerated synthetic biology and biotechnology research. However, long turnaround times or synthesis failures create unpredictable bottlenecks in the design-build-test-learn cycle. We deve...

Talaromydien a and talaroisocoumarin A, new metabolites from the marine-sourced fungus sp. ZZ1616.

Natural product research
New talaromydien A and talaroisocoumarin A (), together with nine known compounds (-), were isolated from a culture of the marine-derived sp. ZZ1616 in potato dextrose broth medium. Structures of the new compounds were elucidated based on their HRE...