mi_balance_data

Data balance.
Most classes adopt random undersampling, while a few classes adopt smote method to oversample to obtain relatively balanced data.

The number of biological databases is growing rapidly, but different databases use different IDs to refer to the same biological entity. The inconsistency in IDs impedes the integration of various types of biological data. To resolve the problem, we developed 'MantaID', a data-driven, machine-learning based approach that automates identifying IDs on a large scale. The 'MantaID' model's prediction accuracy was proven to be 99%, and it correctly and effectively predicted 100,000 ID entries within two minutes. 'MantaID' supports the discovery and exploitation of ID patterns from large quantities of databases. (e.g., up to 542 biological databases). An easy-to-use freely available open-source software R package, a user-friendly web application, and API were also developed for 'MantaID' to improve applicability. To our knowledge, 'MantaID' is the first tool that enables an automatic, quick, accurate, and comprehensive identification of large quantities of IDs, and can therefore be used as a starting point to facilitate the complex assimilation and aggregation of biological data across diverse databases.

Zeng Zhengpeng

MantaID

A Machine-Learning Based Tool to Automate the Identification of
Biological Database IDs

Zhengpeng Zeng

Longfei Mao

Feng Yu

Jiamin Hu

Xiting Wang

mi_balance_data function

<dl><dt>data</dt>
<dd>A data frame. Except for class column, all are numeric types.</dd>
<dt>ratio</dt>
<dd>Numeric between 0 and 1. The percent of the test set split from data.</dd>
<dt>parallel</dt>
<dd>Logical.</dd></dl>

Arguments

Data balance.
Most classes adopt random undersampling, while a few classes adopt smote method to oversample to obtain relatively balanced data. — mi_balance_data

<dl>

<dt>data</dt>
<dd>A data frame. Except for class column, all are numeric types.</dd>


<dt>ratio</dt>
<dd>Numeric between 0 and 1. The percent of the test set split from data.</dd>


<dt>parallel</dt>
<dd>Logical.</dd>

</dl>

mi_balance_data: Data balance. Most classes adopt random undersampling, while a few classes adopt smote method to oversample to obtain relatively balanced data.

Description

Usage

Value

Arguments