Bacteria


Scientists Use Machine Learning to Find Source of Salmonella

#artificialintelligence

Scientists at the University of Georgia Center for Food Safety has developed a new approach to identify the animal source of some types of Salmonella outbreaks. The researchers have developed a machine learning approach. The study is published in the January 2019 issue of Emerging Infectious Diseases. Researchers Xiangyu Deng and Shaokang Zhang, along with a team of colleagues, used more than a thousand genomes to predict the animal sources of Salmonella Typhimurium. The project used experts from the Centers for Disease Control and Prevention, the Food & Drug Administration, the Minnesota Department of Health, and the Translational Genomics Research Institute.


AI Identifies Patients at Highest Risk of Cholera Infection

#artificialintelligence

Image has been cropped and resized. Scientists have developed machine-learning algorithms that can identify patterns in the bacteria of a patient's gut to determine whether the patient is likely to get infected if exposed to cholera. The researchers believe such artificial intelligence (AI) could be critical in areas of high cholera risk, since it can analyze trillions of bacteria, much more than can be done by humans. The study also demonstrates the power of machine learning to uncover medical insights that would otherwise remain obscure. READ: AI's Ethical Concerns Go Beyond Data Security and Quality The research is a collaboration between Duke University, Massachusetts General Hospital, and the International Centre for Diarrheal Disease Research, in Bangladesh.


A new machine learning tool could flag dangerous bacteria before they cause an outbreak

#artificialintelligence

A new machine learning tool that can detect whether emerging strains of the bacterium, Salmonella are more likely to cause dangerous bloodstream infections rather than food poisoning has been developed. The tool, created by a scientist at the Wellcome Sanger Institute and her collaborators at the University of Otago, New Zealand and the Helmholtz Institute for RNA-based Infection Research, a site of the Helmholtz Centre for Infection Research, Germany, greatly speeds up the process for identifying the genetic changes underlying new invasive types of Salmonella that are of public health concern. Reported today (8 May) in PLOS Genetics, the machine learning tool could be useful for flagging dangerous bacteria before they cause an outbreak, from hospital wards to a global scale. As the cost of genomic sequencing falls, scientists around the world are using genetics to better understand the bacteria causing infections, how diseases spread, how bacteria gain resistance to drugs, and which strains of bacteria may cause outbreaks. However, current methods to identify the genetic adaptations in emerging strains of bacteria behind an outbreak are time-consuming and often involve manually comparing the new strain to an older reference collection.


Machine learning flags emerging pathogens: A new machine learning tool could flag dangerous bacteria before they cause an outbreak, from hospital wards to a global scale

#artificialintelligence

Reported today (8 May) in PLOS Genetics, the machine learning tool could be useful for flagging dangerous bacteria before they cause an outbreak, from hospital wards to a global scale. As the cost of genomic sequencing falls, scientists around the world are using genetics to better understand the bacteria causing infections, how diseases spread, how bacteria gain resistance to drugs, and which strains of bacteria may cause outbreaks. However, current methods to identify the genetic adaptations in emerging strains of bacteria behind an outbreak are time-consuming and often involve manually comparing the new strain to an older reference collection. The group of bacteria known as Salmonella includes many different types that vary in the severity of the disease they cause. Some types cause food poisoning, known as gastrointestinal Salmonella, whereas others cause severe disease by spreading beyond the gut, for example Salmonella Typhi which causes typhoid fever.


Machine Learning Flags Emerging Pathogens

#artificialintelligence

A new machine learning tool that can detect whether emerging strains of the bacterium, Salmonella are more likely to cause dangerous bloodstream infections rather than food poisoning has been developed. The tool, created by a scientist at the Wellcome Sanger Institute and her collaborators at the University of Otago, New Zealand and the Helmholtz Institute for RNA-based Infection Research, a site of the Helmholtz Centre for Infection Research, Germany, greatly speeds up the process for identifying the genetic changes underlying new invasive types of Salmonella that are of public health concern. Reported today (8 May) in PLOS Genetics, the machine learning tool could be useful for flagging dangerous bacteria before they cause an outbreak, from hospital wards to a global scale. As the cost of genomic sequencing falls, scientists around the world are using genetics to better understand the bacteria causing infections, how diseases spread, how bacteria gain resistance to drugs, and which strains of bacteria may cause outbreaks. However, current methods to identify the genetic adaptations in emerging strains of bacteria behind an outbreak are time-consuming and often involve manually comparing the new strain to an older reference collection.


Machine learning flags emerging pathogens

#artificialintelligence

A new machine learning tool that can detect whether emerging strains of the bacterium, Salmonella are more likely to cause dangerous bloodstream infections rather than food poisoning has been developed. The tool, created by a scientist at the Wellcome Sanger Institute and her collaborators at the University of Otago, New Zealand and the Helmholtz Institute for RNA-based Infection Research, a site of the Helmholtz Centre for Infection Research, Germany, greatly speeds up the process for identifying the genetic changes underlying new invasive types of Salmonella that are of public health concern. Reported today (8 May) in PLOS Genetics, the machine learning tool could be useful for flagging dangerous bacteria before they cause an outbreak, from hospital wards to a global scale. As the cost of genomic sequencing falls, scientists around the world are using genetics to better understand the bacteria causing infections, how diseases spread, how bacteria gain resistance to drugs, and which strains of bacteria may cause outbreaks. However, current methods to identify the genetic adaptations in emerging strains of bacteria behind an outbreak are time-consuming and often involve manually comparing the new strain to an older reference collection.


AI-Driven Test System Detects Bacteria In Water

#artificialintelligence

"Clean water and health care and school and food and tin roofs and cement floors, all of these things should constitute a set of basics that people must have as birthrights."1 Obtaining clean water is a critical problem for much of the world's population. Testing and confirming a clean water source typically requires expensive test equipment and manual analysis of the results. For regions in the world in which access to clean water is a continuing problem, simpler test methods could dramatically help prevent disease and save lives. To apply artificial intelligence (AI) techniques to evaluating the purity of water sources, Peter Ma, an Intel Software Innovator, developed an effective system for identifying bacteria using pattern recognition and machine learning.


Artificial Intelligence may help identify bacteria quickly, accurately

#artificialintelligence

Microscopes enhanced with artificial intelligence (AI) could help in the quick and accurate diagnosis of the deadly blood infections, which may improve patients' odds of survival, according to a study. The bacteria that most often cause bloodstream infections include the rod-shaped bacteria including Escherichia coli or E.coli, the round clusters of Staphylococcus species, and the pairs or chains of Streptococcus species.


Switched latent force models for reverse-engineering transcriptional regulation in gene expression data

arXiv.org Machine Learning

To survive environmental conditions, cells transcribe their response activities into encoded mRNA sequences in order to produce certain amounts of protein concentrations. The external conditions are mapped into the cell through the activation of special proteins called transcription factors (TFs). Due to the difficult task to measure experimentally TF behaviours, and the challenges to capture their quick-time dynamics, different types of models based on differential equations have been proposed. However, those approaches usually incur in costly procedures, and they present problems to describe sudden changes in TF regulators. In this paper, we present a switched dynamical latent force model for reverse-engineering transcriptional regulation in gene expression data which allows the exact inference over latent TF activities driving some observed gene expressions through a linear differential equation. To deal with discontinuities in the dynamics, we introduce an approach that switches between different TF activities and different dynamical systems. This creates a versatile representation of transcription networks that can capture discrete changes and non-linearities We evaluate our model on both simulated data and real-data (e.g. microaerobic shift in E. coli, yeast respiration), concluding that our framework allows for the fitting of the expression data while being able to infer continuous-time TF profiles.


Recurrent Neural Network Based Modeling of Gene Regulatory Network Using Bat Algorithm

arXiv.org Artificial Intelligence

Correct inference of genetic regulations inside a cell is one of the greatest challenges in post genomic era for the biologist and researchers. Several intelligent techniques and models were already proposed to identify the regulatory relations among genes from the biological database like time series microarray data. Recurrent Neural Network (RNN) is one of the most popular and simple approach to model the dynamics as well as to infer correct dependencies among genes. In this paper, Bat Algorithm (BA) is applied to optimize the model parameters of RNN model of Gene Regulatory Network (GRN). Initially the proposed method is tested against small artificial network without any noise and the efficiency is observed in term of number of iteration, number of population and BA optimization parameters. The model is also validated in presence of different level of random noise for the small artificial network and that proved its ability to infer the correct inferences in presence of noise like real world dataset. In the next phase of this research, BA based RNN is applied to real world benchmark time series microarray dataset of E. coli. The results prove that it can able to identify the maximum number of true positive regulation but also include some false positive regulations. Therefore, BA is very suitable for identifying biological plausible GRN with the help RNN model.