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Maxam-Gilbert Sequencing

Maxam-Gilbert Sequencing

In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
Challenges of the Maxam-Gilbert Method
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Related Experiment Video

Updated: Nov 25, 2025

Optimized Negative Staining: a High-throughput Protocol for Examining Small and Asymmetric Protein Structure by Electron Microscopy

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Toward Better Structure and Constraint to Mine Negative Sequential Patterns.

Xinming Gao, Yongshun Gong, TianTian Xu

IEEE Transactions on Neural Networks and Learning Systems

|December 17, 2020

Summary

This summary is machine-generated.

This study introduces sc-NSP, an efficient algorithm for negative sequential pattern (NSP) mining in behavioral science. It significantly improves upon existing methods, enabling better discovery of non-occurring behaviors (NOB) in complex datasets.

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Area of Science:

Behavioral Science
Data Mining
Computational Science

Background:

Non-occurring behavior (NOB) analysis is crucial in behavioral science.
Negative sequential pattern (NSP) mining is effective for identifying NOB and occurring behaviors (OB).
Existing NSP mining algorithms suffer from inefficiencies in positive sequential pattern (PSP) mining, strict constraints, and poor Negative Sequential Candidate (NSC) generation.

Purpose of the Study:

To propose a highly efficient algorithm, sc-NSP, for mining negative sequential patterns (NSP).
To address the key weaknesses of existing NSP mining techniques.
To improve the practical efficiency and effectiveness of NSP discovery.

Main Methods:

An improved PrefixSpan algorithm integrated with a bitmap storage structure for PSP mining.
Loosened frequency constraints and adapted the PNSP method for NSC generation.
A novel pruning strategy and bitwise-based calculations for efficient NSC support acquisition.

Main Results:

The sc-NSP algorithm demonstrates high efficiency, particularly on large datasets with numerous elements and items.
Experimental results show sc-NSP is 10 times more efficient than state-of-the-art methods.
sc-NSP identifies 5 times more NSPs compared to existing approaches, validated through health data case studies.

Conclusions:

sc-NSP offers a significant advancement in NSP mining efficiency and effectiveness.
The algorithm's improvements address critical limitations in current NSP mining practices.
sc-NSP provides a powerful tool for behavioral science research, especially in analyzing complex sequential data.