Abiotic Stress Prediction Tools

This page provides a curated list of prediction tools dedicated to abiotic stress proteins in plants. These tools assist researchers in exploring proteins and transcription factors associated with abiotic stress tolerance.

Name	Year	Description	PubMed ID
afpropred	2024	Identifies anti-freezing proteins using evolutionary profile data.	39305039
ASRpro	2022	Machine-learning model for identifying proteins linked to multiple abiotic stresses in plants.	36098562
DeepAProt	2022	Deep learning-based tool for classification and identification of abiotic stress proteins in cereals.	36714750
Paul et al.	2014	Uses support vector machines to classify cereal proteins related to abiotic stress based on physicochemical properties.	Link
ASPTF	2024	Predicts abiotic stress-responsive transcription factors in plants using machine learning.	38490467
P. Rodziewicz et al.	2019	Identifies drought-responsive proteins and related proteomic QTLs in barley.	30816960
LSPpred	2023	A suite of tools for predicting leaderless secretory proteins in plants.	37050054
Ahmed et al.	2021	Compares machine learning-based methods for classifying abiotic stress proteins.	Link
MLAS	2025	Machine Learning-Based Approach for Predicting Abiotic Stress-Responsive Genes in Chinese Cabbage.	Link

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