Proteins play a variety of functional roles in cellular activities and are indispensable for life. Understanding the functions of proteins is crucial in many fields such as medicine and drug development. In addition, the application of enzymes in green synthesis has been of great interest, but the high cost of obtaining specific functional enzymes as well as the variety of enzyme types and functions hamper their application. At present, the specific functions of proteins are mainly determined through tedious and time-consuming experimental characterization. With the rapid development of bioinformatics and sequencing technologies, the number of protein sequences that have been sequenced is much larger than those can be annotated, thus developing efficient methods for predicting protein functions becomes crucial. With the rapid development of computer technology, data-driven machine learning methods have become a promising solution to these challenges. This review provides an overview of protein function and its annotation methods as well as the development history and operation process of machine learning. In combination with the application of machine learning in the field of enzyme function prediction, we present an outlook on the future direction of efficient artificial intelligence-assisted protein function research.
Artificial Intelligence ; Machine Learning ; Proteins/genetics* ; Computational Biology/methods* ; Drug Development

Rapid and accurate detection technologies are crucial for disease prevention and control. In particular, the COVID-19 pandemic has posed a great threat to our society, highlighting the importance of rapid and highly sensitive detection techniques. In recent years, CRISPR/Cas-based gene editing technique has brought revolutionary advances in biotechnology. Due to its fast, accurate, sensitive, and cost-effective characteristics, the CRISPR-based nucleic acid detection technology is revolutionizing molecular diagnosis. CRISPR-based diagnostics has been applied in many fields, such as detection of infectious diseases, genetic diseases, cancer mutation, and food safety. This review summarized the advances in CRISPR-based nucleic acid detection systems and its applications. Perspectives on intelligent diagnostics with CRISPR-based nucleic acid detection and artificial intelligence were also provided.
Humans ; CRISPR-Cas Systems/genetics* ; COVID-19/genetics* ; Pandemics ; Artificial Intelligence ; Nucleic Acids

BACKGROUND: Excessive exposure to fluoride can reduce intelligence. Methylenetetrahydrofolate dehydrogenase, cyclohydrolase, and formyltetrahydrofolate synthetase 1 ( MTHFD1 ) polymorphisms have important roles in neurodevelopment. However, the association of MTHFD1 polymorphisms with children's intelligence changes in endemic fluorosis areas has been rarely explored. METHODS: A cross-sectional study was conducted in four randomly selected primary schools in Tongxu County, Henan Province, from April to May in 2017. A total of 694 children aged 8 to 12 years were included in the study with the recruitment by the cluster sampling method. Urinary fluoride (UF) and urinary creatinine were separately determined using the fluoride ion-selective electrode and creatinine assay kit. Children were classified as the high fluoride group and control group according to the median of urinary creatinine-adjusted urinary fluoride (UF Cr ) level. Four loci of MTHFD1 were genotyped, and the Combined Raven's Test was used to evaluate children's intelligence quotient (IQ). Generalized linear model and multinomial logistic regression model were performed to analyze the associations between children's UF Cr level, MTHFD1 polymorphisms, and intelligence. The general linear model was used to explore the effects of gene-environment and gene-gene interaction on intelligence. RESULTS: In the high fluoride group, children's IQ scores decreased by 2.502 when the UF Cr level increased by 1.0 mg/L (β = -2.502, 95% confidence interval [CI]:-4.411, -0.593), and the possibility for having "excellent" intelligence decreased by 46.3% (odds ratio = 0.537, 95% CI: 0.290, 0.994). Children with the GG genotype showed increased IQ scores than those with the AA genotype of rs11627387 locus in the high fluoride group ( P   <  0.05). Interactions between fluoride exposure and MTHFD1 polymorphisms on intelligence were observed (Pinteraction < 0.05). CONCLUSION Our findings suggest that excessive fluoride exposure may have adverse effects on children's intelligence, and changes in children's intelligence may be associated with the interaction between fluoride and MTHFD1 polymorphisms.
Child ; Creatinine ; Cross-Sectional Studies ; Fluorides/urine* ; Formate-Tetrahydrofolate Ligase ; Humans ; Intelligence/genetics* ; Methylenetetrahydrofolate Dehydrogenase (NADP) ; Methylenetetrahydrofolate Reductase (NADPH2)

As an important model industrial microorganism, Escherichia coli has been widely used in pharmaceutical, chemical industry and agriculture. In the past 30 years, a variety of new strategies and techniques, including artificial intelligence, gene editing, metabolic pathway assembly, and dynamic regulation have been used to design, construct, and optimize E. coli cell factories, which remarkably improved the efficiency for biotechnological production of chemicals. In this review, three key aspects for constructing E. coli cell factories, including pathway design, pathway assembly and regulation, and optimization of global cellular performance, are summarized. The technologies that have played important roles in metabolic engineering of E. coli, as well as their future applications, are discussed.
Artificial Intelligence ; Escherichia coli/genetics* ; Gene Editing ; Metabolic Engineering ; Metabolic Networks and Pathways/genetics*

Hepatocellular carcinoma (HCC) is the sixth most common malignancy and the fourth leading cause of cancer related death worldwide. China covers over half of cases, leading HCC to be a vital threaten to public health. Despite advances in diagnosis and treatments, high recurrence rate remains a major obstacle in HCC management. Multi-omics currently facilitates surveillance, precise diagnosis, and personalized treatment decision making in clinical setting. Non-invasive radiomics utilizes preoperative radiological imaging to reflect subtle pixel-level pattern changes that correlate to specific clinical outcomes. Radiomics has been widely used in histopathological diagnosis prediction, treatment response evaluation, and prognosis prediction. High-throughput sequencing and gene expression profiling enabled genomics and proteomics to identify distinct transcriptomic subclasses and recurrent genetic alterations in HCC, which would reveal the complex multistep process of the pathophysiology. The accumulation of big medical data and the development of artificial intelligence techniques are providing new insights for our better understanding of the mechanism of HCC via multi-omics, and show potential to convert surgical/intervention treatment into an antitumorigenic one, which would greatly advance precision medicine in HCC management.
Artificial Intelligence ; Carcinoma, Hepatocellular/therapy* ; Gene Expression Profiling ; Humans ; Liver Neoplasms/genetics* ; Prognosis

OBJECTIVE: To explore the genetic basis for a female with a peripheral lymphocyte karyotype of trisomy 18 but normal intelligence. METHODS: G-banding karyotype analysis, fluorescence in situ hybridization (FISH) and single nucleotide polymorphism microarray (SNP array) were employed to analyze the peripheral blood sample and buccal cells from the patient. RESULTS: Chromosomal karyotyping, SNP array and FISH analysis of the patient's peripheral blood all suggested 47,XX,+18. Interphase FISH analysis of buccal cells, however, revealed presence of 45,X and low percentage of trisomy 18 and monosomy 18. CONCLUSION The clinical manifestation of germ layer chromosomal mosaicism is complex. The impact of the genetic disorder on the individual will depend on the structure and function derived from the affected germ layer.
Female ; Humans ; In Situ Hybridization, Fluorescence ; Intelligence ; Karyotype ; Karyotyping ; Lymphocytes ; Mosaicism ; Mouth Mucosa ; Polymorphism, Single Nucleotide ; Trisomy 18 Syndrome ; genetics

Attention deficit hyperactivity disorder (ADHD) is a form of neuronal dysplasia featuring high hereditary (up to 76%). This paper reviews recent progress made in genetic research on the cognitive function in ADHD. Two aspects of cognitive function were explored from the perspective of genetics, including intelligence and executive function.
Attention Deficit Disorder with Hyperactivity ; genetics ; Cognition ; Executive Function ; Genetic Research ; Humans ; Intelligence

OBJECTIVETo assess the value of chromosomal microarray analysis (CMA) for the diagnosis of children with intellectual disability/developmental delay (ID/DD) but a normal karytype.

METHODSPeripheral blood samples from 92 ID/DD patients were analyzed with CMA using Affymetrix CytoScan 750K arrays. The results were analyzed by ChAS v3.0 software.

RESULTSEighteen cases (19.57%) were detected with abnormalities by CMA, among which 10 cases were diagnosed with microdeletion/microduplication syndromes. These included 2 Williams-Beuren syndromes, 2 Angelman syndromes, 2 Russell-Silver syndromes, 1 Smith-Magenis syndromes, 1 Wolf-Hirschhorn syndromes, 1 15q26 overgrowth syndrome and 1 Xq28 (MECP2) duplication syndrome. In addition, 8 cases were diagnosed with pathogenic copy number variations (pCNV).

CONCLUSIONCMA can significantly improve the diagnostic rate for patients with ID/DD, which is of great value for the treatment of such children and guidance of reproduction for their parents. Therefore, CMA should become the first-line diagnostic test for patients with ID/DD.

Adolescent ; Adult ; Child ; Child, Preschool ; DNA Copy Number Variations ; Developmental Disabilities ; genetics ; psychology ; Female ; Humans ; Intellectual Disability ; genetics ; psychology ; Intelligence ; Karyotype ; Male ; Microarray Analysis ; Middle Aged ; Pedigree ; Young Adult

By combining with informatics theory, ta system model consisting of feature selection which is based on redundancy and correlation is presented to develop disease classification research with five gene data set (NCI, Lymphoma, Lung, Leukemia, Colon). The result indicates that this modeling method can not only reduce data management computation amount, but also help confirming amount of features, further more improve classification accuracy, and the application of this model has a bright foreground in fields of disease analysis and individual treatment project establishment.
Algorithms ; Artificial Intelligence ; Data Mining ; Gene Expression Profiling ; methods ; Informatics ; Neoplasms ; classification ; genetics

In this paper, we present a brief review of the existing computational methods for predicting proteome-wide protein-protein interaction networks from high-throughput data. The availability of various types of omics data provides great opportunity and also unprecedented challenge to infer the interactome in cells. Reconstructing the interactome or interaction network is a crucial step for studying the functional relationship among proteins and the involved biological processes. The protein interaction network will provide valuable resources and alternatives to decipher the mechanisms of these functionally interacting elements as well as the running system of cellular operations. In this paper, we describe the main steps of predicting protein-protein interaction networks and categorize the available approaches to couple the physical and functional linkages. The future topics and the analyses beyond prediction are also discussed and concluded.
Algorithms ; Artificial Intelligence ; Humans ; Models, Biological ; Protein Interaction Domains and Motifs ; Protein Interaction Mapping ; Protein Interaction Maps ; Proteome ; genetics ; metabolism ; Proteomics ; Systems Biology