SpanSeq: Similarity-Based Sequence Data Splitting Method for Deep Learning Projects

핵심 개념

Deep learning models in computational biology require careful data splitting to avoid data leakage and ensure accurate model assessment.

초록

Abstract:

Deep learning models in computational biology have seen a surge in usage, but data splitting methods for model assessment are crucial.
Random data splitting can lead to data leakage and inaccurate model assessment.

Introduction:

Deep learning models rely on their plasticity to learn patterns but can memorize individual samples, leading to overfitting.
SpanSeq is introduced as a method to avoid data leakage between sets in machine learning.

Materials and Methods:

SpanSeq method involves similarity calculation, clustering, and partition creation for dataset splitting.
The software is implemented in C++ and organized using SnakeMake.

Performance Evaluation:

SpanSeq was tested on protein, gene, and genome sequences, showing efficient clustering and partitioning capabilities.

Results:

SpanSeq demonstrated effective clustering and partitioning of biological sequences, improving model assessment and development.

Discussion:

The study highlights the importance of data splitting methods in deep learning model development.

Conclusion:

SpanSeq offers a reliable method for data partitioning in deep learning projects, enhancing model generalization and assessment.

통계

Deep learning models rely on their plasticity to learn patterns but can memorize individual samples, leading to overfitting.
SpanSeq is introduced as a method to avoid data leakage between sets in machine learning.
The software is implemented in C++ and organized using SnakeMake.
SpanSeq demonstrated effective clustering and partitioning of biological sequences, improving model assessment and development.
The study highlights the importance of data splitting methods in deep learning model development.
SpanSeq offers a reliable method for data partitioning in deep learning projects, enhancing model generalization and assessment.

인용구

"The use of deep learning models in computational biology has increased massively in recent years." - Abstract
"SpanSeq is available for downloading and installing at https://github.com/genomicepidemiology/SpanSeq." - Abstract
"Deep learning models rely on their plasticity to learn patterns but can memorize individual samples, leading to overfitting." - Data Sheet

핵심 통찰 요약

SpanSeq

by Alfr... 게시일 arxiv.org 03-06-2024

https://arxiv.org/pdf/2402.14482.pdf

더 깊은 질문

어떻게 SpanSeq 방법을 유전 역학학 이외의 다른 분야에 적응시킬 수 있을까요?

SpanSeq 방법은 유전 역학학 분야에서의 데이터 분할에 특화되어 있지만 다른 분야에도 적용할 수 있습니다. 예를 들어, 텍스트 데이터나 이미지 데이터와 같은 다른 유형의 시퀀스 데이터에도 적용할 수 있습니다. 텍스트 데이터의 경우, 문장이나 단어의 유사성을 기반으로 SpanSeq를 사용하여 데이터를 분할할 수 있습니다. 이미지 데이터의 경우, 특정 이미지 패턴이나 기능의 유사성을 기준으로 데이터를 분할할 수 있습니다. 이를 통해 다른 분야에서도 데이터 유사성을 고려한 효율적인 모델 개발이 가능해질 것입니다.