File format handling csv excel

Skill HolobiomicsLab/asb-skill-collections/collections/metabolomics/v2/skills/file-format-handling-csv-excel

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Install

npx -y skills add HolobiomicsLab/asb-skill-collections --skill file-format-handling-csv-excel

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Use when you have molecular descriptor data in CSV or EXCEL format and need to pass it to BitterPredict.m or another descriptor-based classifier.

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SKILL.md

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file-format-handling-csv-excel

License: restricted — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution.

Summary

Load and parse CSV or EXCEL files containing molecular descriptors into a structured table format suitable for classifier input. This skill bridges raw descriptor data files and machine-learning prediction workflows by ensuring proper file I/O and data integrity.

When to use

You have molecular descriptor data in CSV or EXCEL format and need to pass it to BitterPredict.m or another descriptor-based classifier. The input files must contain required molecular descriptors (columns) and one row per molecule, with molecule identifiers preserved for downstream output annotation.

When NOT to use

Input is already a parsed descriptor table in memory; file I/O is unnecessary.
Input files lack required molecular descriptor columns; validate schema before loading.
Molecule identifiers are missing or non-unique; BitterPredict.m requires traceable per-molecule predictions.

Inputs

CSV file with molecular descriptors and molecule identifiers
EXCEL file with molecular descriptors and molecule identifiers

Outputs

In-memory descriptor table (pandas DataFrame or equivalent)
Structured output file with molecule identifiers and predicted class labels

How to apply

Load the CSV or EXCEL file using pandas (Python) or equivalent file I/O library, ensuring all required descriptor columns are present and non-empty. Validate that the table has one row per molecule and that identifiers (molecule names or IDs) are preserved as a column or index. Pass the resulting descriptor table directly to the BitterPredict.m classifier, which will iterate over rows to generate per-molecule predictions. Compile predictions alongside the original molecule identifiers into a structured output file (CSV or EXCEL) for downstream analysis or reporting.

Related tools

BitterPredict.m (Classifier that accepts the loaded descriptor table and generates binary bitterness predictions per molecule) — https://github.com/Niv-Lab/BitterPredict1

Examples

import pandas as pd; descriptors = pd.read_csv('molecules.csv'); predictions = BitterPredict(descriptors); predictions.to_csv('bitter_predictions.csv')

Evaluation signals

All required molecular descriptor columns are present and non-null in the loaded table.
Number of rows equals number of molecules; no rows are dropped or duplicated during parsing.
Molecule identifiers are preserved and match 1:1 with rows in the descriptor table.
Output file contains one prediction per input molecule, with identifiers intact.
Predictions are binary (bitter or not bitter) with no missing or invalid values.

Limitations

Full code availability is conditional on publication; early-stage adoption may encounter undocumented descriptor requirements.
No changelog is available; version compatibility and format evolution are unclear.
Descriptor schema is not explicitly specified in the publication; users must refer to examples in the BitterPredict.m file itself.

Evidence

[readme] BitterPredict.m gets as input CSV or EXCEL files with required descriptors of molecules, and calucautes a predictions if each molecule is bitter or not.: "BitterPredict.m gets as input CSV or EXCEL files with required descriptors of molecules, and calucautes a predictions if each molecule is bitter or not"
[other] Load the input CSV or EXCEL file containing molecular descriptors using appropriate file I/O (pandas or equivalent).: "Load the input CSV or EXCEL file containing molecular descriptors using appropriate file I/O (pandas or equivalent)"
[other] Compile predictions into a structured output file with molecule identifiers and predicted class labels.: "Compile predictions into a structured output file with molecule identifiers and predicted class labels"
[readme] Details and examples of how to use the code is avalibale inside bitterPredict.m file.: "Details and examples of how to use the code is avalibale inside bitterPredict.m file"