Negative Dataset Research Articles

Ensemble learning model for Protein-Protein interaction prediction with multiple Machine learning techniques

Protein-Protein Interactions (PPIs) encompass the physical interactions or chemical combinations between proteins, which cells employ to carry out diverse biological functions. PPIs Prediction is of great significance for understanding and studying molecular mechanisms and disease mechanisms in organisms. Traditional machine learning algorithms for predicting PPIs face challenges related to imbalanced sample data, loss of long sequence features, and variations in datasets across different species. This paper takes advantage of the attention mechanism in processing sequence tasks and proposes the fusion of Bi-directional Long Short- Term Memory, an integrated learning model of convolutional neural network and multi-head attention mechanism, referred to as CNN BiLSTM Multi-head Att. The model first performs one-hot encoding on the protein sequence and obtains a low-dimensional word vector representation, and then calculates the encoding matrix through the embedding matrix; it uses the convolutional neural network (CNN) and the bidirectional long short-term memory network (Bi-LSTM) to extract the amino acids in the protein sequence. The feature matrix is obtained by collecting information on the time, site, and physical and chemical characteristics; the multi-head attention mechanism is used in weighted calculation and merger of the subspace features of each sequence, and classification is performed after global average pooling and the PPI prediction is output. Cross-validation experiments on different proportions of positive and negative protein data sets and proteins of different species show that CNN BiLSTM Multi-head Att can better predict imbalanced data sets and protein interactions of different species. Compared to the state-of-the-art model, our model improves accuracy by 0.025, precision by 0.046, F1-score by 0.052, and recall by 0.014. Exploring the scalability and adaptability of the proposed model for large-scale biological datasets, along with investigating its potential in real-time applications, could lead to significant advancements in the field of protein–protein interaction prediction.

GCLmf: A Novel Molecular Graph Contrastive Learning Framework Based on Hard Negatives and Application in Toxicity Prediction.

In silico methods for prediction of chemical toxicity can decrease the cost and increase the efficiency in the early stage of drug discovery. However, due to low accessibility of sufficient and reliable toxicity data, constructing robust and accurate prediction models is challenging. Contrastive learning, a type of self-supervised learning, leverages large unlabeled data to obtain more expressive molecular representations, which can boost the prediction performance on downstream tasks. While molecular graph contrastive learning has gathered growing attentions, current models neglect the quality of negative data set. Here, we proposed a self-supervised pretraining deep learning framework named GCLmf. We first utilized molecular fragments that meet specific conditions as hard negative samples to boost the quality of the negative set and thus increase the difficulty of the proxy tasks during pre-training to learn informative representations. GCLmf has shown excellent predictive power on various molecular property benchmarks and demonstrates high performance in 33 toxicity tasks in comparison with multiple baselines. In addition, we further investigated the necessity of introducing hard negatives in model building and the impact of the proportion of hard negatives on the model.

Hybrid Open-Set Segmentation With Synthetic Negative Data.

Open-set segmentation can be conceived by complementing closed-set classification with anomaly detection. Many of the existing dense anomaly detectors operate through generative modelling of regular data or by discriminating with respect to negative data. These two approaches optimize different objectives and therefore exhibit different failure modes. Consequently, we propose a novel anomaly score that fuses generative and discriminative cues. Our score can be implemented by upgrading any closed-set segmentation model with dense estimates of dataset posterior and unnormalized data likelihood. The resulting dense hybrid open-set models require negative training images that can be sampled from an auxiliary negative dataset, from a jointly trained generative model, or from a mixture of both sources. We evaluate our contributions on benchmarks for dense anomaly detection and open-set segmentation. The experiments reveal strong open-set performance in spite of negligible computational overhead.

A general prediction model for compound-protein interactions based on deep learning.

The identification of compound-protein interactions (CPIs) is crucial for drug discovery and understanding mechanisms of action. Accurate CPI prediction can elucidate drug-target-disease interactions, aiding in the discovery of candidate compounds and effective synergistic drugs, particularly from traditional Chinese medicine (TCM). Existing in silico methods face challenges in prediction accuracy and generalization due to compound and target diversity and the lack of largescale interaction datasets and negative datasets for model learning. To address these issues, we developed a computational model for CPI prediction by integrating the constructed large-scale bioactivity benchmark dataset with a deep learning (DL) algorithm. To verify the accuracy of our CPI model, we applied it to predict the targets of compounds in TCM. An herb pair of Astragalus membranaceus and Hedyotis diffusaas was used as a model, and the active compounds in this herb pair were collected from various public databases and the literature. The complete targets of these active compounds were predicted by the CPI model, resulting in an expanded target dataset. This dataset was next used for the prediction of synergistic antitumor compound combinations. The predicted multi-compound combinations were subsequently examined through in vitro cellular experiments. Our CPI model demonstrated superior performance over other machine learning models, achieving an area under the Receiver Operating Characteristic curve (AUROC) of 0.98, an area under the precision-recall curve (AUPR) of 0.98, and an accuracy (ACC) of 93.31% on the test set. The model's generalization capability and applicability were further confirmed using external databases. Utilizing this model, we predicted the targets of compounds in the herb pair of Astragalus membranaceus and Hedyotis diffusaas, yielding an expanded target dataset. Then, we integrated this expanded target dataset to predict effective drug combinations using our drug synergy prediction model DeepMDS. Experimental assay on breast cancer cell line MDA-MB-231 proved the efficacy of the best predicted multi-compound combinations: Combination I (Epicatechin, Ursolic acid, Quercetin, Aesculetin and Astragaloside IV) exhibited a half-maximal inhibitory concentration (IC50) value of 19.41μM, and a combination index (CI) value of 0.682; and Combination II (Epicatechin, Ursolic acid, Quercetin, Vanillic acid and Astragaloside IV) displayed a IC50 value of 23.83μM and a CI value of 0.805. These results validated the ability of our model to make accurate predictions for novel CPI data outside the training dataset and evaluated the reliability of the predictions, showing good applicability potential in drug discovery and in the elucidation of the bioactive compounds in TCM. Our CPI prediction model can serve as a useful tool for accurately identifying potential CPI for a wide range of proteins, and is expected to facilitate drug research, repurposing and support the understanding of TCM.

Leveraging Supervised Machine Learning Algorithms for System Suitability Testing of Mass Spectrometry Imaging Platforms.

Quality control and system suitability testing are vital protocols implemented to ensure the repeatability and reproducibility of data in mass spectrometry investigations. However, mass spectrometry imaging (MSI) analyses present added complexity since both chemical and spatial information are measured. Herein, we employ various machine learning algorithms and a novel quality control mixture to classify the working conditions of an MSI platform. Each algorithm was evaluated in terms of its performance on unseen data, validated with negative control data sets to rule out confounding variables or chance agreement, and utilized to determine the necessary sample size to achieve a high level of accurate classifications. In this work, a robust machine learning workflow was established where models could accurately classify the instrument condition as clean or compromised based on data metrics extracted from the analyzed quality control sample. This work highlights the power of machine learning to recognize complex patterns in MSI data and use those relationships to perform a system suitability test for MSI platforms.

Lessons learned from the IMMREP23 TCR-epitope prediction challenge

Here, we present the findings from IMMREP23, the second benchmark competition focused on predicting the specificity of TCR-pMHC interactions.The interaction of T cell receptors (TCR) towards their pMHC target is a cornerstone of the cellular immune system. Over the last decade, substantial progress has been made within the field of TCR specificity prediction, providing proof of concept for predicting TCR-pMHC interactions in a narrow space of “seen” pMHC targets where substantial training data is available. However, a significant challenge persists in extending the predictive capability to novel “unseen” pMHC targets. Furthermore, the performance of proposed methods is often challenged when evaluated outside the initial publication and data sets.To address these issues, IMMREP23 challenge invited participants to predict, for a given test set of TCR-pMHC pairs, the likelihood that a pair would bind. A total of 53 teams participated, providing a total of 398 submissions.The benchmark confirms that current methods achieve reasonable performance in the "seen" pMHC setting. However, most participating methods had close to random performance on the subset of “unseen” peptides, underlining that this prediction challenge remains essentially unsolved.Finally, another key lesson from the benchmark is the critical issue of data leakage. Specifically, the data set construction procedure employed in IMMREP23 led to biases in the negative test data set. These biases were identified by several participating teams, and complicated the interpretation of the benchmark results. Based on these results, we put forward suggestions on how future competitions could avoid such data leakages and biases.

Benchmarking the negatives: Effect of negative data generation on the classification of miRNA-mRNA interactions.

MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression post-transcriptionally. In animals, this regulation is achieved via base-pairing with partially complementary sequences on mainly 3' UTR region of messenger RNAs (mRNAs). Computational approaches that predict miRNA target interactions (MTIs) facilitate the process of narrowing down potential targets for experimental validation. The availability of new datasets of high-throughput, direct MTIs has led to the development of machine learning (ML) based methods for MTI prediction. To train an ML algorithm, it is beneficial to provide entries from all class labels (i.e., positive and negative). Currently, no high-throughput assays exist for capturing negative examples. Therefore, current ML approaches must rely on either artificially generated or inferred negative examples deduced from experimentally identified positive miRNA-target datasets. Moreover, the lack of uniform standards for generating such data leads to biased results and hampers comparisons between studies. In this comprehensive study, we collected methods for generating negative data for animal miRNA-target interactions and investigated their impact on the classification of true human MTIs. Our study relies on training ML models on a fixed positive dataset in combination with different negative datasets and evaluating their intra- and cross-dataset performance. As a result, we were able to examine each method independently and evaluate ML models' sensitivity to the methodologies utilized in negative data generation. To achieve a deep understanding of the performance results, we analyzed unique features that distinguish between datasets. In addition, we examined whether one-class classification models that utilize solely positive interactions for training are suitable for the task of MTI classification. We demonstrate the importance of negative data in MTI classification, analyze specific methodological characteristics that differentiate negative datasets, and highlight the challenge of ML models generalizing interaction rules from training to testing sets derived from different approaches. This study provides valuable insights into the computational prediction of MTIs that can be further used to establish standards in the field.

AlphaFold2-based prediction of the co-condensation propensity of proteins

The process of protein phase separation into liquid condensates has been implicated in the formation of membraneless organelles (MLOs), which selectively concentrate biomolecules to perform essential cellular functions. Although the importance of this process in health and disease is increasingly recognized, the experimental identification of proteins forming MLOs remains a complex challenge. In this study, we addressed this problem by harnessing the power of AlphaFold2 to perform computational predictions of the conformational properties of proteins from their amino acid sequences. We thus developed the CoDropleT (co-condensation into droplet transformer) method of predicting the propensity of co-condensation of protein pairs. The method was trained by combining experimental datasets of co-condensing proteins from the CD-CODE database with curated negative datasets of non-co-condensing proteins. To illustrate the performance of the method, we applied it to estimate the propensity of proteins to co-condense into MLOs. Our results suggest that CoDropleT could facilitate functional and therapeutic studies on protein condensation by predicting the composition of protein condensates.

Antiprotozoal peptide prediction using machine learning with effective feature selection techniques

BackgroundProtozoal pathogens pose a considerable threat, leading to notable mortality rates and the ongoing challenge of developing resistance to drugs. This situation underscores the urgent need for alternative therapeutic approaches. Antimicrobial peptides stand out as promising candidates for drug development. However, there is a lack of published research focusing on predicting antimicrobial peptides specifically targeting protozoal pathogens. In this study, we introduce a successful machine learning-based framework designed to predict potential antiprotozoal peptides effective against protozoal pathogens. ObjectiveThe primary objective of this study is to classify and predict antiprotozoal peptides using diverse negative datasets. MethodsA comprehensive literature review was conducted to gather experimentally validated antiprotozoal peptides, forming the positive dataset for our study. To construct a robust machine learning classifier, multiple negative datasets were incorporated, including (i) non-antimicrobial, (ii) antiviral, (iii) antibacterial, (iv) antifungal, and (v) antimicrobial peptides excluding those targeting protozoal pathogens. Various compositional features of the peptides were extracted using the pfeature algorithm. Two feature selection methods, SVC-L1 and mRMR, were employed to identify highly relevant features crucial for distinguishing between the positive and negative datasets. Additionally, five popular classifiers i.e. Decision Tree, Random Forest, Support Vector Machine, Logistic Regression, and XGBoost were used to build efficient decision models. ResultsXGBoost was the most effective in classifying antiprotozoal peptides from each negative dataset based on the features selected by the mRMR feature selection method. The proposed machine learning framework efficiently differentiate the antiprotozoal peptides from (i) non-antimicrobial (ii) antiviral (iii) antibacterial (iv) antifungal and (v) antimicrobial with accuracy of 97.27 %, 93.64 %, 86.36 %, 90.91 %, and 89.09 % respectively on the validation dataset. ConclusionThe models are incorporated in a user-friendly web server (www.soodlab.com/appred) to predict the antiprotozoal activity of given peptides.

Algorithm of face anti-spoofing based on pseudo-negative features generation.

Despite advancements in face anti-spoofing technology, attackers continue to pose challenges with their evolving deceptive methods. This is primarily due to the increased complexity of their attacks, coupled with a diversity in presentation modes, acquisition devices, and prosthetic materials. Furthermore, the scarcity of negative sample data exacerbates the situation by causing domain shift issues and impeding robust generalization. Hence, there is a pressing need for more effective cross-domain approaches to bolster the model's capability to generalize across different scenarios. This method improves the effectiveness of face anti-spoofing systems by analyzing pseudo-negative sample features, expanding the training dataset, and boosting cross-domain generalization. By generating pseudo-negative features with a new algorithm and aligning these features with the use of KL divergence loss, we enrich the negative sample dataset, aiding the training of a more robust feature classifier and broadening the range of attacks that the system can defend against. Through experiments on four public datasets (MSU-MFSD, OULU-NPU, Replay-Attack, and CASIA-FASD), we assess the model's performance within and across datasets by controlling variables. Our method delivers positive results in multiple experiments, including those conducted on smaller datasets. Through controlled experiments, we demonstrate the effectiveness of our method. Furthermore, our approach consistently yields favorable results in both intra-dataset and cross-dataset evaluations, thereby highlighting its excellent generalization capabilities. The superior performance on small datasets further underscores our method's remarkable ability to handle unseen data beyond the training set.

VIPERA: Viral Intra-Patient Evolution Reporting and Analysis.

Viral mutations within patients nurture the adaptive potential of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during chronic infections, which are a potential source of variants of concern. However, there is no integrated framework for the evolutionary analysis of intra-patient SARS-CoV-2 serial samples. Herein, we describe Viral Intra-Patient Evolution Reporting and Analysis (VIPERA), a new software that integrates the evaluation of the intra-patient ancestry of SARS-CoV-2 sequences with the analysis of evolutionary trajectories of serial sequences from the same viral infection. We have validated it using positive and negative control datasets and have successfully applied it to a new case, which revealed population dynamics and evidence of adaptive evolution. VIPERA is available under a free software license at https://github.com/PathoGenOmics-Lab/VIPERA.

Dense Out-of-Distribution Detection by Robust Learning on Synthetic Negative Data.

Standard machine learning is unable to accommodate inputs which do not belong to the training distribution. The resulting models often give rise to confident incorrect predictions which may lead to devastating consequences. This problem is especially demanding in the context of dense prediction since input images may be only partially anomalous. Previous work has addressed dense out-of-distribution detection by discriminative training with respect to off-the-shelf negative datasets. However, real negative data may lead to over-optimistic evaluation due to possible overlap with test anomalies. To this end, we extend this approach by generating synthetic negative patches along the border of the inlier manifold. We leverage a jointly trained normalizing flow due to a coverage-oriented learning objective and the capability to generate samples at different resolutions. We detect anomalies according to a principled information-theoretic criterion which can be consistently applied through training and inference. The resulting models set the new state of the art on benchmarks for out-of-distribution detection in road-driving scenes and remote sensing imagery despite minimal computational overhead.

BestCRM: An Exhaustive Search for Optimal Cis-Regulatory Modules in Promoters Accelerated by the Multidimensional Hash Function.

The concept of cis-regulatory modules located in gene promoters represents today's vision of the organization of gene transcriptional regulation. Such modules are a combination of two or more single, short DNA motifs. The bioinformatic identification of such modules belongs to so-called NP-hard problems with extreme computational complexity, and therefore, simplifications, assumptions, and heuristics are usually deployed to tackle the problem. In practice, this requires, first, many parameters to be set before the search, and second, it leads to the identification of locally optimal results. Here, a novel method is presented, aimed at identifying the cis-regulatory elements in gene promoters based on an exhaustive search of all the feasible modules' configurations. All required parameters are automatically estimated using positive and negative datasets. To be computationally efficient, the search is accelerated using a multidimensional hash function, allowing the search to complete in a few hours on a regular laptop (for example, a CPU Intel i7, 3.2 GH, 32 Gb RAM). Tests on an established benchmark and real data show better performance of BestCRM compared to the available methods according to several metrics like specificity, sensitivity, AUC, etc. A great practical advantage of the method is its minimum number of input parameters-apart from positive and negative promoters, only a desired level of module presence in promoters is required.

Analysis Sentiment to Religious Based Issues Netizen Comments on President Jokowi 's Instagram using the Naive Bayes Classifier Method


 
 
 
 The presence of Instagram is one of the most popular social media platforms. Make people make a habit of posting photo and video content. In this way, Instagram can become a source of public opinion and sentiment data, which data can be used for social studies. Social studies is still a crucial issue for countries today, including Indonesia, is a matter of religion. Apart from problems regarding religion, public sentiment can be researched from Instagram. The sentiment dataset related to religion is taken from public comments from President Joko Widodo's content regarding Merry Christmas Greetings and Visits to Churches. The analysis system will use sentiment analysis using a method from machine learning, namely the naive Bayes classifier to determine positive & negative sentiments. The comment dataset is 2,600 in which the positive and negative sentiment class datasets are determined manually by experts in the field of linguistics or communication totaling 1,309 to be used as training data. the results of testing the Naïve Bayes classifier training data with a total of 1309 obtained an accuracy prediction of 97.63% and test data where positive & negative sentiment classes had not been determined before hand amounted to 1291 in sentiment analysis obtained positive sentiment 70.56% & negative sentiment 29.44%.
 
 
 

AntiCVP-Deep: Identify anti-coronavirus peptides between different negative datasets based on self-attention and deep learning

The disease caused by infection with the SARS-CoV-2 virus is threatening people's health and lives. Antimicrobial peptides (AMPs) with anti-coronavirus (anti-CoV) functions may be potentially effective therapeutics for the treatment of infected coronaviruses. Due to the complexity of experimental methods, it is particularly important to identify anti-CoV peptides by computational techniques. In this paper, we propose a new predictive model, called AntiCVP-Deep. First, six feature extraction methods are used to obtain the original feature vector of antimicrobial peptides, and K-means SMOTE is used to handle imbalance data. Then, the processed balanced sample data is passed through the input gate, forget gate and output gate of a bidirectional long short-term memory network (BILSTM) to select optimal features. Next, the important antimicrobial peptides information is given a higher weight through the self-attention mechanism, which enhances the model's ability to learn features. Finally, the data from self-attention layer are input into fully connected neural network (FCN) to predict anti-CoV peptides. The AUC values of the four datasets all reach above 98%. The geometric mean (GMean) of antivirus, non-AVP, non-AMP and all-Neg recognition on the independent test sets reach 90.05%, 92.63%, 99.46% and 91.24%, respectively. This experimental result shows that the AntiCVP-Deep method is helpful for identifying anti-CoV peptides and has better predictive performance compared to other existing predictive models.

Detecting Rice Weevils in Stored Grains using MFCC and CNN

Sitophilus oryzae or commonly known as Rice Weevil is a pest that infests stored rice grains. The feeding and movement of this pest within the grain produce sounds that are audible to a high-performance microphone sensor. This study presents a method for early detection of Rice Weevils based on the frequency of the sound they produce. Sound features were extracted using MFCC and utilized as input features to train a Convolutional Neural Network (CNN). The classifier was evaluated using 2800 acoustic samples containing positive and negative datasets. The experimental results show that the method can be an alternative approach for the early detection of the presence of rice weevils in stored rice grains.

O-GlcNAcPRED-DL: Prediction of Protein O-GlcNAcylation Sites Based on an Ensemble Model of Deep Learning.

O-linked β-N-acetylglucosamine (O-GlcNAc) is a post-translational modification (i.e., O-GlcNAcylation) on serine/threonine residues of proteins, regulating a plethora of physiological and pathological events. As a dynamic process, O-GlcNAc functions in a site-specific manner. However, the experimental identification of the O-GlcNAc sites remains challenging in many scenarios. Herein, by leveraging the recent progress in cataloguing experimentally identified O-GlcNAc sites and advanced deep learning approaches, we establish an ensemble model, O-GlcNAcPRED-DL, a deep learning-based tool, for the prediction of O-GlcNAc sites. In brief, to make a benchmark O-GlcNAc data set, we extracted the information on O-GlcNAc from the recently constructed database O-GlcNAcAtlas, which contains thousands of experimentally identified and curated O-GlcNAc sites on proteins from multiple species. To overcome the imbalance between positive and negative data sets, we selected five groups of negative data sets in humans and mice to construct an ensemble predictor based on connection of a convolutional neural network and bidirectional long short-term memory. By taking into account three types of sequence information, we constructed four network frameworks, with the systematically optimized parameters used for the models. The thorough comparison analysis on two independent data sets of humans and mice and six independent data sets from other species demonstrated remarkably increased sensitivity and accuracy of the O-GlcNAcPRED-DL models, outperforming other existing tools. Moreover, a user-friendly Web server for O-GlcNAcPRED-DL has been constructed, which is freely available at http://oglcnac.org/pred_dl.

Prospection of Peptide Inhibitors of Thrombin from Diverse Origins Using a Machine Learning Pipeline.

Thrombin is a key enzyme involved in the development and progression of many cardiovascular diseases. Direct thrombin inhibitors (DTIs), with their minimum off-target effects and immediacy of action, have greatly improved the treatment of these diseases. However, the risk of bleeding, pharmacokinetic issues, and thrombotic complications remain major concerns. In an effort to increase the effectiveness of the DTI discovery pipeline, we developed a two-stage machine learning pipeline to identify and rank peptide sequences based on their effective thrombin inhibitory potential. The positive dataset for our model consisted of thrombin inhibitor peptides and their binding affinities (KI) curated from published literature, and the negative dataset consisted of peptides with no known thrombin inhibitory or related activity. The first stage of the model identified thrombin inhibitory sequences with Matthew's Correlation Coefficient (MCC) of 83.6%. The second stage of the model, which covers an eight-order of magnitude range in KI values, predicted the binding affinity of new sequences with a log room mean square error (RMSE) of 1.114. These models also revealed physicochemical and structural characteristics that are hidden but unique to thrombin inhibitor peptides. Using the model, we classified more than 10 million peptides from diverse sources and identified unique short peptide sequences (<15 aa) of interest, based on their predicted KI. Based on the binding energies of the interaction of the peptide with thrombin, we identified a promising set of putative DTI candidates. The prediction pipeline is available on a web server.

Recognition of mineralization-related anomaly patterns through an autoencoder neural network for mineral exploration targeting

In mineral potential mapping, supervised machine learning algorithms have shown great promise in delineating and prioritizing potential areas. However, since mineralization being a relatively rare geological event, most supervised machine learning-based models face substantial challenges in properly identifying prospective areas. Data sets with strongly imbalanced distributions of the target variable (deposits) and insufficient training data sets impose obstacles to these kinds of models which can significantly impact adversely on the performance of the models. Moreover, in some cases, negative training data sets as the non-deposit locations aren't really true negative data, which cause higher uncertainty in a mineral potential map. In this study, for handling these challenges the deep autoencoder neural network is adopted. The autoencoder can be trained to reconstruct geospatial data set in totally unsupervised manner and identify prospective areas based on the reconstruction error, where higher error corresponds with areas of higher mineral potential. In order to confirm the efficiency of the autoencoder algorithm in mineral potential modeling, the model was compared with a popular data-driven approach that assigned a weight to the evidence layer by using a concentration-area (C-A) fractal model and a prediction-area (P-A) plot, and combined them using a multi-class index overlay method. Receiver operating characteristic (ROC) curve, success-rate curve, and P-A plot were adopted to evaluate the predictive ability of Fe prospectivity models pertaining to the Esfordi district of Iran. Also, we use an area under the ROC curve (AUC) and partial AUC (pAUC) to quantitatively evaluate the overall and sensitivity performance of models, respectively.

LncRNA-Top: Controlled deep learning approaches for lncRNA gene regulatory relationship annotations across different platforms

By soaking microRNAs (miRNAs), long non-coding RNAs (lncRNAs) have the potential to regulate gene expression. Few methods have been created based on this mechanism to anticipate the lncRNA-gene relationship prediction. Hence, we present lncRNA-Top to forecast potential lncRNA-gene regulation relationships. Specifically, we constructed controlled deep-learning methods using 12417 lncRNAs and 16127 genes. We have provided retrospective and innovative views among negative sampling, random seeds, cross-validation, metrics, and independent datasets. The AUC, AUPR, and our defined precision@k were leveraged to evaluate performance. In-depth case studies demonstrate that 47 out of 100 projected top unknown pairings were recorded in publications, supporting the predictive power. Our additional software can annotate the scores with target candidates. The lncRNA-Top will be a helpful tool to uncover prospective lncRNA targets and better comprehend the regulatory processes of lncRNAs.