Breast cancer subtyping presents a significant clinical and scientific challenge. The prevalent expression-based Prediction Analysis of the Microarray of 50 genes (PAM50) system and its Immunohistochemistry (IHC) surrogate tests showed substantial inconsistencies and did not apply to the rapidly progressing circulating tumor DNA screenings. We developed Unified Genetic and Epigenetic Subtyping (UGES), a new intrinsic subtype classifier, by integrating large-scale DNA-level omics data with a hierarchy learning algorithm. Our benchmarks showed that both multi-step hierarchical learning and using all DNA-level alteration data are crucial, improving the overall AUC score by over 8.3% compared to the one-step multi-classification method. Based on these insights, we developed UGES, a three-step classifier based on 50831 DNA features of 2065 samples, including mutations, copy number aberrations, and methylations. UGES achieved an overall AUC score of 0.963 and greatly improved the clinical stratification of real-world patients, as each subtype strata's survival difference became statistically more significant, P = 9.7e-55 (UGES) vs. 2.2e-47 (PAM50). Finally, UGES identified 52 subtype-specific DNA biomarkers that can be targeted in early screening technology to expand the time window for precision care. The UGES code is freely available at https://github.com/labxscut/UGES.

A data-secure and cost-efficient Personalized Travel Recommendation (PTR) is necessary to develop urban intelligence in transportation. Although prevalent machine learning-based methods have gained tremendous progress on PTR, Mixed Logit Model (MXL), as a mathematical method, is attracting much attention from industry and academia to analyze refined individual behavior for PTR. Since MXL is still in its infancy in PTR, it encounters three challenges that need to be resolved jointly, namely 1) how to use personal data to describe user preferences without violating user privacy, 2) how to utilize the idle computing resources at the edge to improve the estimation efficiency, and 3) how to coordinate devices whose capability and availability may change over time and space. To fill the gap, we propose an asynchronous and privacy-preserving mixed logit model (APMXL), which aims to integrate MXL with asynchronous federated learning (AFL), which can 1) non-intrusively exchange model parameters between the clients and the server without exposing the raw data, 2) separately perform local and global estimation at the client and server to optimize the load, and 3) collaboratively approximate the posterior of the standard MXL through a continuous asynchronous interaction mechanism. Moreover, based on a standard dataset, APMXL is evaluated, showing that the model accuracy is about 13% higher compared to a flat logit model. Meanwhile, the estimation time has been reduced by about 60% compared to a centralized MXL model, and the robustness of the model has been improved compared with the synchronous and privacy-preserving mixed logit model (SPMXL).

Local associations refer to spatial-temporal correlations that emerge from the biological realm, such as time-dependent gene co-expression or seasonal interactions between microbes. One can reveal the intricate dynamics and inherent interactions of biological systems by examining the biological time series data for these associations. To accomplish this goal, local similarity analysis algorithms and statistical methods that facilitate the local alignment of time series and assess the significance of the resulting alignments have been developed. Although these algorithms were initially devised for gene expression analysis from microarrays, they have been adapted and accelerated for multi-omics next generation sequencing datasets, achieving high scientific impact. In this review, we present an overview of the historical developments and recent advances for local similarity analysis algorithms, their statistical properties, and real applications in analyzing biological time series data. The benchmark data and analysis scripts used in this review are freely available at http://github.com/labxscut/lsareview.

Since mobility needs grow rapidly, the metro system of modern cities is suffering from the oversaturated situation in the rush hour, which makes the metro system vulnerable and inefficient. Thus, passenger inflow control is proposed and implemented. This paper investigates the station-based passenger inflow control problem with the objective of maximizing overall transport efficiency and fairness. To solve the formulated nonlinear and nonconvex programming model, a novel framework integrating unsupervised subgoal discovery and hierarchical reinforcement learning, named SD-HIC, is proposed. With a series of subgoals, the complicated and long-horizon original task is decomposed and can be solved by reinforcement learning responsively and far-sightedly. A real-world case with operational data in the Guangzhou metro is presented to demonstrate the performance of the proposed model and framework. According to the results, the overall transport utility is improved by 28.87% compared with the benchmark inflow control strategy that is frequently used in daily operations. Through solution algorithm studies and critical parameter analyses, the performance of the proposed passenger inflow control framework is further verified. Notably, the revealed subgoals are also distinguishable and interpretable, which is helpful for the operation staff in practice.

In the planning stage, train operators design timetables to serve passenger trips and a train circulation plan to support these timetables. These designs consider not only operating costs but also passenger convenience. In this study, we developed an optimization model for a new problem that focuses on timetabling and train-unit scheduling while also considering passenger itinerary choices in a schedule-based train system. This optimization model minimizes passenger travel costs within the constraints of a limited budget available for operating costs. The model is solved by an iterative heuristic that simulates the interaction between train operations and passenger itinerary choices. The heuristic solves the timetabling and train-unit scheduling problem using a decomposition approach to increase computational efficiency, while passenger loading is solved by a user-equilibrium passenger assignment model. An example based on the high-speed railway network in southern China was used to demonstrate the effectiveness of the proposed model and method.