Lawrence Livermore National Laboratory (LLNL) researchers and a multi-institutional team of scientists have developed a machine learning-backed model showing the importance of lipids to the signaling dynamics of RAS, a

DNA damage is constantly occurring in cells, either due to external sources or as a result of internal cellular metabolic reactions and physiological activities. Accurate repair of such DNA damages is critical to avoid mutations and chromosomal rearrangements linked to diseases including cancer, immunodeficiencies, neurodegeneration, and premature aging. A team of researchers at Massachusetts General Hospital and the National Cancer Research Centre have identified a way to repair genetic damage and prevent DNA alterations using machine learning techniques. The researchers state that it is possible to learn more about how cancer develops and how to fight it if we understand how DNA lesions originate and repair. Therefore, they hope that their discovery will help create better cancer treatments while also protecting our healthy cells. To combat challenges to DNA integrity, cells have evolved systems that detect DNA lesions and initiate a signaling cascade that promotes DNA repair, referred to as the DNA damage response (DDR).

The method, named Drug Ranking Using Machine Learning (DRUML), was published today in Nature Communications and is based on machine learning analysis of data derived from the study of proteins expressed in cancer cells. Having been trained on the responses of these cells to over 400 drugs, DRUML predicts the best drug to treat a given cancer model. Speaking of the new method, Professor Pedro Cutillas from Queen Mary University of London, who led the study, said: "DRUML predicted drug efficacy in several cancer models and from data obtained from different laboratories and in a clinical dataset. By training the models using the responses of these cells to 412 cancer drugs listed in drug response repositories, DRUML was able to produce ordered lists based on the effectiveness of the drugs to reduce cancer cell growth. This study represents a significant advancement in artificial intelligence in biomedical research, and demonstrates that machine learning using proteomics and phosphoproteomics data may be an effective way of selecting the best drug to treat different cancer models. The method, named Drug Ranking Using Machine Learning (DRUML), was published today in Nature Communications and is based on machine learning analysis of data derived from the study of proteins expressed in cancer cells. Having been trained on the responses of these cells to over 400 drugs, DRUML predicts the best drug to treat a given cancer model. Speaking of the new method, Professor Pedro Cutillas from Queen Mary University of London, who led the study, said: "DRUML predicted drug efficacy in several cancer models and from data obtained from different laboratories and in a clinical dataset.

Using a highly sophisticated form of pattern matching, researchers from Florida Atlantic University's College of Engineering and Computer Science are teaching "machines" to detect Medicare fraud. About $19 billion to $65 billion is lost every year because of Medicare fraud, waste, or abuse. Like the proverbial "needle in a haystack," human auditors or investigators have the painstaking task of manually checking thousands of Medicare claims for specific patterns that could indicate foul play or fraudulent behaviors. Furthermore, according to the U.S. Department of Justice, right now fraud enforcement efforts rely heavily on health care professionals coming forward with information about Medicare fraud. "The Effects of Varying Class Distribution on Learner Behavior for Medicare Fraud Detection With Imbalanced Big Data," published in the journal Health Information Science and Systems, uses big data from Medicare Part B and employs advanced data analytics and machine learning to automate the fraud detection process.

In this case, the user performed an image search for nanoparticles. As scientific datasets increase in both size and complexity, the ability to label, filter and search this deluge of information has become a laborious, time-consuming and sometimes impossible task, without the help of automated tools. With this in mind, a team of researchers from the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) and UC Berkeley are developing innovative machine learning tools to pull contextual information from scientific datasets and automatically generate metadata tags for each file. Scientists can then search these files via a web-based search engine for scientific data, called Science Search, that the Berkeley team is building. As a proof-of-concept, the team is working with staff at Berkeley Lab's Molecular Foundry, to demonstrate the concepts of Science Search on the images captured by the facility's instruments.

A team of researchers has found a new way to detect dangerous strains of bacteria, potentially preventing outbreaks of food poisoning. The team developed a method that utilizes machine learning and tested it with isolates of Escherichia coli strains. The details are in a paper that was just published in the journal Proceedings of the National Academy of Sciences. Most strains of Escherichia coli are harmless and naturally found in the human body. There are pathogenic strains, however, and they are a rising health concern.