{"doi":"10.1109/access.2020.2989713","title":"Mal-Light: Enhancing Lysine Malonylation Sites Prediction Problem Using Evolutionary-based Features","abstract":"Post Translational Modification (PTM) is considered an important biological process with a tremendous impact on the function of proteins in both eukaryotes, and prokaryotes cells. During the past decades, a wide range of PTMs has been identified. Among them, malonylation is a recently identified PTM which plays a vital role in a wide range of biological interactions. Notwithstanding, this modification plays a potential role in energy metabolism in different species including Homo Sapiens. The identification of PTM sites using experimental methods is time-consuming and costly. Hence, there is a demand for introducing fast and cost-effective computational methods. In this study, we propose a new machine learning method, called Mal-Light, to address this problem. To build this model, we extract local evolutionary-based information according to the interaction of neighboring amino acids using a bi-peptide based method. We then use Light Gradient Boosting (LightGBM) as our classifier to predict malonylation sites. Our results demonstrate that Mal-Light is able to significantly improve malonylation site prediction performance compared to previous studies found in the literature. Using Mal-Light we achieve Matthew's correlation coefficient (MCC) of 0.74 and 0.60, Accuracy of 86.66% and 79.51%, Sensitivity of 78.26% and 67.27%, and Specificity of 95.05% and 91.75%, for Homo Sapiens and Mus Musculus proteins, respectively. Mal-Light is implemented as an online predictor which is publicly available at: (http://brl.uiu.ac.bd/MalLight/).","journal":"IEEE Access","year":2020,"id":75345,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":25,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9619,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":393257,"name":"Md. Easin Arafat","orcid":"0000-0003-4014-9144","position":1,"is_corresponding":false},{"id":393258,"name":"Ghazaleh Taherzadeh","orcid":"0000-0003-3998-3967","position":2,"is_corresponding":false},{"id":12485,"name":"Alok Sharma","orcid":"0000-0002-7668-3501","position":3,"is_corresponding":false},{"id":393259,"name":"Shubhashis Roy Dipta","orcid":"0000-0002-9176-1782","position":4,"is_corresponding":false},{"id":393260,"name":"Abdollah Dehzangi","orcid":"0000-0001-8577-0271","position":5,"is_corresponding":false},{"id":393261,"name":"Swakkhar Shatabda","orcid":"0000-0003-0669-072X","position":6,"is_corresponding":false},{"id":394256,"name":"Md. Wakil Ahmad","orcid":null,"position":0,"is_corresponding":true}],"reference_count":89,"raw_metadata":null,"created_at":"2026-07-18T21:46:33.313355Z","pmid":"33354488","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}