Stacking-ac4C: an ensemble model using mixed features for identifying n4-acetylcytidine in mRNA.

N4-acetylcytidine (ac4C) is a modification of cytidine at the nitrogen-4 position, playing a significant role in the translation process of mRNA. However, the precise mechanism and details of how ac4C modifies translated mRNA remain unclear. Since identifying ac4C sites using conventional experimental methods is both labor-intensive and time-consuming, there is an urgent need for a method that can promptly recognize ac4C sites.

Predicting the Lung Adenocarcinoma and Its Biomarkers by Integrating Gene Expression and DNA Methylation Data.

The early symptoms of lung adenocarcinoma patients are inapparent, and the clinical diagnosis of lung adenocarcinoma is primarily through X-ray examination and pathological section examination, whereas the discovery of biomarkers points out another direction for the diagnosis of lung adenocarcinoma with the development of bioinformatics technology. However, it is not accurate and trustworthy to diagnose lung adenocarcinoma due to omics data with high-dimension and low-sample size (HDLSS) features or biomarkers produced by utilizing only single omics data. To address the above problems, the feature selection methods of biological analysis are used to reduce the dimension of gene expression data (GSE19188) and DNA methylation data (GSE139032, GSE49996).

DTI-BERT: Identifying Drug-Target Interactions in Cellular Networking Based on BERT and Deep Learning Method.

Drug-target interactions (DTIs) are regarded as an essential part of genomic drug discovery, and computational prediction of DTIs can accelerate to find the lead drug for the target, which can make up for the lack of time-consuming and expensive wet-lab techniques. Currently, many computational methods predict DTIs based on sequential composition or physicochemical properties of drug and target, but further efforts are needed to improve them. In this article, we proposed a new sequence-based method for accurately identifying DTIs.

iAMP-CA2L: a new CNN-BiLSTM-SVM classifier based on cellular automata image for identifying antimicrobial peptides and their functional types.

Predicting antimicrobial peptides (AMPs') function is an important and difficult problem, particularly when AMPs have many multiplex functions, i.e. some AMPs simultaneously have two or three functional classes.

Analyzing Surgical Treatment of Intestinal Obstruction in Children with Artificial Intelligence.

Intestinal obstruction is a common surgical emergency in children. However, it is challenging to seek appropriate treatment for childhood ileus since many diagnostic measures suitable for adults are not applicable to children. The rapid development of machine learning has spurred much interest in its application to medical imaging problems but little in medical text mining.

iPTT(2 L)-CNN: A Two-Layer Predictor for Identifying Promoters and Their Types in Plant Genomes by Convolutional Neural Network.

A promoter is a short DNA sequence near to the start codon, responsible for initiating transcription of a specific gene in genome. The accurate recognition of promoters has great significance for a better understanding of the transcriptional regulation. Because of their importance in the process of biological transcriptional regulation, there is an urgent need to develop in silico tools to identify promoters and their types timely and accurately.

iPromoter-5mC: A Novel Fusion Decision Predictor for the Identification of 5-Methylcytosine Sites in Genome-Wide DNA Promoters.

The hypomethylation of the whole cancer genome and the hypermethylation of the promoter of specific tumor suppressor genes are the important reasons for the rapid proliferation of cancer cells. Therefore, obtaining the distribution of 5-methylcytosine (5mC) in promoters is a key step to further understand the relationship between promoter methylation and mRNA gene expression regulation. Large-scale detection of DNA 5mC through wet experiments is still time-consuming and laborious.

Using Cellular Automata to Simulate Domain Evolution in Proteins.

Proteins play primary roles in important biological processes such as catalysis, physiological functions, and immune system functions. Thus, the research on how proteins evolved has been a nuclear question in the field of evolutionary biology. General models of protein evolution help to determine the baseline expectations for evolution of sequences, and these models have been extensively useful in sequence analysis as well as for the computer simulation of artificial sequence data sets.

iPPI-PseAAC(CGR): Identify protein-protein interactions by incorporating chaos game representation into PseAAC.

Investigation into the network of protein-protein interactions (PPIs) will provide valuable insights into the inner workings of cells. Accordingly, it is crucially important to develop an automated method or high-throughput tool that can efficiently predict the PPIs. In this study, a new predictor, called "iPPI-PseAAC(CGR)", was developed by incorporating the information of "chaos game representation" into the PseAAC (Pseudo Amino Acid Composition).

iSS-PC: Identifying Splicing Sites via Physical-Chemical Properties Using Deep Sparse Auto-Encoder.

Gene splicing is one of the most significant biological processes in eukaryotic gene expression, such as RNA splicing, which can cause a pre-mRNA to produce one or more mature messenger RNAs containing the coded information with multiple biological functions. Thus, identifying splicing sites in DNA/RNA sequences is significant for both the bio-medical research and the discovery of new drugs. However, it is expensive and time consuming based only on experimental technique, so new computational methods are needed.

iAFP-Ense: An Ensemble Classifier for Identifying Antifreeze Protein by Incorporating Grey Model and PSSM into PseAAC.

Antifreeze proteins (AFPs), known as thermal hysteresis proteins, are ice-binding proteins. AFPs have been found in many fields such as in vertebrates, invertebrates, plants, bacteria, and fungi. Although the function of AFPs is common, the sequences and structures of them show a high degree of diversity.

pSumo-CD: predicting sumoylation sites in proteins with covariance discriminant algorithm by incorporating sequence-coupled effects into general PseAAC.

Motivation: Sumoylation is a post-translational modification (PTM) process, in which small ubiquitin-related modifier (SUMO) is attaching by covalent bonds to substrate protein. It is critical to many different biological processes such as replicating genome, expressing gene, localizing and stabilizing proteins; unfortunately, it is also involved with many major disorders including Alzheimer's and Parkinson's diseases. Therefore, for both basic research and drug development, it is important to identify the sumoylation sites in proteins.

iCar-PseCp: identify carbonylation sites in proteins by Monte Carlo sampling and incorporating sequence coupled effects into general PseAAC.

Carbonylation is a posttranslational modification (PTM or PTLM), where a carbonyl group is added to lysine (K), proline (P), arginine (R), and threonine (T) residue of a protein molecule. Carbonylation plays an important role in orchestrating various biological processes but it is also associated with many diseases such as diabetes, chronic lung disease, Parkinson's disease, Alzheimer's disease, chronic renal failure, and sepsis. Therefore, from the angles of both basic research and drug development, we are facing a challenging problem: for an uncharacterized protein sequence containing many residues of K, P, R, or T, which ones can be carbonylated, and which ones cannot? To address this problem, we have developed a predictor called iCar-PseCp by incorporating the sequence-coupled information into the general pseudo amino acid composition, and balancing out skewed training dataset by Monte Carlo sampling to expand positive subset.

iROS-gPseKNC: Predicting replication origin sites in DNA by incorporating dinucleotide position-specific propensity into general pseudo nucleotide composition.

DNA replication, occurring in all living organisms and being the basis for biological inheritance, is the process of producing two identical replicas from one original DNA molecule. To in-depth understand such an important biological process and use it for developing new strategy against genetics diseases, the knowledge of duplication origin sites in DNA is indispensible. With the explosive growth of DNA sequences emerging in the postgenomic age, it is highly desired to develop high throughput tools to identify these regions purely based on the sequence information alone.

Classifying Multifunctional Enzymes by Incorporating Three Different Models into Chou's General Pseudo Amino Acid Composition.

With the avalanche of the newly found protein sequences in the post-genomic epoch, there is an increasing trend for annotating a number of newly discovered enzyme sequences. Among the various proteins, enzyme was considered as the one of the largest kind of proteins. It takes part in most of the biochemical reactions and plays a key role in metabolic pathways.

iPPBS-Opt: A Sequence-Based Ensemble Classifier for Identifying Protein-Protein Binding Sites by Optimizing Imbalanced Training Datasets.

Knowledge of protein-protein interactions and their binding sites is indispensable for in-depth understanding of the networks in living cells. With the avalanche of protein sequences generated in the postgenomic age, it is critical to develop computational methods for identifying in a timely fashion the protein-protein binding sites (PPBSs) based on the sequence information alone because the information obtained by this way can be used for both biomedical research and drug development. To address such a challenge, we have proposed a new predictor, called iPPBS-Opt, in which we have used: (1) the K-Nearest Neighbors Cleaning (KNNC) and Inserting Hypothetical Training Samples (IHTS) treatments to optimize the training dataset; (2) the ensemble voting approach to select the most relevant features; and (3) the stationary wavelet transform to formulate the statistical samples.

pRNAm-PC: Predicting N(6)-methyladenosine sites in RNA sequences via physical-chemical properties.

Just like PTM or PTLM (post-translational modification) in proteins, PTCM (post-transcriptional modification) in RNA plays very important roles in biological processes. Occurring at adenine (A) with the genetic code motif (GAC), N(6)-methyldenosine (m(6)A) is one of the most common and abundant PTCMs in RNA found in viruses and most eukaryotes. Given an uncharacterized RNA sequence containing many GAC motifs, which of them can be methylated, and which cannot? It is important for both basic research and drug development to address this problem.

iSuc-PseOpt: Identifying lysine succinylation sites in proteins by incorporating sequence-coupling effects into pseudo components and optimizing imbalanced training dataset.

Succinylation is a posttranslational modification (PTM) where a succinyl group is added to a Lys (K) residue of a protein molecule. Lysine succinylation plays an important role in orchestrating various biological processes, but it is also associated with some diseases. Therefore, we are challenged by the following problem from both basic research and drug development: given an uncharacterized protein sequence containing many Lys residues, which one of them can be succinylated, and which one cannot? With the avalanche of protein sequences generated in the postgenomic age, the answer to the problem has become even more urgent.

Predicting the Functional Types of Singleplex and Multiplex Eukaryotic Membrane Proteins via Different Models of Chou's Pseudo Amino Acid Compositions.

Given a membrane protein sequence, how can we identify its type, particularly when a query protein may have the multiplex character, i.e., simultaneously exist at two or more different types.

Identification of protein-protein binding sites by incorporating the physicochemical properties and stationary wavelet transforms into pseudo amino acid composition.

With the explosive growth of protein sequences entering into protein data banks in the post-genomic era, it is highly demanded to develop automated methods for rapidly and effectively identifying the protein-protein binding sites (PPBSs) based on the sequence information alone. To address this problem, we proposed a predictor called iPPBS-PseAAC, in which each amino acid residue site of the proteins concerned was treated as a 15-tuple peptide segment generated by sliding a window along the protein chains with its center aligned with the target residue. The working peptide segment is further formulated by a general form of pseudo amino acid composition via the following procedures: (1) it is converted into a numerical series via the physicochemical properties of amino acids; (2) the numerical series is subsequently converted into a 20-D feature vector by means of the stationary wavelet transform technique.

Benchmark data for identifying DNA methylation sites via pseudo trinucleotide composition.

This data article contains three benchmark datasets for training and testing iDNA-Methyl, a web-server predictor for identifying DNA methylation sites [Liu et al. Anal. Biochem.

iCataly-PseAAC: Identification of Enzymes Catalytic Sites Using Sequence Evolution Information with Grey Model GM (2,1).

Enzymes play pivotal roles in most of the biological reaction. The catalytic residues of an enzyme are defined as the amino acids which are directly involved in chemical catalysis; the knowledge of these residues is important for understanding enzyme function. Given an enzyme, which residues are the catalytic sites, and which residues are not? This is the first important problem for in-depth understanding the catalytic mechanism and drug development.

iPPI-Esml: An ensemble classifier for identifying the interactions of proteins by incorporating their physicochemical properties and wavelet transforms into PseAAC.

A cell contains thousands of proteins. Many important functions of cell are carried out through the proteins therein. Proteins rarely function alone.

iMem-Seq: A Multi-label Learning Classifier for Predicting Membrane Proteins Types.

Predicting membrane protein type is a challenging problem, particularly when the query proteins may simultaneously have two or more different types. Most of the existing methods can only be used to deal with the single-label proteins. Actually, multiple-label proteins should not be ignored because they usually bear some special functions worthy of in-depth studies.

iDNA-Methyl: identifying DNA methylation sites via pseudo trinucleotide composition.

Predominantly occurring on cytosine, DNA methylation is a process by which cells can modify their DNAs to change the expression of gene products. It plays very important roles in life development but also in forming nearly all types of cancer. Therefore, knowledge of DNA methylation sites is significant for both basic research and drug development.