Kmer-Node2Vec: a Fast and Efficient Method for Kmer Embedding from the Kmer Co-occurrence Graph, with Applications to DNA Sequences.

Learning low-dimensional continuous vector representation for short k-mers divided from long DNA sequences is key to DNA sequence modeling that can be utilized in many bioinformatics investigations, such as DNA sequence retrieval and classification. DNA2Vec is the most widely used method for DNA sequence embedding. However, it poorly scales to large data sets due to its extremely long training time in kmer embedding. In this paper, we propose a novel efficient graph-based kmer embedding method, named Kmer-Node2Vec, to tackle this concern. Our method converts the large DNA corpus into one kmer co-occurrence graph, and extracts kmer relation on the graph by random walks to learn fast and high-quality kmer embedding. Extensive experiments show that our method is faster than DNA2Vec by 29 times for training on a 4GB data set, and on par with DNA2Vec in terms of task-specific accuracy of sequence retrieval and classification.

The transmission pattern and clinical course of the first 417 patients with COVID-19 infection in Shenzhen, China.

OBJECTIVE: To explore the transmission patterns and clinical course of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that was first identified in Wuhan, China in December 2019 as clustered and non-clustered cases of coronavirus disease (COVID-19) emerged in Shenzhen, China. METHODS: This retrospective study included the patients that were confirmed by laboratory detection of SARS-CoV-2 in Shenzen between 19 January 2020 and 21 February 2020. Data on the epidemiological and clinical characteristics were analysed. The patients were divided into non-clustered and clustered groups. The time course, intervals between first and second COVID-19 cases and other transmission patterns were compared between the groups. RESULTS: The 417 patients were divided into clustered (n = 235) and non-clustered groups (n = 182). Compared with the non-clustered group, the clustered group had significantly more young (≤20 years) and old (>60 years) patients. The clustered group had significantly more severe cases (nine of 235; 3.83%) compared with the non-clustered group (three of 182; 1.65%). Patients with severe disease spent 4-5 more days of hospitalization than patients with moderate and mild disease. CONCLUSION: This retrospective study analysed the transmission patterns and clinical course of the first wave of COVID-19 infection in Shenzhen, China.

Saliva diagnostics: emerging techniques and biomarkers for salivaomics in cancer detection.

INTRODUCTION: The pursuit of easy-to-use, non-invasive and inexpensive diagnostics is an urgent task for clinicians and scientists. Saliva is an important component of body fluid with regular changes of contents under various pathophysiological conditions, and the biomarkers identified from saliva shows high application potentials and values in disease diagnostics. This review introduces the latest developments in saliva research, with an emphasis on the detection and application of salivary biomarkers in cancer detection. AREAS COVERED: Detection of disease-specific biomarkers in saliva samples by existing salivaomic methods can be used to diagnose various human pathological conditions and was introduced in details. This review also covers the saliva collection methods, the analytical techniques as well as the corresponding commercial products, with an aim to describe an holistic process for saliva-based diagnostics. EXPERT OPINION: Saliva, as a non-invasive and collectable body fluid, can reflect the pathophysiological changes of the human body to a certain extent. Identification of reliable saliva biomarkers can provide a convenient way for cancer detection in clinical applications.