RESUMEN
Promoters, which are short (50-1500 base-pair) in DNA regions, have emerged to play a critical role in the regulation of gene transcription. Numerous dangerous diseases, likewise cancer, cardiovascular, and inflammatory bowel diseases, are caused by genetic variations in promoters. Consequently, the correct identification and characterization of promoters are significant for the discovery of drugs. However, experimental approaches to recognizing promoters and their strengths are challenging in terms of cost, time, and resources. Therefore, computational techniques are highly desirable for the correct characterization of promoters from unannotated genomic data. Here, we designed a powerful bi-layer deep-learning based predictor named "PROCABLES", which discriminates DNA samples as promoters in the first-phase and strong or weak promoters in the second-phase respectively. The proposed method utilizes five distinct features, such as word2vec, k-spaced nucleotide pairs, trinucleotide propensity-based features, trinucleotide composition, and electron-ion interaction pseudopotentials, to extract the hidden patterns from the DNA sequence. Afterwards, a stacked framework is formed by integrating a convolutional neural network (CNN) with bidirectional long-short-term memory (LSTM) using multi-view attributes to train the proposed model. The PROCABLES model achieved an accuracy of 0.971 and 0.920 and the MCC 0.940 and 0.840 for the first and second-layer using the ten-fold cross-validation test, respectively. The predicted results anticipate that the proposed PROCABLES protocol outperformed the advanced computational predictors targeting promoters and their types. In summary, this research will provide useful hints for the recognition of large-scale promoters in particular and other DNA problems in general.
RESUMEN
Diabetes is a chronic disease that is characterized by high blood sugar levels and can have several harmful outcomes. Hyperglycemia, which is defined by persistently elevated blood sugar, is one of the primary concerns. People can improve their overall well-being and get optimal health outcomes by prioritizing diabetes control. Although the use of experimental approaches in diabetes treatment is cost-effective, it necessitates the development of many strategies for evaluating the efficacy of therapies. Researchers can quickly create new strategies for managing diabetes and get vital insights by enabling virtual screening with computational tools and procedures. In this study, we suggest a predictor named STADIP (STacking-based predictor for AntiDiabetic Peptides), a new method to predict antidiabetic peptides (ADPs) utilizing a stacked-based ensemble approach. It uses 12 different feature encodings and seven machine-learning techniques to construct 84 baseline models. The impacts of various baseline models on ADP prediction were then thoroughly examined. A two-step feature selection method, eXtreme Gradient Boosting with Sequential Forward Selection (XGB-SFS), was employed to determine the optimal number, out of 84 PFs to enhance predictive performance. Subsequently, utilizing the meta-predictor approach, 45 selected PFs were integrated into an XGB classifier to formulate the final hybrid model. The proposed method demonstrated superior predictive capabilities compared to constituent baseline models, as evidenced by evaluations on both cross-validation and independent tests. During extensive independent testing, STADIP achieved promising performance with accuracy and mathew's correlation coefficient of 0.954 and 0.877, respectively. It is anticipated that it will be useful tool in helping the scientific community to identify new antidiabetic proteins.