In the field of biological research, it is essential to comprehend the
characteristics and functions of molecular sequences. The classification of
molecular sequences has seen widespread use of neural network-based techniques.
Despite their astounding accuracy, these models often require a substantial
number of parameters and more data collection. In this work, we present a novel
approach based on the compression-based Model, motivated from
\cite{jiang2023low}, which combines the simplicity of basic compression
algorithms like Gzip and Bz2, with Normalized Compression Distance (NCD)
algorithm to achieve better performance on classification tasks without relying
on handcrafted features or pre-trained models. Firstly, we compress the
molecular sequence using well-known compression algorithms, such as Gzip and
Bz2. By leveraging the latent structure encoded in compressed files, we compute
the Normalized Compression Distance between each pair of molecular sequences,
which is derived from the Kolmogorov complexity. This gives us a distance
matrix, which is the input for generating a kernel matrix using a Gaussian
kernel. Next, we employ kernel Principal Component Analysis (PCA) to get the
vector representations for the corresponding molecular sequence, capturing
important structural and functional information. The resulting vector
representations provide an efficient yet effective solution for molecular
sequence analysis and can be used in ML-based downstream tasks. The proposed
approach eliminates the need for computationally intensive Deep Neural Networks
(DNNs), with their large parameter counts and data requirements. Instead, it
leverages a lightweight and universally accessible compression-based model.

在生物研究领域，我们提出了一种基于压缩模型的新方法，通过使用压缩算法和标准化压缩距离来进行分子序列分类，避免了依赖手工特征或预训练模型，提供了一种高效且有效的分析分子序列的方法。

改进分子序列分析的通用非参数方法

A Universal Non-Parametric Approach For Improved Molecular Sequence  Analysis

Live cell microscopy captures 5-D $(x,y,z,channel,time)$ movies that display
patterns of cellular motion and signaling dynamics. We present here an approach
to finding spatiotemporal patterns of cell signaling dynamics in 5-D live cell
microscopy movies unique in requiring no \emph{a priori} knowledge of expected
pattern dynamics, and no training data. The proposed cell signaling structure
function (SSF) is a Kolmogorov structure function that optimally measures cell
signaling state as nuclear intensity w.r.t. surrounding cytoplasm, a
significant improvement compared to the current state-of-the-art cytonuclear
ratio. SSF kymographs store at each spatiotemporal cell centroid the SSF value,
or a functional output such as velocity. Patterns of similarity are identified
via the metric normalized compression distance (NCD). The NCD is a reproducing
kernel for a Hilbert space that represents the input SSF kymographs as points
in a low dimensional embedding that optimally captures the pattern similarity
identified by the NCD throughout the space. The only parameter is the expected
cell radii ($\mu m$). A new formulation of the cluster structure function
optimally estimates how meaningful an embedding from the RKHS representation.
Results are presented quantifying the impact of ERK and AKT signaling between
different oncogenic mutations, and by the relation between ERK signaling and
cellular velocity patterns for movies of 2-D monolayers of human breast
epithelial (MCF10A) cells, 3-D MCF10A spheroids under optogenetic manipulation
of ERK, and human induced pluripotent stem cells .

通过使用无先验知识和训练数据的细胞信号结构函数 (SSF) 和标准化压缩距离 (NCD)，可以在 5-D 实时细胞显微镜拍摄的影片中发现细胞信号动力学的时空模式，并且定量评估 ERK 和 AKT 信号以及 ERK 信号与细胞运动模式之间的关系。

细胞信号传导结构与功能

The cell signaling structure function

In this paper, we propose a feature-free method for detecting phishing
websites using the Normalized Compression Distance (NCD), a parameter-free
similarity measure which computes the similarity of two websites by compressing
them, thus eliminating the need to perform any feature extraction. It also
removes any dependence on a specific set of website features. This method
examines the HTML of webpages and computes their similarity with known phishing
websites, in order to classify them. We use the Furthest Point First algorithm
to perform phishing prototype extractions, in order to select instances that
are representative of a cluster of phishing webpages. We also introduce the use
of an incremental learning algorithm as a framework for continuous and adaptive
detection without extracting new features when concept drift occurs. On a large
dataset, our proposed method significantly outperforms previous methods in
detecting phishing websites, with an AUC score of 98.68%, a high true positive
rate (TPR) of around 90%, while maintaining a low false positive rate (FPR) of
0.58%. Our approach uses prototypes, eliminating the need to retain long term
data in the future, and is feasible to deploy in real systems with a processing
time of roughly 0.3 seconds.

本文提出了一种无需特征提取的检测网络钓鱼网站的方法，使用标准化压缩距离（NCD）计算网站的相似性，并引入原型选择和增量学习算法来持续适应不断变化的网络钓鱼页面中，检测方法在大规模数据集上表现良好，具有较高的准确率和可部署性。