MBB-MOGWO: Modified Boltzmann-Based Multi-Objective Grey Wolf Optimizer.

The primary objective of multi-objective optimization techniques is to identify optimal solutions within the context of conflicting objective functions. While the multi-objective gray wolf optimization (MOGWO) algorithm has been widely adopted for its superior performance in solving multi-objective optimization problems, it tends to encounter challenges such as local optima and slow convergence in the later stages of optimization. To address these issues, we propose a Modified Boltzmann-Based MOGWO, referred to as MBB-MOGWO. The performance of the proposed algorithm is evaluated on multiple multi-objective test functions. Experimental results demonstrate that MBB-MOGWO exhibits rapid convergence and a reduced likelihood of being trapped in local optima. Furthermore, in the context of the Internet of Things (IoT), the quality of web service composition significantly impacts complexities related to sensor resource scheduling. To showcase the optimization capabilities of MBB-MOGWO in real-world scenarios, the algorithm is applied to address a Multi-Objective Problem (MOP) within the domain of web service composition, utilizing real data records from the QWS dataset. Comparative analyses with four representative algorithms reveal distinct advantages of our MBB-MOGWO-based method, particularly in terms of solution precision for web service composition. The solutions obtained through our method demonstrate higher fitness and improved service quality.

Semantic consistency generative adversarial network for cross-modality domain adaptation in ultrasound thyroid nodule classification.

Deep convolutional networks have been widely used for various medical image processing tasks. However, the performance of existing learning-based networks is still limited due to the lack of large training datasets. When a general deep model is directly deployed to a new dataset with heterogeneous features, the effect of domain shifts is usually ignored, and performance degradation problems occur. In this work, by designing the semantic consistency generative adversarial network (SCGAN), we propose a new multimodal domain adaptation method for medical image diagnosis. SCGAN performs cross-domain collaborative alignment of ultrasound images and domain knowledge. Specifically, we utilize a self-attention mechanism for adversarial learning between dual domains to overcome visual differences across modal data and preserve the domain invariance of the extracted semantic features. In particular, we embed nested metric learning in the semantic information space, thus enhancing the semantic consistency of cross-modal features. Furthermore, the adversarial learning of our network is guided by a discrepancy loss for encouraging the learning of semantic-level content and a regularization term for enhancing network generalization. We evaluate our method on a thyroid ultrasound image dataset for benign and malignant diagnosis of nodules. The experimental results of a comprehensive study show that the accuracy of the SCGAN method for the classification of thyroid nodules reaches 94.30%, and the AUC reaches 97.02%. These results are significantly better than the state-of-the-art methods.

Unraveling the Mechanism of Near-Infrared Thermally Activated Delayed Fluorescence of TPA-Based Molecules: Effect of Hydrogen Bond Steric Hindrance.

A recently synthesized novel molecule (named CAT-1) exhibits intriguing near-infrared (NIR) thermally activated delayed fluorescence (TADF) close to 1000 nm wavelength; however, the mechanism behind these intrinsic properties is not fully understood. Herein, we unravel that the fluorescence emission spectrum with a broad wavelength range (770-950 nm) of CAT-1 is primarily induced by hydrogen bond steric hindrance based on density functional theory and Marcus theory. It is found that the hydrogen bond steric hindrance plays a critical role in inhibiting the twist of the configuration of different excited states, which leads to the minor driving force for fast electron trapping between the excited states, as well as small internal reorganization energy caused by less changed geometric configuration. Furthermore, such steric hindrance will cause a more distorted plane, resulting in a less favorable electron delocalization. A faster reverse intersystem crossing (RISC) rate is then obtained due to the nearly unchanged conformation between excited states caused by steric hindrance, although the spin-orbit coupling is small. Consequently, the NIR TADF with a longer wavelength can be emitted in CAT-1. This work shows that the hydrogen bond steric hindrance can fine-tune the electronic interactions of the donor and acceptor units to control the TADF.

MCtandem: an efficient tool for large-scale peptide identification on many integrated core (MIC) architecture.

BACKGROUND: Tandem mass spectrometry (MS/MS)-based database searching is a widely acknowledged and widely used method for peptide identification in shotgun proteomics. However, due to the rapid growth of spectra data produced by advanced mass spectrometry and the greatly increased number of modified and digested peptides identified in recent years, the current methods for peptide database searching cannot rapidly and thoroughly process large MS/MS spectra datasets. A breakthrough in efficient database search algorithms is crucial for peptide identification in computational proteomics. RESULTS: This paper presents MCtandem, an efficient tool for large-scale peptide identification on Intel Many Integrated Core (MIC) architecture. To support big data processing capability, a novel parallel match scoring algorithm, named MIC-SDP (spectrum dot product), and its two-level parallelization are presented in MCtandem's design. In addition, a series of optimization strategies on both the host CPU side and the MIC side, which includes pre-fetching, optimized communication overlapping scheme, multithreading and hyper-threading, are exploited to improve the execution performance. CONCLUSIONS: For fair comparisons, we first set up experiments and verified the 28 fold times speedup on a single MIC against the original CPU-based implementation. We then execute the MCtandem for a very large dataset on an MIC cluster (a component of the Tianhe-2 supercomputer) and achieved much higher scalability than in a benchmark MapReduce-based programs, MR-Tandem. MCtandem is an open-source software tool implemented in C++. The source code and the parameter settings are available at https://github.com/LogicZY/MCtandem .

Hausdorff Distance Model-Based Identity Authentication for IP Circuits in Service-Centric Internet-of-Things Environment.

Rapid advances in the Internet-of-Things (IoT) have exposed the underlying hardware devices to security threats. As the major component of hardware devices, the integrated circuit (IC) chip also suffers the threat of illegal, malicious attacks. To protect against attacks and vulnerabilities of a chip, a credible authentication is of fundamental importance. In this paper, we propose a Hausdorff distance-based method to authenticate the identity of IC chips in IoT environments, where the structure is analyzed, and the lookup table (LUT) resources are treated as a set of reconfigurable nodes in field programmable gate array (FPGA)-based IC design. Unused LUT resources are selected for insertion of the copyright information by using the depth-first search algorithm, and the random positions are reordered with the Hausdorff distance matching function next, so these positions are mapped to satisfy the specific constraints of the optimal watermark positions. If the authentication process is activated, virtual positions are mapped to the initial key file, yet the identity of the IC designed can be authenticated using the mapping relationship of the Hausdorff distance function. Experimental results show that the proposed method achieves good randomness and secrecy in watermark embedding, as well the extra resource overhead caused by watermarks are promising.

Insights into the Functions of LncRNAs in Drosophila.

Long non-coding RNAs (lncRNAs) are a class of non-coding RNAs longer than 200 nucleotides (nt). LncRNAs have high spatiotemporal specificity, and secondary structures have been preserved throughout evolution. They have been implicated in a range of biological processes and diseases and are emerging as key regulators of gene expression at the epigenetic, transcriptional, and post-transcriptional levels. Comparative analyses of lncRNA functions among multiple organisms have suggested that some of their mechanisms seem to be conserved. Transcriptome studies have found that some Drosophila lncRNAs have highly specific expression patterns in embryos, nerves, and gonads. In vivo studies of lncRNAs have revealed that dysregulated expression of lncRNAs in Drosophila may result in impaired embryo development, impaired neurological and gonadal functions, and poor stress resistance. In this review, we summarize the epigenetic, transcriptional, and post-transcriptional mechanisms of lncRNAs and mainly focus on recent insights into the transcriptome studies and biological functions of lncRNAs in Drosophila.

3-Aminobenzamide protects against cerebral artery injury and inflammation in rats with intracranial aneurysms.

This study aimed to investigate the treatment effects and molecular mechanism of 3-aminobenzamide (3-AB) on intracranial aneurysms (IA). The IA model was established in male Sprague-Dawley (SD) rats and sham group was set up without ligation. The rats were intraperitoneally injected with normal saline in sham and model control groups and 10 mg/kg, 20 mg/kg and 40 mg/kg 3-AB for low, middle and high 3-AB groups for 3 months, respectively. The rates in and blood pressures of caudal artery were measured and anterior cerebral artery and olfactory artery were stained with hematoxylin and eosin (HE) for morphology observation. Besides, the effects of 3-AB on inflammatory cells, macrophages, neutrophils and T cells, were evaluated using immunohistochemistry. Gene expressions of TNF-α, MMP-9, MMP-2, iNOS, TLR4, PARP-1 and p65 were measured using qRT-PCR and the protein levels of TLR4, PARP-1 and p-p65 were evaluated using western blotting. Blood pressures of rats in 3-AB treatment groups were decreased in a dose-dependent manner. The damage of cerebral artery wall was alleviated and the inflammatory cells (macrophages, neutrophils and T cells) were reduced to some extent in 3-AB high-dose groups. The gene expression of TNF-α, MMP-9, MMP-2, iNOS, TLR4, PARP-1 and p65, as well as the protein expression of TLR4, PARP-1 and p-p65 in 3-AB treatment groups were decreased in a dose-dependent manner (P < 0.01).3-AB exhibited therapeutic effects on IA through inhibiting the secretions of inflammatory cytokines and MMPs.

Seeksv: an accurate tool for somatic structural variation and virus integration detection.

MOTIVATION: Many forms of variations exist in the human genome including single nucleotide polymorphism, small insert/deletion (DEL) (indel) and structural variation (SV). Somatically acquired SV may regulate the expression of tumor-related genes and result in cell proliferation and uncontrolled growth, eventually inducing tumor formation. Virus integration with host genome sequence is a type of SV that causes the related gene instability and normal cells to transform into tumor cells. Cancer SVs and viral integration sites must be discovered in a genome-wide scale for clarifying the mechanism of tumor occurrence and development. RESULTS: In this paper, we propose a new tool called seeksv to detect somatic SVs and viral integration events. Seeksv simultaneously uses split read signal, discordant paired-end read signal, read depth signal and the fragment with two ends unmapped. Seeksv can detect DEL, insertion, inversion and inter-chromosome transfer at single-nucleotide resolution. Different types of sequencing data, such as single-end sequencing data or paired-end sequencing data can accommodate to detect SV. Seeksv develops a rescue model for SV with breakpoints located in sequence homology regions. Results on simulated and real data from the 1000 Genomes Project and esophageal squamous cell carcinoma samples show that seeksv has higher efficiency and precision compared with other similar software in detecting SVs. For the discovery of hepatitis B virus integration sites from probe capture data, the verified experiments show that more than 90% viral integration sequences detected by seeksv are true. AVAILABILITY AND IMPLEMENTATION: seeksv is implemented in C ++ and can be downloaded from https://github.com/qkl871118/seeksv CONTACT: : dragonbw@163.comSupplementary information: Supplementary data are available at Bioinformatics online.

Innovative method for the enrichment of high-polarity bioactive molecules present at low concentrations in complex matrices.

Ginsenoside Rg1 is a valuable bioactive molecule but its high polarity and low concentration in complex mixtures makes it a challenge to separate Ginsenoside Rg1 from other saponins with similar structures, resulting in low extraction efficiency. The successful development of effective Rg1 molecularly imprinted polymers that exhibit high selectivity and adsorption may offer an improved method for the enrichment of active compounds. In this work, molecularly imprinted polymers were prepared with two different methods, precipitation polymerization or surface imprinted polymerization. Comparison of the adsorption abilities showed higher adsorption of the surface molecularly imprinted polymers prepared by surface imprinted polymerization, 46.80 mg/g, compared to the 27.74 mg/g observed for the molecularly imprinted polymers prepared by precipitation polymerization. Therefore, for higher adsorption of the highly polar Rg1, surface imprinted polymerization is a superior technique to make Rg1 molecularly imprinted polymers. The prepared surface molecularly imprinted polymers were tested as a solid-phase extraction column to directionally enrich Rg1 and its analogues from ginseng tea and total ginseng extracts. The column with surface molecularly imprinted polymers showed higher enrichment efficiency and better selectivity than a C18 solid-phase extraction column. Overall, a new, innovative method was developed to efficiently enrich high-polarity bioactive molecules present at low concentrations in complex matrices.

An arginine-rich motif of ring finger protein 4 (RNF4) oversees the recruitment and degradation of the phosphorylated and SUMOylated Krüppel-associated box domain-associated protein 1 (KAP1)/TRIM28 protein during genotoxic stress.

Krüppel-associated box domain-associated protein 1 (KAP1) is a universal transcriptional corepressor that undergoes multiple posttranslational modifications (PTMs), including SUMOylation and Ser-824 phosphorylation. However, the functional interplay of KAP1 PTMs in regulating KAP1 turnover during DNA damage response remains unclear. To decipher the role and cross-talk of multiple KAP1 PTMs, we show here that DNA double strand break-induced KAP1 Ser-824 phosphorylation promoted the recruitment of small ubiquitin-like modifier (SUMO)-targeted ubiquitin E3 ligase, ring finger protein 4 (RNF4), and subsequent RNF4-mediated, SUMO-dependent degradation. Besides the SUMO interacting motif (SIM), a previously unrecognized, but evolutionarily conserved, arginine-rich motif (ARM) in RNF4 acts as a novel recognition motif for selective target recruitment. Results from combined mutagenesis and computational modeling studies suggest that RNF4 utilizes concerted bimodular recognition, namely SIM for Lys-676 SUMOylation and ARM for Ser(P)-824 of simultaneously phosphorylated and SUMOylated KAP1 (Ser(P)-824-SUMO-KAP1). Furthermore, we proved that arginines 73 and 74 within the ARM of RNF4 are required for efficient recruitment to KAP1 or accelerated degradation of promyelocytic leukemia protein (PML) under stress. In parallel, results of bimolecular fluorescence complementation assays validated the role of the ARM in recognizing Ser(P)-824 in living cells. Taken together, we establish that the ARM is required for RNF4 to efficiently target Ser(P)-824-SUMO-KAP1, conferring ubiquitin Lys-48-mediated proteasomal degradation in the context of double strand breaks. The conservation of such a motif may possibly explain the requirement for timely substrate selectivity determination among a myriad of SUMOylated proteins under stress conditions. Thus, the ARM dynamically regulates the SIM-dependent recruitment of targets to RNF4, which could be critical to dynamically fine-tune the abundance of Ser(P)-824-SUMO-KAP1 and, potentially, other SUMOylated proteins during DNA damage response.

Efficacy and safety of B-mode ultrasound-guided percutaneous transhepatic gallbladder drainage combined with laparoscopic cholecystectomy for acute cholecystitis in elderly and high-risk patients.

BACKGROUND: Standards in treatment of acute cholecystitis (AC) in the elderly and high-risk patients has not been established. Our study evaluated the efficacy and safety of B-mode ultrasound-guided percutaneous transhepatic gallbladder drainage (PTGD) in combination with laparoscopic cholecystectomy (LC) for acute cholecystitis (AC) in elderly and high-risk patients. METHODS: Our study enrolled 35 elderly and high-risk AC patients, hospitalized between January 2010 and April 2014 at the Wenzhou People's Hospital. The patients underwent B-mode ultrasound-guided PTGD and LC (PTGD + LC group). As controls, a separate group of 35 elderly and high-risk AC patients who underwent LC alone (LC group) during the same period at the same hospital were randomly selected from a pool of 186 such cases. The volume of bleeding, surgery time, postoperative length of stay, conversion rate to laparotomy and complication rates (bile leakage, bleeding, incisional hernia, incision infection, pulmonary infarction and respiratory failure) were recorded for each patient in the two groups. RESULTS: All patients in the PTGD + LC group successfully underwent PTGD. In the PTGD + LC group, abdominal pain in patients was relieved and leukocyte count, alkaline phosphatase level, total bilirubin and carbohydrate antigen 19-9 (CA19-9) decreased to normal range, and alanine aminotransferase and aspartate aminotransferase levels improved significantly within 72 h after treatment. All patients in the PTGD + LC group underwent LC within 6-10 weeks after PTGD. Our study revealed that PTGD + LC showed a significantly higher efficacy and safety compared to LC alone in AC treatment, as measured by the following parameters: duration of operation, postoperative length of hospital stay, volume of bleeding, conversion rate to laparotomy and complication rate (operation time of LC: 55.6 ± 23.3 min vs. 91.35 ± 25.1 min; hospitalized period after LC: 3.0 ± 1.3 d vs. 7.0 ± 1.7 d; intraoperative bleeding: 28.7 ± 15.2 ml vs. 60.38 ± 16.4 ml; conversion to laparotomy: 3 cases vs. 10 cases; complication: 3 cases vs. 8 cases; all P < 0.05 ). CONCLUSION: Our results suggest that B-mode ultrasound-guided PTGD in combination with LC is superior to LC alone for treatment of AC in elderly and high-risk patients, showing multiple advantages of minimal wounding, accelerated recovery, higher safety and efficacy, and fewer complications.

miR-495 mediates metabolic shift in glioma cells via targeting Glut1.

Cancer cell metabolism is often characterized by a shift from an oxidative to a glycolytic bioenergetics pathway, a phenomenon known as the Warburg effect. Whether the deregulation of microRNAs contributes to the Warburg effect remains largely unknown. Here, we show that miR-495 expression is decreased and thus induces a metabolic shift in glioma cells. miR-495 performs this function by increasing the expression of Glut1, leading to the increase of glucose uptake and lactate production. The altered metabolism induced by miR-495 results in the rapid growth of cancer cells. These results identify miR-495 as a molecular switch involved in the orchestration of the Warburg effect in glioma cells via targeting the expression of Glut1.

[Analysis on signaling pathway network of proliferation of neural stem cells].

Neural stem cells in brains have capacities of proliferation and differentiation, which is very critical to rebuild the cerebral cortex functions. Therefore, it is of great importance to find key targets and network pathways that regulate the proliferation of neural stem cells, which is also a pressing problem in the medical circle. With the Notch pathway as the core of the network, this paper summarized the advance of the bimolecular network system composed of Wnt, Shh, EGFR, cytokines and Notch signal, and analyzed such key nodes as Notch receptor, CBF1, NICD, Hesl, which may become potential targets of new-type drugs in the future. With the multi-component, multi-target, multi-lever characteristics, traditional Chinese medicines have many common grounds with the network pharmacology. The active component groups or active ingredients in traditional Chinese medicines are one of the material bases for showing their network pharmacological effect, which is worth exploring. This paper aims to provide a new strategy for the treatment of neurodegenerative disease and nerve injury with traditional Chinese medicines.

DER-GCN: Dialog and Event Relation-Aware Graph Convolutional Neural Network for Multimodal Dialog Emotion Recognition.

With the continuous development of deep learning (DL), the task of multimodal dialog emotion recognition (MDER) has recently received extensive research attention, which is also an essential branch of DL. The MDER aims to identify the emotional information contained in different modalities, e.g., text, video, and audio, and in different dialog scenes. However, the existing research has focused on modeling contextual semantic information and dialog relations between speakers while ignoring the impact of event relations on emotion. To tackle the above issues, we propose a novel dialog and event relation-aware graph convolutional neural network (DER-GCN) for multimodal emotion recognition method. It models dialog relations between speakers and captures latent event relations information. Specifically, we construct a weighted multirelationship graph to simultaneously capture the dependencies between speakers and event relations in a dialog. Moreover, we also introduce a self-supervised masked graph autoencoder (SMGAE) to improve the fusion representation ability of features and structures. Next, we design a new multiple information Transformer (MIT) to capture the correlation between different relations, which can provide a better fuse of the multivariate information between relations. Finally, we propose a loss optimization strategy based on contrastive learning to enhance the representation learning ability of minority class features. We conduct extensive experiments on the benchmark datasets, Interactive Emotional Dyadic Motion Capture (IEMOCAP) and Multimodal EmotionLines Dataset (MELD), which verify the effectiveness of the DER-GCN model. The results demonstrate that our model significantly improves both the average accuracy and the F1 value of emotion recognition. Our code is publicly available at https://github.com/yuntaoshou/DER-GCN.

ParaCPI: A Parallel Graph Convolutional Network for Compound-Protein Interaction Prediction.

Identifying compound-protein interactions (CPIs) is critical in drug discovery, as accurate prediction of CPIs can remarkably reduce the time and cost of new drug development. The rapid growth of existing biological knowledge has opened up possibilities for leveraging known biological knowledge to predict unknown CPIs. However, existing CPI prediction models still fall short of meeting the needs of practical drug discovery applications. A novel parallel graph convolutional network model for CPI prediction (ParaCPI) is proposed in this study. This model constructs feature representation of compounds using a unique approach to predict unknown CPIs from known CPI data more effectively. Experiments are conducted on five public datasets, and the results are compared with current state-of-the-art (SOTA) models under three different experimental settings to evaluate the model's performance. In the three cold-start settings, ParaCPI achieves an average performance gain of 26.75%, 23.84%, and 14.68% in terms of area under the curve compared with the other SOTA models. In addition, the results of the experiments in the case study show ParaCPI's superior ability to predict unknown CPIs based on known data, with higher accuracy and stronger generalization compared with the SOTA models. Researchers can leverage ParaCPI to accelerate the drug discovery process.

XAI Driven Intelligent IoMT Secure Data Management Framework.

The Internet of Medical Things (IoMT) has transformed traditional healthcare systems by enabling real-time monitoring, remote diagnostics, and data-driven treatment. However, security and privacy remain significant concerns for IoMT adoption due to the sensitive nature of medical data. Therefore, we propose an integrated framework leveraging blockchain and explainable artificial intelligence (XAI) to enable secure, intelligent, and transparent management of IoMT data. First, the traceability and tamper-proof of blockchain are used to realize the secure transaction of IoMT data, transforming the secure transaction of IoMT data into a two-stage Stackelberg game. The dual-chain architecture is used to ensure the security and privacy protection of the transaction. The main-chain manages regular IoMT data transactions, while the side-chain deals with data trading activities aimed at resale. Simultaneously, the perceptual hash technology is used to realize data rights confirmation, which maximally protects the rights and interests of each participant in the transaction. Subsequently, medical time-series data is modeled using bidirectional simple recurrent units to detect anomalies and cyberthreats accurately while overcoming vanishing gradients. Lastly, an adversarial sample generation method based on local interpretable model-agnostic explanations is provided to evaluate, secure, and improve the anomaly detection model, as well as to make it more explainable and resilient to possible adversarial attacks. Simulation results are provided to illustrate the high performance of the integrated secure data management framework leveraging blockchain and XAI, compared with the benchmarks.

Enhancing drug-food interaction prediction with precision representations through multilevel self-supervised learning.

Drug-food interactions (DFIs) crucially impact patient safety and drug efficacy by modifying absorption, distribution, metabolism, and excretion. The application of deep learning for predicting DFIs is promising, yet the development of computational models remains in its early stages. This is mainly due to the complexity of food compounds, challenging dataset developers in acquiring comprehensive ingredient data, often resulting in incomplete or vague food component descriptions. DFI-MS tackles this issue by employing an accurate feature representation method alongside a refined computational model. It innovatively achieves a more precise characterization of food features, a previously daunting task in DFI research. This is accomplished through modules designed for perturbation interactions, feature alignment and domain separation, and inference feedback. These modules extract essential information from features, using a perturbation module and a feature interaction encoder to establish robust representations. The feature alignment and domain separation modules are particularly effective in managing data with diverse frequencies and characteristics. DFI-MS stands out as the first in its field to combine data augmentation, feature alignment, domain separation, and contrastive learning. The flexibility of the inference feedback module allows its application in various downstream tasks. Demonstrating exceptional performance across multiple datasets, DFI-MS represents a significant advancement in food presentations technology. Our code and data are available at https://github.com/kkkayle/DFI-MS.

Dual-View Learning Based on Images and Sequences for Molecular Property Prediction.

The prediction of molecular properties remains a challenging task in the field of drug design and development. Recently, there has been a growing interest in the analysis of biological images. Molecular images, as a novel representation, have proven to be competitive, yet they lack explicit information and detailed semantic richness. Conversely, semantic information in SMILES sequences is explicit but lacks spatial structural details. Therefore, in this study, we focus on and explore the relationship between these two types of representations, proposing a novel multimodal architecture named ISMol. ISMol relies on a cross-attention mechanism to extract information representations of molecules from both images and SMILES strings, thereby predicting molecular properties. Evaluation results on 14 small molecule ADMET datasets indicate that ISMol outperforms machine learning (ML) and deep learning (DL) models based on single-modal representations. In addition, we analyze our method through a large number of experiments to test the superiority, interpretability and generalizability of the method. In summary, ISMol offers a powerful deep learning toolbox for drug discovery in a variety of molecular properties.

An overview of detecting gene-trait associations by integrating GWAS summary statistics and eQTLs.

Detecting genes that affect specific traits (such as human diseases and crop yields) is important for treating complex diseases and improving crop quality. A genome-wide association study (GWAS) provides new insights and directions for understanding complex traits by identifying important single nucleotide polymorphisms. Many GWAS summary statistics data related to various complex traits have been gathered recently. Studies have shown that GWAS risk loci and expression quantitative trait loci (eQTLs) often have a lot of overlaps, which makes gene expression gradually become an important intermediary to reveal the regulatory role of GWAS. In this review, we review three types of gene-trait association detection methods of integrating GWAS summary statistics and eQTLs data, namely colocalization methods, transcriptome-wide association study-oriented approaches, and Mendelian randomization-related methods. At the theoretical level, we discussed the differences, relationships, advantages, and disadvantages of various algorithms in the three kinds of gene-trait association detection methods. To further discuss the performance of various methods, we summarize the significant gene sets that influence high-density lipoprotein, low-density lipoprotein, total cholesterol, and triglyceride reported in 16 studies. We discuss the performance of various algorithms using the datasets of the four lipid traits. The advantages and limitations of various algorithms are analyzed based on experimental results, and we suggest directions for follow-up studies on detecting gene-trait associations.

DeepCBA: A deep learning framework for gene expression prediction in maize based on DNA sequences and chromatin interactions.

Chromatin interactions create spatial proximity between distal regulatory elements and target genes in the genome, which has an important impact on gene expression, transcriptional regulation, and phenotypic traits. To date, several methods have been developed for predicting gene expression. However, existing methods do not take into consideration the effect of chromatin interactions on target gene expression, thus potentially reducing the accuracy of gene expression prediction and mining of important regulatory elements. In this study, we developed a highly accurate deep learning-based gene expression prediction model (DeepCBA) based on maize chromatin interaction data. Compared with existing models, DeepCBA exhibits higher accuracy in expression classification and expression value prediction. The average Pearson correlation coefficients (PCCs) for predicting gene expression using gene promoter proximal interactions, proximal-distal interactions, and both proximal and distal interactions were 0.818, 0.625, and 0.929, respectively, representing an increase of 0.357, 0.16, and 0.469 over the PCCs obtained with traditional methods that use only gene proximal sequences. Some important motifs were identified through DeepCBA; they were enriched in open chromatin regions and expression quantitative trait loci and showed clear tissue specificity. Importantly, experimental results for the maize flowering-related gene ZmRap2.7 and the tillering-related gene ZmTb1 demonstrated the feasibility of DeepCBA for exploration of regulatory elements that affect gene expression. Moreover, promoter editing and verification of two reported genes (ZmCLE7 and ZmVTE4) demonstrated the utility of DeepCBA for the precise design of gene expression and even for future intelligent breeding. DeepCBA is available at http://www.deepcba.com/ or http://124.220.197.196/.