Network-Based Trajectory Search over Time Intervals

Chen, Mingming; Wang, Ning; Lin, Guofeng; Shang, Jedi S.

doi:10.1016/j.bdr.2021.100221

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…1b-c ). This is particularly highlighted in discussions about integrating DTW with the concept of gaps 12,13 (as in biological sequence alignment 14,15 ). Both matches and mismatches between trajectories inform our understanding of temporal gene expression dynamics.…”

Section: Introductionmentioning

confidence: 99%

Gene-level alignment of single cell trajectories

Sumanaweera¹,

Suo²,

Muraro³

et al. 2023

Preprint

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Single cell data analysis can infer dynamic changes in cell populations, for example across time, space or in response to perturbation. To compare these dynamics between two conditions, trajectory alignment via dynamic programming (DP) optimization is frequently used, but is limited by assumptions such as a definite existence of a match. Here we describe Genes2Genes, a Bayesian information-theoretic DP framework for aligning single-cell trajectories. Genes2Genes overcomes current limitations and is able to capture sequential matches and mismatches between a reference and a query at single gene resolution, highlighting distinct clusters of genes with varying patterns of gene expression dynamics. Across both real life and simulated datasets, Genes2Genes accurately captured different alignment patterns, and revealed that T cells differentiated in vitro matched to an immature in vivo state while lacking the final TNFɑ signaling. This use case demonstrates that precise trajectory alignment can pinpoint divergence from the in vivo system, thus providing an opportunity to optimize in vitro culture conditions.

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Section: Introductionmentioning

confidence: 99%

Gene-level alignment of single cell trajectories

Sumanaweera¹,

Suo²,

Muraro³

et al. 2023

Preprint

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Parallel Skyline Query Processing of Massive Incomplete Activity-Trajectories Data

Belhassena¹,

Wang

2022

Lecture Notes in Computer Science

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Taxi Travel Distance Clustering Method Based on Exponential Fitting and k-Means Using Data from the US and China

Song,

Cai,

Yang

2024

Systems

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The taxi travel distance distribution can be used to forecast the origin and destination (OD) distribution of taxis and private cars. Most of the existing studies on taxi trip distributions have summarized a “low–high–low” trend and approached zero at both ends; however, they failed to explain the reason for this distance distribution. The key indicators and parameters identified by various researchers using big data for the same city and year typically differ, especially in terms of the mode and mean values of distance and time. This study uses New York yellow and green taxi data (a total of 417,018,811 data points) from 2017 to 2022, as well as data from China, to obtain a general law of the taxi travel distance distribution through an analysis of the relative distance and relative frequency. The travel mode was 0.54 times the relative distance, while the data tended towards zero at 2.0 times the relative distance. We verified the reliability of the research method based on reference and survey data. The results reveal the formation mechanism of the taxi travel distance distribution characteristics, which follow an exponential distribution. These laws can be used in the context of urban planning and transportation research. We propose a taxi form distance clustering method based on the k-means approach, chosen for its effectiveness on large datasets, interpretability, and alignment with our research objectives. This method provides visual results for the travel distance and accurate information for urban transportation planning and taxi services. The practical implications for policymakers, urban planners, and taxi services are discussed, demonstrating how the identified travel distance distribution laws can influence urban planning and taxi service optimization. Finally, the problems of data collection, cleaning, and processing are identified from the perspective of data statistics and analysis.

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Network-Based Trajectory Search over Time Intervals

Cited by 3 publications

References 17 publications

Gene-level alignment of single cell trajectories

Gene-level alignment of single cell trajectories

Parallel Skyline Query Processing of Massive Incomplete Activity-Trajectories Data

Taxi Travel Distance Clustering Method Based on Exponential Fitting and k-Means Using Data from the US and China

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