Coronavirus disease, which comes up in China at the end of 2019 and showed different symptoms in people infected, affected millions of people. Computer-aided expert systems are needed due to the inadequacy of the reverse transcription-polymerase chain reaction kit, which is widely used in the diagnosis of this disease. Undoubtedly, expert systems that provide effective solutions to many problems will

Long non-coding RNA (lncRNA), which is a type of non-coding RNA, was reported to contain short open reading frames (sORFs). SORFs-encoded short peptides (SEPs) have been demonstrated to play a crucial role in regulating the biological processes such as growth, development, and resistance response. The identification of SEPs is vital to further understanding their function. However, there is still a

Background Prediction of protein solubility is an indispensable prerequisite for pharmaceutical research and production. The general and specific objective of this work is to design a new model for predicting protein solubility by using protein sequence feature fusion and deep dual-channel convolutional neural networks (DDcCNN) to improve the performance of existing prediction models. Methods The redundancy

Recently, circRNAs with covalently closed loops have been discovered to play important parts in the progression of diseases. Nevertheless, the study of circRNA-disease associations is highly dependent on biological experiments, which are time-consuming and expensive. Hence, a computational approach to predict circRNA-disease associations is urgently needed. In this paper, we presented an approach that

Background Solid organ transplantation (SOT) is a treatment method for end-stage organ diseases and improve their life quality, while using long-term immunosuppressant drugs (ISD) is needed to suppress the function of the immune system. Immune checkpoint inhibitors (ICIs) are a class of anti-tumor drugs that kill tumors by activating the autoimmune system. The primary objective of our systematic review

Mild cognitive impairment (MCI) is a dangerous signal of severe cognitive decline. It can be separated into two steps: early MCI (EMCI) and late MCI (LMCI). As the post-state of MCI and pre-state of Alzheimer’s disease (AD), LMCI receives insufficient attention in the field of brain science, causing the internal mechanism of LMCI has not been well understood. To better explore the focus and pathological

High-throughput sequencing of single-cell gene expression reveals a complex mechanism of individual cell’s heterogeneity in a population. An important purpose for analyzing single-cell RNA sequencing (scRNA-seq) data is to identify cell subtypes and functions by cell clustering. To deal with high levels of noise and cellular heterogeneity, we introduced a new single cell data analysis model called

The rapid spread of multi-drug resistant microbes has lead researchers to discover natural alternative remedies such as antimicrobial peptides (AMPs). In the first line of defense, AMPs display a broad spectrum of potent activity against multi-resistant pathogenic bacteria, viruses, fungi, and even cancer. AMPs can be further characterised into families according to amino acid composition, secondary

Protein–protein interaction plays an important role in the understanding of biological processes in the body. A network of dynamic protein complexes within a cell that regulates most biological processes is known as a protein–protein interaction network (PPIN). Complex prediction from PPINs is a challenging task. Most of the previous computation approaches mine cliques, stars, linear and hybrid structures

One important challenge in the post-genomic era is to explore disease mechanisms by efficiently integrating different types of biological data. In fact, a single disease is usually caused through multiple genes products such as protein complexes rather than single gene. Therefore, it is meaningful for us to discover protein communities from the protein–protein interaction network and use them for inferring

piRNAs are a class of small non-coding RNA molecules, which interact with the PIWI family and have many important and diverse biological functions. The present review is aimed to provide guidelines and contribute to piRNA research. We focused on the four types of identification models on piRNA-related molecules, including piRNA, piRNA cluster, piRNA target, and disease-related piRNA. We evaluated the

DNA N6-methyladenine (6 mA), as an essential component of epigenetic modification, cannot be neglected in genetic regulation mechanism. The efficient and accurate prediction of 6 mA sites is beneficial to the development of biological genetics. Biochemical experimental methods are considered to be time-consuming and laborious. Most of the established machine learning methods have a single dataset.

Alzheimer’s disease (AD) is a kind of neurological brain disease. It is an irretrievable, neurodegenerative brain disorder. There are no pills or drugs to cure AD. Therefore, an early diagnosis may help the physician to make accurate analysis and to provide better treatment. With the advent of computational intelligence techniques, machine learning models have made tremendous progress in brain images

Essential proteins are assumed to be an indispensable element in sustaining normal physiological function and crucial to drug design and disease diagnosis. The discovery of essential proteins is of great importance in revealing the molecular mechanisms and biological processes. Owing to the tedious biological experiment, many numerical methods have been developed to discover key proteins by mining

Identification of groups of co-expressed or co-regulated genes is critical for exploring the underlying mechanism behind a particular disease like cancer. Condition-specific (disease-specific) gene-expression profiles acquired from different platforms are widely utilized by researchers to get insight into the regulatory mechanism of the disease. Several clustering algorithms are developed using gene

Progress in single-cell RNA sequencing (scRNA-seq) has yielded a lot of valuable data. Analysis of these data can provide a new perspective for studying the intratumoral heterogeneity and identifying gene markers. In this paper, the scRNA-seq data of colorectal cancer (CRC) are analyzed, and it is found that the shape of the gene expression difference (GED) data shows certain distribution regularity

Discovering relations of cross-type biomedical entities is crucial for biology research. A large amount of potential or indirect connected biological relations is hidden in millions of biomedical literatures and biological databases. The previous rules-based and deep learning approaches rely on plenty of manual annotations, which is laborious, time-consuming and unsatisfactory. It is necessary to be

Background In the broader healthcare domain, the prediction bears more value than an explanation considering the cost of delays in its services. There are various risk prediction models for cardiovascular diseases (CVDs) in the literature for early risk assessment. However, the substantial increase in CVDs-related mortality is challenging global health systems, especially in developing countries. This

Gene sequencing technology has been playing an important role in many aspects, such as life science, disease medicine and health medicine, particularly in the extremely tough process of fighting against 2019-novel coronavirus. Drawing DNA restriction map is a particularly important technology in genetic biology. The simplified partial digestion method (SPDP), a biological method, has been widely used

In the hospital, a limited number of COVID-19 test kits are available due to the spike in cases every day. For this reason, a rapid alternative diagnostic option should be introduced as an automated detection method to prevent COVID-19 spreading among individuals. This article proposes multi-objective optimization and a deep-learning methodology for the detection of infected coronavirus patients with

An important task in the early stage of drug discovery is the identification of mutagenic compounds. Mutagenicity prediction models that can interpret relationships between toxicological endpoints and compound structures are especially favorable. In this research, we used an advanced graph convolutional neural network (GCNN) architecture to identify the molecular representation and develop predictive

Electrocardiogram (ECG) is the most effective instrument for making decisions about various forms of heart disease. As a result, several researchers have focused on the ECG signal to extract the features of heartbeats with high precision and efficiency. This article offers a hybrid approach to classifying different cardiac conditions using the Feed Forward Back Propagation Neural Network (FFBPNN),

Mucopolysaccharidoses are caused by a deficiency of enzymes involved in the degradation of glycosaminoglycans. Heart diseases are a significant cause of morbidity and mortality in MPS patients, even in conditions in which enzyme replacement therapy is available. In this sense, cardiovascular manifestations, such as heart hypertrophy, cardiac function reduction, increased left ventricular chamber, and

The new type of corona virus (SARS-COV-2) emerging in Wuhan, China has spread rapidly to the world and has become a pandemic. In addition to having a significant impact on daily life, it also shows its effect in different areas, including public health and economy. Currently, there is no vaccine or antiviral drug available to prevent the COVID-19 disease. Therefore, determination of protein interactions

Diabetic retinopathy (DR) is one of the most prevalent genetic diseases in human and it is caused by damage to the blood vessels in the eye retina. If it is undetected and untreated at right time, it can lead to vision loss. There are many medical imaging and processing technologies to improve the diagnostic process of DR to overcome the lack of human experts. In the existing image processing methods