The promoter region is located near the transcription start sites and regulates initiation of gene by controlling binding RNA polymerase. Thus, recognition an important area interest in field bioinformatics. Numerous tools for prediction were proposed. However, reliability these still needs to be improved. In this work, we propose a robust deep learning model, called DeePromoter, analyze characteristics short eukaryotic sequences, accurately recognize human mouse sequences. DeePromoter...

Vehicle detection and counting in aerial images have become an interesting research focus since the last decade. It is important for a wide range of applications, such as urban planning traffic management. However, this task challenging one due to small size vehicles, their different types orientations, similarity visual appearance, some other objects, air conditioning units on buildings, trash bins, road marks. Many methods been introduced literature solving problem. These are either based...

Object detection in very high-resolution (VHR) aerial images is an essential step for a wide range of applications such as military applications, urban planning, and environmental management. Still, it challenging task due to the different scales appearances objects. On other hand, object VHR has improved remarkably recent years achieved advances convolution neural networks (CNN). Most proposed methods depend on two-stage approach, namely: region proposal stage classification Faster R-CNN....

The 2'-O-methylation transferase is involved in the process of 2'-O-methylation. In catalytic processes, 2-hydroxy group ribose moiety a nucleotide accept methyl group. This methylation post-transcriptional modification, which occurs various cellular RNAs and plays vital role regulation gene expressions at level. Through biochemical experiments sites produce good results but these exploratory techniques are very expensive. Thus, it required to develop computational method identify sites....

Abstract Background Predicting protein-ligand binding sites is a fundamental step in understanding the functional characteristics of proteins, which plays vital role elucidating different biological functions and crucial drug discovery. A protein exhibits its true nature after to interacting molecule known as ligand that binds only favorable site structure. Different computational methods exploiting features proteins have been developed identify structure, but none seems provide promising...

Pseudouridine is the most prevalent RNA modification and has been found in both eukaryotes prokaryotes. Currently, pseudouridine demonstrated several kinds of RNAs, such as small nuclear RNA, rRNA, tRNA, mRNA, nucleolar RNA. Therefore, its significance to academic research drug development understandable. Through biochemical experiments, site identification produced good outcomes, but these lab exploratory methods processes are expensive time consuming. it important introduce efficient for...

Drug discovery (DD) research is aimed at the of new medications. Solubility an important physicochemical property in drug development. Active pharmaceutical ingredients (APIs) are essential substances for high efficacy. During DD research, aqueous solubility (AS) a key attribute required API characterization. High-precision silico prediction reduces experimental cost and time Several artificial tools have been employed using machine learning deep techniques. This study aims to create...

5-methylcytosine (m5C) is indeed a critical post-transcriptional alteration that widely present in various kinds of RNAs and crucial to the fundamental biological processes. By correctly identifying m5C-methylation sites on RNA, clinicians can more clearly comprehend precise function these m5C-sites different Due their effectiveness affordability, computational methods have received greater attention over last few years for identification methylation species. To precisely identify RNA m5C...

Remarkable and intelligent perovskite solar cells (PSCs) have attracted substantial attention from researchers are undergoing rapid advancements in photovoltaic technology. These developments aim to create highly efficient energy devices with fewer dominant recombination losses within the realm of third-generation cells. Diverse machine learning (ML) algorithms implemented, addressing due PSCs, focusing on grain boundaries (GBs), interfaces, band-to-band recombination. The extreme gradient...

Computational methods play a pivotal role in the pursuit of efficient drug discovery, enabling rapid assessment compound properties before costly and time-consuming laboratory experiments. With advent technology large data availability, machine deep learning have proven predicting molecular solubility. High-precision silico solubility prediction has revolutionized development by enhancing formulation design, guiding lead optimization, pharmacokinetic parameters. These benefits result...

Anticancer peptides (ACPs) play a vital role in selectively targeting and eliminating cancer cells. Evaluating comparing predictions from various machine learning (ML) deep (DL) techniques is challenging but crucial for anticancer drug research. We conducted comprehensive analysis of 15 ML 10 DL models, including the models released after 2022, found that support vector machines (SVMs) with feature combination selection significantly enhance overall performance. especially convolutional...

The epigenetic modification, DNA N4 - methylcytosine(4mC) plays an important role in expression, repair, and replication. It simply a crucial restriction-modification systems. better accurate prediction of 4mC sites is much-needed work to understand their functional behavior that leads help both drug discovery biomedical research. Therefore, computational model required. In this work, we present efficient one-dimensional convolutional neural network (CNN) model, called 4mCCNN, for 4mc...

Post-transcriptional modification such as N6-methyladenosine (m6A) has a crucial role in the stability and regulation of gene expression. Therefore, identification m6A is highly required for understanding functional mechanisms biological processes. Several machine learning techniques based on handy craft feature extraction methods have been proposed to facilitate laborious work. However, due inefficient extraction, these increase computational complexity thereby affect accuracy m6A.This...

The enhancer is a short regulatory element that plays major role in up-regulating eukaryotic gene expression. To identify enhancers, an experimental process takes long time and high cost; therefore, accurate computational tool much-needed work this area. Existing techniques were developed by the use of handcrafted features followed machine learning techniques, while proposed model extracts enhancers from raw DNA sequences integration natural language processing (NLP) technique using word2vec...

Abstract Motivation DNA N6-methyladenine (6mA) has been demonstrated to have an essential function in epigenetic modification eukaryotic species recent research. 6mA linked various biological processes. It’s critical create a new algorithm that can rapidly and reliably detect sites genomes investigate their roles. The identification of marks the genome is first most important step understanding underlying molecular processes, as well regulatory functions. Results In this article, we proposed...

Respiratory toxicity is a serious public health concern caused by the adverse effects of drugs or chemicals, so pharmaceutical and chemical industries demand reliable precise computational tools to assess respiratory compounds. The purpose this study develop quantitative structure-activity relationship models for large dataset compounds associated with system toxicity. First, several feature selection techniques are explored find optimal subset molecular descriptors efficient modeling. Then,...

N6-methyladenosine (m6A) is a common post-transcriptional alteration that plays critical function in variety of biological processes. Although experimental approaches for identifying m6A sites have been developed and deployed, they are currently expensive transcriptome-wide identification. Some computational strategies presented as an effective complement to the procedure. However, their performance still requires improvement. In this study, we proposed novel tool called DL-m6A...