We evaluate the feasibility of using co-folding models for synthetic data augmentation in training machine learning-based scoring functions (MLSFs) for binding affinity prediction. Our results show that performance gains depend critically on the structural quality of augmented data. In light of this, we established simple heuristics for identifying high-quality co-folding predictions without reference structures, enabling them to substitute for experimental structures in MLSF training. Our study informs future data augmentation strategies based on co-folding models.

A new approach (SMOTE-RF-SVM) is proposed to identify SARS-CoV-2 epitopes that can be used in vaccine design. Epitope candidates that can be used in vaccine design were determined using machine learning-based in silico and bioinformatics tools. In the unbalanced dataset, generating artificial data with the SMOTE technique increased the model performance. Nonallergic, high antigen (antigen score ≥1.0) and nontoxic 11 possible epitopes candidates were proposed. The search space for vaccine studies was narrowed by SMOTE-RF-SVM.

The concept of smart cities is founded on sophisticated cellular networks that would not only connect humans in the future but also humans to other smart devices. However, this would also require huge energy consumption. In the wake of climate change, this can make matters worse for our environment by increasing the greenhouse gas emissions. Thus, we not only need smart cities but also greener smart cities. One way to address this issue is by switching off base stations (BSs), radio transmitters/receivers that serve as the hub of the local wireless network, when they have little to no traffic load.

The Food and Drug Administration this week gave emergency-use authorization to a "machine learning-based" device that will reportedly work to detect COVID even in cases in which no immediate symptoms are evident. The device, manufactured by Tiger Tech Solutions, "identifies certain biomarkers that may be indicative of SARS-CoV-2 infection … in asymptomatic individuals over the age of 5," the FDA said in a press release. The device works by reading signals of a patient's blood flow using an armband. "The sensors first obtain pulsatile signals from blood flow over a period of three to five minutes," the FDA said. "Once the measurement is completed," the statement continued.

One of the biggest buzzwords around Google and the overall technology market is machine learning. Google uses it with RankBrain for search and in other ways. We asked Gary Illyes from Google in part two of our interview how Google uses machine learning with search. Illyes said that Google uses it mostly for "coming up with new signals and signal aggregations." So they may look at two or more different existing non-machine-learning signals and see if adding machine learning to the aggregation of them can help improve search rankings and quality. He also said, "RankBrain, where … which re-ranks based on based on historical signals," is another way they use machine learning, and later explained how RankBrain works and that Penguin doesn't really use machine learning.

One of the biggest buzzwords around Google and the overall technology market is machine learning. Google uses it with RankBrain for search and in other ways. We asked Gary Illyes from Google in part two of our interview how Google uses machine learning with search. Illyes said that Google uses it mostly for "coming up with new signals and signal aggregations." So they may look at two or more different existing non-machine-learning signals and see if adding machine learning to the aggregation of them can help improve search rankings and quality.