Biological sequence analysis is crucial in understanding the sequence structure, function, and evolutionary relationships. In traditional methods, using Euclidean distance metrics is common in measuring the similarity between sequence embeddings. However, they fail to capture sequence space’s inherent curvature and spherical nature. Therefore, we explore the application of spherical geometry and distance metric, circular arc length (CAL) based distance for comparing biological sequence embeddings. Spherical geometry is a non-Euclidean geometry that models the surface of a sphere, accounting for its curvature. By leveraging CAL, we can more accurately measure the pairwise distances between bio-sequence embeddings. In this study, we propose the utilization of CAL for comparing bio-sequence embeddings. We develop a function to compute the CAL distance between two sequence embeddings, enabling researchers to accurately measure the similarity between sequences while considering the underlying spherical geometry. Incorporating spherical geometry enables a more comprehensive understanding of the relationships and similarities between biological sequences, improving various downstream tasks such as classification, clustering, and evolutionary analysis. Our experimental evaluation demonstrates the advantages of using a spherical distance metric over Euclidean metrics for bio-sequence analysis. Our proposed CAL-based approach outperforms the Euclidean geometry-based baselines by depicting a huge performance improvement for the protein subcellular location classification task. In our experiments, accuracy is improved by 52.5% and 60. 3% compared to the PWM2Vec and Autoencoder methods, respectively, corresponding to the DT classifier.

Coronaviruses are membrane-enveloped, non-segmented positive-strand RNA viruses belonging to the Coronaviridae family. They are primarily divided into two subfamilies, Letovirinae and Coronavirinae, with the majority of these viruses belonging to the latter subfamily. Various animal species, mainly mammalian and avian, are severely infected by various coronaviruses, causing serious concerns like the recent pandemic (COVID-19) – one example of the impact of these viruses on human health as well as the global economy. Therefore, building a deeper understanding of these viruses is essential to devise prevention and mitigation mechanisms. Coronaviruses have an invariant genome organization of$\approx 30\text{KB}$, divided into regions that code for non-structural and structural proteins. Among these, an essential structural region is the spike region and its resulting protein which is responsible for attaching the virus to the host cell membrane. Therefore, the usage of only the spike protein, instead of the full genome, provides most of the essential information for performing analyses such as host classification. In this paper, we propose a novel method for predicting the host specificity of coronaviruses by analyzing spike protein sequences from different viral subgenera and species. Our method involves using the Poisson correction distance to generate a distance matrix, followed by using a radial basis function (RBF) kernel and kernel principal component analysis (PCA) to generate a low-dimensional embedding. Finally, we apply classification algorithms to the low-dimensional embedding to generate the resulting predictions of the host specificity of coronaviruses. We provide theoretical proofs for the non-negativity, symmetry, and triangle inequality properties of the Poisson correction distance metric, which are important properties in a machine-learning setting. By encoding the spike protein structure and sequences using this comprehensive approach, we aim to uncover hidden patterns in the biological sequences to make accurate predictions about host specificity. Finally, our classification results illustrate that our method can achieve higher predictive accuracy and improve performance over existing baselines.

Offloading videos from end devices to edge or cloud servers is the key to enabling computation-intensive video analytics. To ensure the analytics accuracy at the server, the video quality for offloading must be configured based on the specific content and the available network bandwidth. While adaptive video streaming for user viewing has been widely studied, none of the existing works can guarantee the analytics accuracy at the server in bandwidth- and content-adaptive way. To fill in this gap, this paper presents DAO, a dynamic adaptive offloading framework for video analytics that jointly considers the dynamics of network bandwidth and video content. DAO is able to maximize the analytics accuracy at the server by adapting the video bitrate and resolution dynamically. In essence, we shift the context of adaptive video transport from traditional DASH systems to a new dynamic adaptive offloading framework tailored for video analytics. DAO is empowered by some new discoveries about the inherent relationship between analytics accuracy, video content, bitrate, and resolution, as well as by an optimization formulation to adapt the bitrate and resolution dynamically. Results from the real-world implementation of object detection tasks show that DAO's performance is close to the theoretical bound, achieving 20% bandwidth saving and 59% category-wise mAP improvement compared to conventional DASH schemes.