Application of Biological Domain Knowledge Based Feature Selection on Gene Expression Data

Yousef, Malik; Kumar, Abhishek; Bakır-Güngör, Burcu

doi:10.20944/preprints202012.0377.v1

Cited by 9 publications

(1 citation statement)

References 37 publications

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“…In the last two decades, evolvement in computational approaches and modeling led to a paradigm shift in research methodologies related to infectious diseases [ 48 , 49 , 50 , 51 , 52 , 53 , 54 ]. Advancements in AI algorithms have helped to analyze a great volume of data and make meaningful predictions, conclusions, and automation [ 55 ].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

et al. 2021

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As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning(ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

et al. 2021

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Prediction of Linear Cationic Antimicrobial Peptides Active against Gram-Negative and Gram-Positive Bacteria Based on Machine Learning Models

et al. 2022

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Antimicrobial peptides (AMPs) are considered as promising alternatives to conventional antibiotics in order to overcome the growing problems of antibiotic resistance. Computational prediction approaches receive an increasing interest to identify and design the best candidate AMPs prior to the in vitro tests. In this study, we focused on the linear cationic peptides with non-hemolytic activity, which are downloaded from the Database of Antimicrobial Activity and Structure of Peptides (DBAASP). Referring to the MIC (Minimum inhibition concentration) values, we have assigned a positive label to a peptide if it shows antimicrobial activity; otherwise, the peptide is labeled as negative. Here, we focused on the peptides showing antimicrobial activity against Gram-negative and against Gram-positive bacteria separately, and we created two datasets accordingly. Ten different physico-chemical properties of the peptides are calculated and used as features in our study. Following data exploration and data preprocessing steps, a variety of classification algorithms are used with 100-fold Monte Carlo Cross-Validation to build models and to predict the antimicrobial activity of the peptides. Among the generated models, Random Forest has resulted in the best performance metrics for both Gram-negative dataset (Accuracy: 0.98, Recall: 0.99, Specificity: 0.97, Precision: 0.97, AUC: 0.99, F1: 0.98) and Gram-positive dataset (Accuracy: 0.95, Recall: 0.95, Specificity: 0.95, Precision: 0.90, AUC: 0.97, F1: 0.92) after outlier elimination is applied. This prediction approach might be useful to evaluate the antibacterial potential of a candidate peptide sequence before moving to the experimental studies.

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AMP-GSM: Prediction of Antimicrobial Peptides via a Grouping–Scoring–Modeling Approach

2023

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Due to the increasing resistance of bacteria to antibiotics, scientists began seeking new solutions against this problem. One of the most promising solutions in this field are antimicrobial peptides (AMP). To identify antimicrobial peptides, and to aid the design and production of novel antimicrobial peptides, there is a growing interest in the development of computational prediction approaches, in parallel with the studies performing wet-lab experiments. The computational approaches aim to understand what controls antimicrobial activity from the perspective of machine learning, and to uncover the biological properties that define antimicrobial activity. Throughout this study, we aim to develop a novel prediction approach that can identify peptides with high antimicrobial activity against selected target bacteria. Along this line, we propose a novel method called AMP-GSM (antimicrobial peptide-grouping–scoring–modeling). AMP-GSM includes three main components: grouping, scoring, and modeling. The grouping component creates sub-datasets via placing the physicochemical, linguistic, sequence, and structure-based features into different groups. The scoring component gives a score for each group according to their ability to distinguish whether it is an antimicrobial peptide or not. As the final part of our method, the model built using the top-ranked groups is evaluated (modeling component). The method was tested for three AMP prediction datasets, and the prediction performance of AMP-GSM was comparatively evaluated with several feature selection methods and several classifiers. When we used 10 features (which are members of the physicochemical group), we obtained the highest area under curve (AUC) value for both the Gram-negative (99%) and Gram-positive (98%) datasets. AMP-GSM investigates the most significant feature groups that improve AMP prediction. A number of physico-chemical features from the AMP-GSM’s final selection demonstrate how important these variables are in terms of defining peptide characteristics and how they should be taken into account when creating models to predict peptide activity.

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Application of Biological Domain Knowledge Based Feature Selection on Gene Expression Data

Cited by 9 publications

References 37 publications

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

Prediction of Linear Cationic Antimicrobial Peptides Active against Gram-Negative and Gram-Positive Bacteria Based on Machine Learning Models

AMP-GSM: Prediction of Antimicrobial Peptides via a Grouping–Scoring–Modeling Approach

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