SADeepcry: a deep learning framework for protein crystallization propensity prediction using self-attention and auto-encoder networks.

The X-ray diffraction (XRD) technique based on crystallography is the main experimental method to analyze the three-dimensional structure of proteins. The production process of protein crystals on which the XRD technique relies has undergone multiple experimental steps, which requires a lot of manpower and material resources. In addition, studies have shown that not all proteins can form crystals under experimental conditions, and the success rate of the final crystallization of proteins is only <10%. Although some protein crystallization predictors have been developed, not many tools capable of predicting multi-stage protein crystallization propensity are available and the accuracy of these tools is not satisfactory. In this paper, we propose a novel deep learning framework, named SADeepcry, for predicting protein crystallization propensity. The framework can be used to estimate the three steps (protein material production, purification and crystallization) in protein crystallization experiments and the success rate of the final protein crystallization. SADeepcry uses the optimized self-attention and auto-encoder modules to extract sequence, structure and physicochemical features from the proteins. Compared with other state-of-the-art protein crystallization propensity prediction models, SADeepcry can obtain more complex global spatial long-distance dependence of protein sequence information. Our computational results show that SADeepcry has increased Matthews correlation coefficient and area under the curve, by 100.3% and 13.4%, respectively, over the DCFCrystal method on the benchmark dataset. The codes of SADeepcry are available at https://github.com/zhc940702/SADeepcry.

Drug repositioning based on multi-view learning with matrix completion.

Determining drug indications is a critical part of the drug development process. However, traditional drug discovery is expensive and time-consuming. Drug repositioning aims to find potential indications for existing drugs, which is considered as an important alternative to the traditional drug discovery. In this article, we propose a multi-view learning with matrix completion (MLMC) method to predict the potential associations between drugs and diseases. Specifically, MLMC first learns the comprehensive similarity matrices from five drug similarity matrices and two disease similarity matrices based on the multi-view learning (ML) with Laplacian graph regularization, and updates the drug-disease association matrix simultaneously. Then, we introduce matrix completion (MC) to add some positive entries in original association matrix based on low-rank structure, and re-execute the multi-view learning algorithm for association prediction. At last, the prediction results of the above two operations are integrated as the final output. Evaluated by 10-fold cross-validation and de novo tests, MLMC achieves higher prediction accuracy than the current state-of-the-art methods. Moreover, case studies confirm the ability of our method in novel drug-disease association discovery. The codes of MLMC are available at https://github.com/BioinformaticsCSU/MLMC. Contact: jxwang@mail.csu.edu.cn.

NASMDR: a framework for miRNA-drug resistance prediction using efficient neural architecture search and graph isomorphism networks.

As a frontier field of individualized therapy, microRNA (miRNA) pharmacogenomics facilitates the understanding of different individual responses to certain drugs and provides a reasonable reference for clinical treatment. However, the known drug resistance-associated miRNAs are not yet sufficient to support precision medicine. Although existing methods are effective, they all focus on modelling miRNA-drug resistance interaction graphs, making their performance bounded by the interaction density. In this study, we propose a framework for miRNA-drug resistance prediction through efficient neural architecture search and graph isomorphism networks (NASMDR). NASMDR uses attribute information instead of the commonly used interactive graph information. In the cross-validation experiment, the proposed framework can achieve an AUC of 0.9468 on the ncDR dataset, which is 2.29% higher than the state-of-the-art method. In addition, we propose a novel sequence characterization approach, k-mer Sparse Nonnegative Matrix Factorization (KSNMF). The results show that NASMDR provides novel insights for integrating efficient neural architecture search and graph isomorphic networks into a unified framework to predict drug resistance-related miRNAs. The codes for NASMDR are available at https://github.com/kaizheng-academic/NASMDR.

A similarity-based deep learning approach for determining the frequencies of drug side effects.

The side effects of drugs present growing concern attention in the healthcare system. Accurately identifying the side effects of drugs is very important for drug development and risk assessment. Some computational models have been developed to predict the potential side effects of drugs and provided satisfactory performance. However, most existing methods can only predict whether side effects will occur and cannot determine the frequency of side effects. Although a few existing methods can predict the frequency of drug side effects, they strongly depend on the known drug-side effect relationships. Therefore, they cannot be applied to new drugs without known side effect frequency information. In this paper, we develop a novel similarity-based deep learning method, named SDPred, for determining the frequencies of drug side effects. Compared with the existing state-of-the-art models, SDPred integrates rich features and can be applied to predict the side effect frequencies of new drugs without any known drug-side effect association or frequency information. To our knowledge, this is the first work that can predict the side effect frequencies of new drugs in the population. The comparison results indicate that SDPred is much superior to all previously reported models. In addition, some case studies also demonstrate the effectiveness of our proposed method in practical applications. The SDPred software and data are freely available at https://github.com/zhc940702/SDPred, https://zenodo.org/record/5112573 and https://hub.docker.com/r/zhc940702/sdpred.

MSDRP: a deep learning model based on multisource data for predicting drug response.

MOTIVATION: Cancer heterogeneity drastically affects cancer therapeutic outcomes. Predicting drug response in vitro is expected to help formulate personalized therapy regimens. In recent years, several computational models based on machine learning and deep learning have been proposed to predict drug response in vitro. However, most of these methods capture drug features based on a single drug description (e.g. drug structure), without considering the relationships between drugs and biological entities (e.g. target, diseases, and side effects). Moreover, most of these methods collect features separately for drugs and cell lines but fail to consider the pairwise interactions between drugs and cell lines. RESULTS: In this paper, we propose a deep learning framework, named MSDRP for drug response prediction. MSDRP uses an interaction module to capture interactions between drugs and cell lines, and integrates multiple associations/interactions between drugs and biological entities through similarity network fusion algorithms, outperforming some state-of-the-art models in all performance measures for all experiments. The experimental results of de novo test and independent test demonstrate the excellent performance of our model for new drugs. Furthermore, several case studies illustrate the rationality for using feature vectors derived from drug similarity matrices from multisource data to represent drugs and the interpretability of our model. AVAILABILITY AND IMPLEMENTATION: The codes of MSDRP are available at https://github.com/xyzhang-10/MSDRP.

Comparison of Remimazolam and Propofol on Postoperative Delirium in Elderly Patients Undergoing Radical Resection of Colon Cancer: A Single-Center Prospective Randomized Controlled Study.

BACKGROUND We compared the effect of remimazolam and propofol intravenous anesthesia on postoperative delirium in elderly patients undergoing laparoscopic radical resection of colon cancer. MATERIAL AND METHODS One hundred patients undergoing elective radical operation of colon cancer under general anesthesia were divided into a remimazolam group (group R) and propofol group (group P) by a random number table method. During anesthesia induction and maintenance, group R was intravenously injected with remimazolam to exert sedation; however, in group P, propofol was injected instead of remimazolam. The occurrence of postoperative delirium was assessed with the Confusion Assessment Method for the Intensive Care Unit scale and postoperative pain was assessed with the visual analogue score (VAS). The primary outcome measures were the incidence and duration of delirium within 7 days following surgery. Secondary outcome measures included postoperative VAS scores, intraoperative anesthetic drug dosage, and adverse reactions, including nausea and vomiting, hypoxemia, and respiratory depression. RESULTS There was no significant difference in baseline data between the 2 groups (P>0.05). There was no statistically significant difference in the incidence and duration of postoperative delirium between the 2 groups (P>0.05). There were no significant differences in VAS scores, remifentanil consumption, and adverse reactions, including nausea and vomiting, hypoxemia, and respiratory depression between the 2 groups (P>0.05). CONCLUSIONS In elderly patients undergoing radical colon cancer surgery, remimazolam administration did not improve or aggravate the incidence and duration of delirium, compared with propofol.

Terahertz Sensing of L-Valine and L-Phenylalanine Solutions.

To detect and differentiate two essential amino acids (L-Valine and L-Phenylalanine) in the human body, a novel asymmetrically folded dual-aperture metal ring terahertz metasurface sensor was designed. A solvent mixture of water and glycerol with a volume ratio of 2:8 was proposed to reduce the absorption of terahertz waves by reducing the water content. A sample chamber with a controlled liquid thickness of 15 µm was fabricated. And a terahertz time-domain spectroscopy (THz-TDS) system, which is capable of horizontally positioning the samples, was assembled. The results of the sensing test revealed that as the concentration of valine solution varied from 0 to 20 mmol/L, the sensing resonance peak shifted from 1.39 THz to 1.58 THz with a concentration sensitivity of 9.98 GHz/mmol∗L-1. The resonance peak shift phenomenon in phenylalanine solution was less apparent. It is assumed that the coupling enhancement between the absorption peak position of solutes in the solution and the sensing peak position amplified the terahertz localized electric field resonance, which resulted in the increase in frequency shift. Therefore, it could be shown that the sensor has capabilities in performing the marker sensing detection of L-Valine.

A novel graph attention model for predicting frequencies of drug-side effects from multi-view data.

Identifying the frequencies of the drug-side effects is a very important issue in pharmacological studies and drug risk-benefit. However, designing clinical trials to determine the frequencies is usually time consuming and expensive, and most existing methods can only predict the drug-side effect existence or associations, not their frequencies. Inspired by the recent progress of graph neural networks in the recommended system, we develop a novel prediction model for drug-side effect frequencies, using a graph attention network to integrate three different types of features, including the similarity information, known drug-side effect frequency information and word embeddings. In comparison, the few available studies focusing on frequency prediction use only the known drug-side effect frequency scores. One novel approach used in this work first decomposes the feature types in drug-side effect graph to extract different view representation vectors based on three different type features, and then recombines these latent view vectors automatically to obtain unified embeddings for prediction. The proposed method demonstrates high effectiveness in 10-fold cross-validation. The computational results show that the proposed method achieves the best performance in the benchmark dataset, outperforming the state-of-the-art matrix decomposition model. In addition, some ablation experiments and visual analyses are also supplied to illustrate the usefulness of our method for the prediction of the drug-side effect frequencies. The codes of MGPred are available at https://github.com/zhc940702/MGPred and https://zenodo.org/record/4449613.

HyperAttentionDTI: improving drug-protein interaction prediction by sequence-based deep learning with attention mechanism.

MOTIVATION: Identifying drug-target interactions (DTIs) is a crucial step in drug repurposing and drug discovery. Accurately identifying DTIs in silico can significantly shorten development time and reduce costs. Recently, many sequence-based methods are proposed for DTI prediction and improve performance by introducing the attention mechanism. However, these methods only model single non-covalent inter-molecular interactions among drugs and proteins and ignore the complex interaction between atoms and amino acids. RESULTS: In this article, we propose an end-to-end bio-inspired model based on the convolutional neural network (CNN) and attention mechanism, named HyperAttentionDTI, for predicting DTIs. We use deep CNNs to learn the feature matrices of drugs and proteins. To model complex non-covalent inter-molecular interactions among atoms and amino acids, we utilize the attention mechanism on the feature matrices and assign an attention vector to each atom or amino acid. We evaluate HpyerAttentionDTI on three benchmark datasets and the results show that our model achieves significantly improved performance compared with the state-of-the-art baselines. Moreover, a case study on the human Gamma-aminobutyric acid receptors confirm that our model can be used as a powerful tool to predict DTIs. AVAILABILITY AND IMPLEMENTATION: The codes of our model are available at https://github.com/zhaoqichang/HpyerAttentionDTI and https://zenodo.org/record/5039589. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A convolutional neural network and graph convolutional network-based method for predicting the classification of anatomical therapeutic chemicals.

MOTIVATION: The Anatomical Therapeutic Chemical (ATC) system is an official classification system established by the World Health Organization for medicines. Correctly assigning ATC classes to given compounds is an important research problem in drug discovery, which can not only discover the possible active ingredients of the compounds, but also infer theirs therapeutic, pharmacological and chemical properties. RESULTS: In this article, we develop an end-to-end multi-label classifier called CGATCPred to predict 14 main ATC classes for given compounds. In order to extract rich features of each compound, we use the deep Convolutional Neural Network and shortcut connections to represent and learn the seven association scores between the given compound and others. Moreover, we construct the correlation graph of ATC classes and then apply graph convolutional network on the graph for label embedding abstraction. We use all label embedding to guide the learning process of compound representation. As a result, by using the Jackknife test, CGATCPred obtain reliable Aiming of 81.94%, Coverage of 82.88%, Accuracy 80.81%, Absolute True 76.58% and Absolute False 2.75%, yielding significantly improvements compared to exiting multi-label classifiers. AVAILABILITY AND IMPLEMENTATION: The codes of CGATCPred are available at https://github.com/zhc940702/CGATCPred and https://zenodo.org/record/4552917.

Low-complexity full-field ultrafast nonlinear dynamics prediction by a convolutional feature separation modeling method.

The modeling and prediction of the ultrafast nonlinear dynamics in the optical fiber are essential for the studies of laser design, experimental optimization, and other fundamental applications. The traditional propagation modeling method based on the nonlinear Schrödinger equation (NLSE) has long been regarded as extremely time-consuming, especially for designing and optimizing experiments. The recurrent neural network (RNN) has been implemented as an accurate intensity prediction tool with reduced complexity and good generalization capability. However, the complexity of long grid input points and the flexibility of neural network structure should be further optimized for broader applications. Here, we propose a convolutional feature separation modeling method to predict full-field ultrafast nonlinear dynamics with low complexity and strong generalization ability with high accuracy, where the linear effects are firstly modeled by NLSE-derived methods, then a convolutional deep learning method is implemented for nonlinearity modeling. With this method, the temporal relevance of nonlinear effects is substantially shortened, and the parameters and scale of neural networks can be greatly reduced. The running time achieves a 94% reduction versus NLSE and an 87% reduction versus RNN without accuracy deterioration. In addition, the input pulse conditions, including grid point numbers, durations, peak powers, and propagation distance, can be generalized accurately during the predicting process. The results represent a remarkable improvement in ultrafast nonlinear dynamics prediction and this work also provides novel perspectives of the feature separation modeling method for quickly and flexibly studying the nonlinear characteristics in other fields.

Effects of Abdominal Wall Blocks on Postoperative Delirium in Elderly Patients Undergoing Laparoscopic Surgery: A Randomized Controlled Study.

BACKGROUND Postoperative delirium (POD) seriously affects the rapid postoperative recovery of elderly patients. We investigated the effect of abdominal wall blocks on POD in elderly patients undergoing laparoscopic radical resection of colon cancer and underlying mechanisms. MATERIAL AND METHODS A total of 100 patients undergoing laparoscopic radical resection of colon cancer were randomly assigned to group C (control) and group R (regional nerve blocks). In group R, 20 mL of local anesthesia-mixed solution was injected into the bilateral transverse abdominis muscle plane and 10 mL was injected into the bilateral posterior sheath of the rectus abdominis muscle. In group C, the same amount of saline was used for nerve block. The consumption of propofol and remifentanil during surgery was recorded. Levels of serum interleukin (IL)-6 and highly sensitive C-reactive protein (hs-CRP) during surgery were evaluated. The Confusion Assessment Method for the Intensive Care Unit Scale and the Richmond Agitation-Sedation Scale were adopted to evaluate POD. RESULTS The incidence of POD was lower in group R than in group C (P=0.048). The consumption of propofol and remifentanil was significantly reduced in group R, compared with group C (P<0.05). Compared with T0, serum IL-6 and hs-CRP levels in both groups were significantly increased at T1 and T2 (P<0.05). Moreover, serum IL-6 and hs-CRP were lower at T1 and T2 in group R compared with group C (P<0.05). CONCLUSIONS Abdominal wall blocks may alleviate POD in elderly patients undergoing laparoscopic surgery, which may be related to the reduction of anesthetic consumption and inflammatory response.

Perioperative pectoral nerve block type II and postoperative recurrence in breast cancer: a randomized controlled trial.

BACKGROUND: A new technique for analgesia called pectoral nerve block is widely used in surgeries of breast cancer. Pectoral nerve block type II (Pecs II) block has less influence on immunity when compared with general anesthesia method. The purpose of this research is to demonstrate whether Pecs II block has influence on the recurrence of breast cancer after surgical operation. METHODS: 526 breast cancer patients were recruited in this research and randomized into general anesthesia group and general anesthesia with Pecs II block group. Recurrence-free survival (RFS), distant recurrence-free survival (DRFS), and overall survival (OS) were evaluated for the two groups. RESULTS: Based on the statistical data, only the consumption of remifentanil was dramatically reduced by the performance of Pecs II block when compared with general anesthesia method. The performance of Pecs II block had no significant influence on OS, RFS, and DRFS of breast cancer patients after surgery. ASA physical status III, TNM stage 2 + 3, and mastectomy were proved to have association with lower recurrence-free survival. CONCLUSION: In conclusion, the performance of Pecs II block declined the remifentanil consumption during surgery of breast cancer. Meanwhile, the performance of Pecs II block had no significant influence on the OS, RFS, and DRFS of breast cancer patients after surgical resection.

Precision location approach in deep-space optical communications.

A traditional beacon location method is difficult to apply to a deep space optical communications link due to the high laser power required for long distances. The use of natural celestial bodies as beacon images can solve this problem. The correct location of the beacon is critical to establish and maintain an optical communications link. Therefore, in this paper we propose an approach to determine the location of a natural celestial beacon. To identify a beacon in an uncertain region, the phase correlation between the detected and reference images is applied. The influence of an image translation is eliminated through a Fourier transform, and the scaling and rotation are converted into the translation and solved using a log-polar transformation and phase correlation, respectively. The availability of a new approach is verified by the experiment. A field-programmable gate array embedded processing system is designed to realize the proposed algorithm. When the image noise is considered, the success probability of the algorithm can reach more than 96%. We believe this work is beneficial for deep space optical communications system design.

Comprehensive Profiling of Tubby-Like Protein Expression Uncovers Ripening-Related TLP Genes in Tomato (Solanum lycopersicum).

Tubby-like proteins (TLPs), which were firstly identified in obese mice, play important roles in male gametophyte development, biotic stress response, and abiotic stress responses in plants. To date, the role of TLP genes in fruit ripening is largely unknown. Here, through a bioinformatics analysis, we identified 11 TLPs which can be divided into three subgroups in tomato (Solanum lycopersicum), a model plant for studying fruit development and ripening. It was shown that all SlTLPs except SlTLP11 contain both the Tub domain and F-box domain. An expression profiling analysis in different tomato tissues and developmental stages showed that 7 TLP genes are mainly expressed in vegetative tissues, flower, and early fruit developmental stages. Interestingly, other 4 TLP members (SlTLP1, SlTLP2, SlTLP4, and SlTLP5) were found to be highly expressed after breaker stage, suggesting a potential role of these genes in fruit ripening. Moreover, the induced expression of SlTLP1 and SlTLP2 by exogenous ethylene treatment and the down expression of the two genes in ripening mutants, further support their putative role in the ripening process. Overall, our study provides a basis for further investigation of the function of TLPs in plant development and fruit ripening.

A Novel Network-Based Computational Model for Prediction of Potential LncRNA⁻Disease Association.

Accumulating studies have shown that long non-coding RNAs (lncRNAs) are involved in many biological processes and play important roles in a variety of complex human diseases. Developing effective computational models to identify potential relationships between lncRNAs and diseases can not only help us understand disease mechanisms at the lncRNA molecular level, but also promote the diagnosis, treatment, prognosis, and prevention of human diseases. For this paper, a network-based model called NBLDA was proposed to discover potential lncRNA⁻disease associations, in which two novel lncRNA⁻disease weighted networks were constructed. They were first based on known lncRNA⁻disease associations and topological similarity of the lncRNA⁻disease association network, and then an lncRNA⁻lncRNA weighted matrix and a disease⁻disease weighted matrix were obtained based on a resource allocation strategy of unequal allocation and unbiased consistence. Finally, a label propagation algorithm was applied to predict associated lncRNAs for the investigated diseases. Moreover, in order to estimate the prediction performance of NBLDA, the framework of leave-one-out cross validation (LOOCV) was implemented on NBLDA, and simulation results showed that NBLDA can achieve reliable areas under the ROC curve (AUCs) of 0.8846, 0.8273, and 0.8075 in three known lncRNA⁻disease association datasets downloaded from the lncRNADisease database, respectively. Furthermore, in case studies of lung cancer, leukemia, and colorectal cancer, simulation results demonstrated that NBLDA can be a powerful tool for identifying potential lncRNA⁻disease associations as well.

Prediction of microRNA-disease associations based on distance correlation set.

BACKGROUND: Recently, numerous laboratory studies have indicated that many microRNAs (miRNAs) are involved in and associated with human diseases and can serve as potential biomarkers and drug targets. Therefore, developing effective computational models for the prediction of novel associations between diseases and miRNAs could be beneficial for achieving an understanding of disease mechanisms at the miRNA level and the interactions between diseases and miRNAs at the disease level. Thus far, only a few miRNA-disease association pairs are known, and models analyzing miRNA-disease associations based on lncRNA are limited. RESULTS: In this study, a new computational method based on a distance correlation set is developed to predict miRNA-disease associations (DCSMDA) by integrating known lncRNA-disease associations, known miRNA-lncRNA associations, disease semantic similarity, and various lncRNA and disease similarity measures. The novelty of DCSMDA is due to the construction of a miRNA-lncRNA-disease network, which reveals that DCSMDA can be applied to predict potential lncRNA-disease associations without requiring any known miRNA-disease associations. Although the implementation of DCSMDA does not require known disease-miRNA associations, the area under curve is 0.8155 in the leave-one-out cross validation. Furthermore, DCSMDA was implemented in case studies of prostatic neoplasms, lung neoplasms and leukaemia, and of the top 10 predicted associations, 10, 9 and 9 associations, respectively, were separately verified in other independent studies and biological experimental studies. In addition, 10 of the 10 (100%) associations predicted by DCSMDA were supported by recent bioinformatical studies. CONCLUSIONS: According to the simulation results, DCSMDA can be a great addition to the biomedical research field.

Rational Design of Yolk-Shell CuO/Silicalite-1@mSiO2 Composites for a High-Performance Nonenzymatic Glucose Biosensor.

In this study, an interface coassembly strategy is employed to rationally synthesize a yolk-shell CuO/silicalite-1@void@mSiO2 composite consisting of silicalite-1 supported CuO nanoparticles confined in the hollow space of mesoporous silica, and the obtained composite materials were used as a novel nonenzymatic biosensor for highly sensitive and selective detecting glucose with excellent anti-interference ability. The synthesis of CuO/silicalite-1@mSiO2 includes four steps: coating silicalite-1 particles with resorcinol-formaldehyde polymer (RF), immobilization of copper species, interface deposition of a mesoporous silica layer, and final calcination in air to decompose RF and form CuO nanoparticles. The unique hierarchical porous structure with mesopores and micropores is beneficial to selectively enrich glucose for fast oxidation into gluconic acid. Besides, the mesopores in the silica shell can effectively inhibit the large interfering substances or biomacromolecules diffusing into the void as well as the loss of CuO nanoparticles. The hollow chamber inside serves as a nanoreactor for glucose oxidation catalyzed by the active CuO nanoparticles, which are spatially accessible for glucose molecules. The nonenzymatic glucose biosensors based on CuO/silicalite-1@mSiO2 materials show excellent electrocatalytic sensing performance with a wide linear range (5-500 µM), high sensitivity (5.5 µA·mM-1·cm-2), low detection limit (0.17 µM), and high selectivity against interfering species. Furthermore, the unique sensors even display a good capability in the determination of glucose in real blood serum samples.

A Novel Approach Based on a Weighted Interactive Network to Predict Associations of MiRNAs and Diseases.

Accumulating evidence progressively indicated that microRNAs (miRNAs) play a significant role in the pathogenesis of diseases through many experimental studies; therefore, developing powerful computational models to identify potential human miRNA⁻disease associations is vital for an understanding of the disease etiology and pathogenesis. In this paper, a weighted interactive network was firstly constructed by combining known miRNA⁻disease associations, as well as the integrated similarity between diseases and the integrated similarity between miRNAs. Then, a new computational method implementing the newly weighted interactive network was developed for discovering potential miRNA⁻disease associations (WINMDA) by integrating the T most similar neighbors and the shortest path algorithm. Simulation results show that WINMDA can achieve reliable area under the receiver operating characteristics (ROC) curve (AUC) results of 0.9183 ± 0.0007 in 5-fold cross-validation, 0.9200 ± 0.0004 in 10-fold cross-validation, 0.9243 in global leave-one-out cross-validation (LOOCV), and 0.8856 in local LOOCV. Furthermore, case studies of colon neoplasms, gastric neoplasms, and prostate neoplasms based on the Human microRNA Disease Database (HMDD) database were implemented, for which 94% (colon neoplasms), 96% (gastric neoplasms), and 96% (prostate neoplasms) of the top 50 predicting miRNAs were confirmed by recent experimental reports, which also demonstrates that WINMDA can effectively uncover potential miRNA⁻disease associations.

MA-PEP: A novel anticancer peptide prediction framework with multimodal feature fusion based on attention mechanism.

AntiCancer Peptides (ACPs) have emerged as promising therapeutic agents for cancer treatment. The time-consuming and costly nature of wet-lab discriminatory methods has spurred the development of various machine learning and deep learning-based ACP classification methods. Nonetheless, current methods encountered challenges in efficiently integrating features from various peptide modalities, thereby limiting a more comprehensive understanding of ACPs and further restricting the improvement of prediction model performance. In this study, we introduce a novel ACP prediction method, MA-PEP, which leverages multiple attention mechanisms for feature enhancement and fusion to improve ACP prediction. By integrating the enhanced molecular-level chemical features and sequence information of peptides, MA-PEP demonstrates superior prediction performance across several benchmark datasets, highlighting its efficacy in ACP prediction. Moreover, the visual analysis and case studies further demonstrate MA-PEP's reliable feature extraction capability and its promise in the realm of ACP exploration. The code and datasets for MA-PEP are available at https://github.com/liangxiaodata/MA-PEP.