DeepTraSynergy: drug combinations using multimodal deep learning with transformers.

MOTIVATION: Screening bioactive compounds in cancer cell lines receive more attention. Multidisciplinary drugs or drug combinations have a more effective role in treatments and selectively inhibit the growth of cancer cells. RESULTS: Hence, we propose a new deep learning-based approach for drug combination synergy prediction called DeepTraSynergy. Our proposed approach utilizes multimodal input including drug-target interaction, protein-protein interaction, and cell-target interaction to predict drug combination synergy. To learn the feature representation of drugs, we have utilized transformers. It is worth noting that our approach is a multitask approach that predicts three outputs including the drug-target interaction, its toxic effect, and drug combination synergy. In our approach, drug combination synergy is the main task and the two other ones are the auxiliary tasks that help the approach to learn a better model. In the proposed approach three loss functions are defined: synergy loss, toxic loss, and drug-protein interaction loss. The last two loss functions are designed as auxiliary losses to help learn a better solution. DeepTraSynergy outperforms the classic and state-of-the-art models in predicting synergistic drug combinations on the two latest drug combination datasets. The DeepTraSynergy algorithm achieves accuracy values of 0.7715 and 0.8052 (an improvement over other approaches) on the DrugCombDB and Oncology-Screen datasets, respectively. Also, we evaluate the contribution of each component of DeepTraSynergy to show its effectiveness in the proposed method. The introduction of the relation between proteins (PPI networks) and drug-protein interaction significantly improves the prediction of synergistic drug combinations. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at https://github.com/fatemeh-rafiei/DeepTraSynergy.

CFSSynergy: Combining Feature-Based and Similarity-Based Methods for Drug Synergy Prediction.

Drug synergy prediction plays a vital role in cancer treatment. Because experimental approaches are labor-intensive and expensive, computational-based approaches get more attention. There are two types of computational methods for drug synergy prediction: feature-based and similarity-based. In feature-based methods, the main focus is to extract more discriminative features from drug pairs and cell lines to pass to the task predictor. In similarity-based methods, the similarities among all drugs and cell lines are utilized as features and fed into the task predictor. In this work, a novel approach, called CFSSynergy, that combines these two viewpoints is proposed. First, a discriminative representation is extracted for paired drugs and cell lines as input. We have utilized transformer-based architecture for drugs. For cell lines, we have created a similarity matrix between proteins using the Node2Vec algorithm. Then, the new cell line representation is computed by multiplying the protein-protein similarity matrix and the initial cell line representation. Next, we compute the similarity between unique drugs and unique cells using the learned representation for paired drugs and cell lines. Then, we compute a new representation for paired drugs and cell lines based on the similarity-based features and the learned features. Finally, these features are fed to XGBoost as a task predictor. Two well-known data sets were used to evaluate the performance of our proposed method: DrugCombDB and OncologyScreen. The CFSSynergy approach consistently outperformed existing methods in comparative evaluations. This substantiates the efficacy of our approach in capturing complex synergistic interactions between drugs and cell lines, setting it apart from conventional similarity-based or feature-based methods.

Restoring the epigenetic landscape of lung microbiome: potential therapeutic approach for chronic respiratory diseases.

BACKGROUND: Chronic respiratory diseases, such as chronic obstructive pulmonary disease (COPD) and bronchiectasis, present significant threats to global health. Recent studies have revealed the crucial role of the lung microbiome in the development of these diseases. Pathogens have evolved complex strategies to evade the immune response, with the manipulation of host cellular epigenetic mechanisms playing a pivotal role. There is existing evidence regarding the effects of Pseudomonas on epigenetic modifications and their association with pulmonary diseases. Therefore, this study aims to directly assess the connection between Pseudomonas abundance and chronic respiratory diseases. We hope that our findings will shed light on the molecular mechanisms behind lung pathogen infections. METHODS: We analyzed data from 366 participants, including individuals with COPD, acute exacerbations of COPD (AECOPD), bronchiectasis, and healthy individuals. Previous studies have given limited attention to the impact of Pseudomonas on these groups and their comparison with healthy individuals. Two independent datasets from different ethnic backgrounds were used for external validation. Each dataset separately analyzed bacteria at the genus level. RESULTS: The study reveals that Pseudomonas, a bacterium, was consistently found in high concentrations in all chronic lung disease datasets but it was present in very low abundance in the healthy datasets. This suggests that Pseudomonas may influence cellular mechanisms through epigenetics, contributing to the development and progression of chronic respiratory diseases. CONCLUSIONS: This study emphasizes the importance of understanding the relationship between the lung microbiome, epigenetics, and the onset of chronic pulmonary disease. Enhanced recognition of molecular mechanisms and the impact of the microbiome on cellular functions, along with a better understanding of these concepts, can lead to improved diagnosis and treatment.

DrugRep-HeSiaGraph: when heterogenous siamese neural network meets knowledge graphs for drug repurposing.

BACKGROUND: Drug repurposing is an approach that holds promise for identifying new therapeutic uses for existing drugs. Recently, knowledge graphs have emerged as significant tools for addressing the challenges of drug repurposing. However, there are still major issues with constructing and embedding knowledge graphs. RESULTS: This study proposes a two-step method called DrugRep-HeSiaGraph to address these challenges. The method integrates the drug-disease knowledge graph with the application of a heterogeneous siamese neural network. In the first step, a drug-disease knowledge graph named DDKG-V1 is constructed by defining new relationship types, and then numerical vector representations for the nodes are created using the distributional learning method. In the second step, a heterogeneous siamese neural network called HeSiaNet is applied to enrich the embedding of drugs and diseases by bringing them closer in a new unified latent space. Then, it predicts potential drug candidates for diseases. DrugRep-HeSiaGraph achieves impressive performance metrics, including an AUC-ROC of 91.16%, an AUC-PR of 90.32%, an accuracy of 84.63%, a BS of 0.119, and an MCC of 69.31%. CONCLUSION: We demonstrate the effectiveness of the proposed method in identifying potential drugs for COVID-19 as a case study. In addition, this study shows the role of dipeptidyl peptidase 4 (DPP-4) as a potential receptor for SARS-CoV-2 and the effectiveness of DPP-4 inhibitors in facing COVID-19. This highlights the practical application of the model in addressing real-world challenges in the field of drug repurposing. The code and data for DrugRep-HeSiaGraph are publicly available at https://github.com/CBRC-lab/DrugRep-HeSiaGraph .

Structure-based drug repurposing against COVID-19 and emerging infectious diseases: methods, resources and discoveries.

To attain promising pharmacotherapies, researchers have applied drug repurposing (DR) techniques to discover the candidate medicines to combat the coronavirus disease 2019 (COVID-19) outbreak. Although many DR approaches have been introduced for treating different diseases, only structure-based DR (SBDR) methods can be employed as the first therapeutic option against the COVID-19 pandemic because they rely on the rudimentary information about the diseases such as the sequence of the severe acute respiratory syndrome coronavirus 2 genome. Hence, to try out new treatments for the disease, the first attempts have been made based on the SBDR methods which seem to be among the proper choices for discovering the potential medications against the emerging and re-emerging infectious diseases. Given the importance of SBDR approaches, in the present review, well-known SBDR methods are summarized, and their merits are investigated. Then, the databases and software applications, utilized for repurposing the drugs against COVID-19, are introduced. Besides, the identified drugs are categorized based on their targets. Finally, a comparison is made between the SBDR approaches and other DR methods, and some possible future directions are proposed.

DrugRep-KG: Toward Learning a Unified Latent Space for Drug Repurposing Using Knowledge Graphs.

Drug repurposing or repositioning (DR) refers to finding new therapeutic applications for existing drugs. Current computational DR methods face data representation and negative data sampling challenges. Although retrospective studies attempt to operate various representations, it is a crucial step for an accurate prediction to aggregate these features and bring the associations between drugs and diseases into a unified latent space. In addition, the number of unknown associations between drugs and diseases, which is considered negative data, is much higher than the number of known associations, or positive data, leading to an imbalanced dataset. In this regard, we propose the DrugRep-KG method, which applies a knowledge graph embedding approach for representing drugs and diseases, to address these challenges. Despite the typical DR methods that consider all unknown drug-disease associations as negative data, we select a subset of unknown associations, provided the disease occurs because of an adverse reaction to a drug. DrugRep-KG has been evaluated based on different settings and achieves an AUC-ROC (area under the receiver operating characteristic curve) of 90.83% and an AUC-PR (area under the precision-recall curve) of 90.10%, which are higher than in previous works. Besides, we checked the performance of our framework in finding potential drugs for coronavirus infection and skin-related diseases: contact dermatitis and atopic eczema. DrugRep-KG predicted beclomethasone for contact dermatitis, and fluorometholone, clocortolone, fluocinonide, and beclomethasone for atopic eczema, all of which have previously been proven to be effective in other studies. Fluorometholone for contact dermatitis is a novel suggestion by DrugRep-KG that should be validated experimentally. DrugRep-KG also predicted the associations between COVID-19 and potential treatments suggested by DrugBank, in addition to new drug candidates provided with experimental evidence. The data and code underlying this article are available at https://github.com/CBRC-lab/DrugRep-KG.

Pan-cancer analysis of biological events on cell cycle instability in gastrointestinal cancers with integrative scoring method.

Omics data integration plays an essential role in manifesting hidden cancer insights. To detect the main combinatorial/parallel impact of cancer events, integrative approaches in pan-cancer studies must be used. Here, we assessed gastrointestinal (GI) cancers from several perspectives of genomics, transcriptomics, epigenomics, and also combinatorial impacts using a novel integrative approach to score genes. Next, scores were diffused on a signaling network and extracted subnetworks. We also implemented our new scoring method to compare upper-/lower-GI cancers, investigate the regulatory mechanisms of lncRNAs, and detect amplifications/deletions between GI and non-GI cancers. The integrative subnetwork indicated the interplay among essential protein families in the cell cycle. The copy-number-variation-related subnetwork revealed minor cell cycle and immune effects, whereas the methylation-related subnetwork revealed significant immune effects. The top-score lncRNAs indicated a distinct regulatory pattern for lower-/upper-, and accessory-GI categories. In summary, cell cycle dysfunction might be largely the consequence of combinatorial abnormalities.

Revealing transcriptional and post-transcriptional regulatory mechanisms of γ-glutamyl transferase and keratin isoforms as novel cooperative biomarkers in low-grade glioma and glioblastoma multiforme.

Gamma-glutamyltransferase (GGT) and keratins (KRT) are key factors in regulating tumor progression rely on emerging evidence. However, the prognostic values of GGT and KRT isoforms and their regulation patterns at transcriptional and post-transcriptional levels have been rarely studied. In this study, we aimed to identify cooperative prognostic biomarker signature conducted by GGT and KRT genes for overall survival prediction and discrimination in patients with low-grade glioma (LGG) and glioblastoma multiforme (GBM). To this end, we employed a differential expression network analysis on LGG-NORMAL, GBM-NORMAL, and LGG-GBM datasets. Then, all the differentially expressed genes related to a GO term "GGT activity" were excluded. After that, for obtained potential biomarkers genes, differentially expressed lncRNAs were used to detect cis-regulatory elements (CREs) and trans-regulatory elements (TREs). To scrutinize the regulation on the cytoplasm, potential interactions between these biomarker genes and DElncRNAs were predicted. Our analysis, for the first time, revealed that GGT6, KRT33B, and KRT75 in LGG, GGT2, and KRT75 in GBM and KRT75 for LGG to GBM transformation tumors can be novel cooperative prognostic biomarkers that may be applicable for early detection of LGG, GBM, and LGG to GBM transformation tumors. Consequently, KRT75 was the most important gene being regulated at both transcriptional and post-transcriptional levels significantly. Furthermore, CREs and their relative genes were coordinative up-regulated or down-regulated suggesting CREs as regulation points of these genes. In the end, up-regulation of most DElncRNAs that had physical interaction with target genes pints out that the transcripted genes may have obstacles for translation process.

DeepCDA: deep cross-domain compound-protein affinity prediction through LSTM and convolutional neural networks.

MOTIVATION: An essential part of drug discovery is the accurate prediction of the binding affinity of new compound-protein pairs. Most of the standard computational methods assume that compounds or proteins of the test data are observed during the training phase. However, in real-world situations, the test and training data are sampled from different domains with different distributions. To cope with this challenge, we propose a deep learning-based approach that consists of three steps. In the first step, the training encoder network learns a novel representation of compounds and proteins. To this end, we combine convolutional layers and long-short-term memory layers so that the occurrence patterns of local substructures through a protein and a compound sequence are learned. Also, to encode the interaction strength of the protein and compound substructures, we propose a two-sided attention mechanism. In the second phase, to deal with the different distributions of the training and test domains, a feature encoder network is learned for the test domain by utilizing an adversarial domain adaptation approach. In the third phase, the learned test encoder network is applied to new compound-protein pairs to predict their binding affinity. RESULTS: To evaluate the proposed approach, we applied it to KIBA, Davis and BindingDB datasets. The results show that the proposed method learns a more reliable model for the test domain in more challenging situations. AVAILABILITY AND IMPLEMENTATION: https://github.com/LBBSoft/DeepCDA.

Distinguishing drug/non-drug-like small molecules in drug discovery using deep belief network.

The advent of computational methods for efficient prediction of the druglikeness of small molecules and their ever-burgeoning applications in the fields of medicinal chemistry and drug industries have been a profound scientific development, since only a few amounts of the small molecule libraries were identified as approvable drugs. In this study, a deep belief network was utilized to construct a druglikeness classification model. For this purpose, small molecules and approved drugs from the ZINC database were selected for the unsupervised pre-training step and supervised training step. Various binary fingerprints such as Macc 166 bit, PubChem 881 bit, and Morgan 2048 bit as data features were investigated. The report revealed that using an unsupervised pre-training phase can lead to a good performance model and generalizability capability. Accuracy, precision, and recall of the model for Macc features were 97%, 96%, and 99%, respectively. For more consideration about the generalizability of the model, the external data by expression and investigational drugs in drug banks as drug data and randomly selected data from the ZINC database as non-drug were created. The results confirmed the good performance and generalizability capability of the model. Also, the outcomes depicted that a large proportion of misclassified non-drug small molecules ascertain the bioavailability conditions and could be investigated as a drug in the future. Furthermore, our model attempted to tap potential opportunities as a drug filter in drug discovery.

In silico design of novel aptamers utilizing a hybrid method of machine learning and genetic algorithm.

Aptamers can be regarded as efficient substitutes for monoclonal antibodies in many diagnostic and therapeutic applications. Due to the tedious and prohibitive nature of SELEX (systematic evolution of ligands by exponential enrichment), the in silico methods have been developed to improve the enrichment processes rate. However, the majority of these methods did not show any effort in designing novel aptamers. Moreover, some target proteins may have not any binding RNA candidates in nature and a reductive mechanism is needed to generate novel aptamer pools among enormous possible combinations of nucleotide acids to be examined in vitro. We have applied a genetic algorithm (GA) with an embedded binding predictor fitness function to in silico design of RNA aptamers. As a case study of this research, all steps were accomplished to generate an aptamer pool against aminopeptidase N (CD13) biomarker. First, the model was developed based on sequential and structural features of known RNA-protein complexes. Then, utilizing RNA sequences involved in complexes with positive prediction results, as the first-generation, novel aptamers were designed and top-ranked sequences were selected. A 76-mer aptamer was identified with the highest fitness value with a 3 to 6 time higher score than parent oligonucleotides. The reliability of obtained sequences was confirmed utilizing docking and molecular dynamic simulation. The proposed method provides an important simplified contribution to the oligonucleotide-aptamer design process. Also, it can be an underlying ground to design novel aptamers against a wide range of biomarkers.

A multimodal deep learning-based drug repurposing approach for treatment of COVID-19.

Recently, various computational methods have been proposed to find new therapeutic applications of the existing drugs. The Multimodal Restricted Boltzmann Machine approach (MM-RBM), which has the capability to connect the information about the multiple modalities, can be applied to the problem of drug repurposing. The present study utilized MM-RBM to combine two types of data, including the chemical structures data of small molecules and differentially expressed genes as well as small molecules perturbations. In the proposed method, two separate RBMs were applied to find out the features and the specific probability distribution of each datum (modality). Besides, RBM was used to integrate the discovered features, resulting in the identification of the probability distribution of the combined data. The results demonstrated the significance of the clusters acquired by our model. These clusters were used to discover the medicines which were remarkably similar to the proposed medications to treat COVID-19. Moreover, the chemical structures of some small molecules as well as dysregulated genes' effect led us to suggest using these molecules to treat COVID-19. The results also showed that the proposed method might prove useful in detecting the highly promising remedies for COVID-19 with minimum side effects. All the source codes are accessible using https://github.com/LBBSoft/Multimodal-Drug-Repurposing.git.

Target controllability with minimal mediators in complex biological networks.

Controllability of a complex network system is related to finding a set of minimum number of nodes, known as drivers, controlling which allows having a full control on the dynamics of the network. For some applications, only a portion of the network is required to be controlled, for which target control has been proposed. Often, along the controlling route from driver nodes to target nodes, some mediators (intermediate nodes) are also unwillingly controlled, which might cause various side effects. In controlling cancerous cells, unwillingly controlling healthy cells, might result in weakening them, thus affecting the immune system against cancer. This manuscript proposes a suitable candidate solution to the problem of finding minimum number of driver nodes under minimal mediators. Although many others have attempted to develop algorithms to find minimum number of drivers for target control, the newly proposed algorithm is the first one that is capable of achieving this goal and at the same time, keeping the number of the mediators to a minimum. The proposed controllability condition, based on path lengths between node pairs, meets Kalman's controllability rank condition and can be applied on directed networks. Our results show that the path length is a major determinant of in properties of the target control under minimal mediators. As the average path length becomes larger, the ratio of drivers to target nodes decreases and the ratio of mediators to targets increases. The proposed methodology has potential applications in biological networks. The source code of the algorithm and the networks that have been used are available from the following link: https://github.com/LBBSoft/Target-Control-with-Minimal-Mediators.git.

mRNA and microRNA selection for breast cancer molecular subtype stratification using meta-heuristic based algorithms.

Cancer subtype stratification, which may help to make a better decision in treating cancerous patients, is one of the most crucial and challenging problems in cancer studies. To this end, various computational methods such as Feature selection, which enhances the accuracy of the classification and is an NP-Hard problem, have been proposed. However, the performance of the applied methods is still low and can be increased by the state-of-the-art and efficient methods. We used 11 efficient and popular meta-heuristic algorithms including WCC, LCA, GA, PSO, ACO, ICA, LA, HTS, FOA, DSOS and CUK along with SVM classifier to stratify human breast cancer molecular subtypes using mRNA and micro-RNA expression data. The applied algorithms select 186 mRNAs and 116 miRNAs out of 9692 mRNAs and 489 miRNAs, respectively. Although some of the selected mRNAs and miRNAs are common in different algorithms results, six miRNAs including miR-190b, miR-18a, miR-301a, miR-34c-5p, miR-18b, and miR-129-5p were selected by equal or more than three different algorithms. Further, six mRNAs, including HAUS6, LAMA2, TSPAN33, PLEKHM3, GFRA3, and DCBLD2, were chosen through two different algorithms. We have reported these miRNAs and mRNAs as important diagnostic biomarkers to the stratification of breast cancer subtypes. By investigating the literature, it is also observed that most of our reported mRNAs and miRNAs have been proposed and introduced as biomarkers in cancer subtypes stratification.

An efficient hybrid feature selection method to identify potential biomarkers in common chronic lung inflammatory diseases.

Asthma, chronic obstructive pulmonary disease (COPD), and idiopathic pulmonary fibrosis (IPF) are three serious lung inflammatory diseases. The understanding of the pathogenesis mechanism and the identification of potential prognostic biomarkers of these diseases can provide the patients with more efficient treatments. In this study, an efficient hybrid feature selection method was introduced in order to extract informative genes. We implemented an ontology-based ranking approach on differentially expressed genes following a wrapper method. The examination of the different gene ontologies and their combinations motivated us to propose a biological functional-based method to improve the performance of further wrapper methods. The results identified: TOM1L1, SRSF1, and GIT2 in asthma; CHCHD4, PAIP2, CRLF3, UBQLN4, TRAK1, PRELID1, VAMP4, CCM2, and APBB1IP in COPD; and TUFT1, GAB2, B4GALNT1, TNFRSF17, PRDM8, and SETDB2 in IPF as the potential biomarkers. The proposed method can be used to identify hub genes in other high-throughput datasets.

Drug databases and their contributions to drug repurposing.

Drug repurposing is an interesting field in the drug discovery scope because of reducing time and cost. It is also considered as an appropriate method for finding medications for orphan and rare diseases. Hence, many researchers have proposed novel methods based on databases which contain different information. Thus, a suitable organization of data which facilitates the repurposing applications and provides a tool or a web service can be beneficial. In this review, we categorize drug databases and discuss their advantages and disadvantages. Surprisingly, to the best of our knowledge, the importance and potential of databases in drug repurposing are yet to be emphasized. Indeed, the available databases can be divided into several groups based on data content, and different classes can be applied to find a new application of the existing drugs. Furthermore, we propose some suggestions for making databases more effective and popular in this field.

RPINBASE: An online toolbox to extract features for predicting RNA-protein interactions.

Feature extraction is one of the most important preprocessing steps in predicting the interactions between RNAs and proteins by applying machine learning approaches. Despite many efforts in this area, still, no suitable structural feature extraction tool has been designed. Therefore, an online toolbox, named RPINBASE which can be applied to different scopes of biological applications, is introduced in this paper. This toolbox employs efficient nested queries that enhance the speed of the requests and produces desired features in the form of positive and negative samples. To show the capabilities of the proposed toolbox, the developed toolbox was investigated in the aptamer design problem, and the obtained results are discussed. RPINBASE is an online toolbox and is accessible at http://rpinbase.com.

Synthetic repurposing of drugs against hypertension: a datamining method based on association rules and a novel discrete algorithm.

BACKGROUND: Drug repurposing aims to detect the new therapeutic benefits of the existing drugs and reduce the spent time and cost of the drug development projects. The synthetic repurposing of drugs may prove to be more useful than the single repurposing in terms of reducing toxicity and enhancing efficacy. However, the researchers have not given it serious consideration. To address the issue, a novel datamining method is introduced and applied to repositioning of drugs for hypertension (HT) which is a serious medical condition and needs some improved treatment plans to help treat it. RESULTS: A novel two-step data mining method, which is based on the If-Then association rules as well as a novel discrete optimization algorithm, was introduced and applied to the synthetic repurposing of drugs for HT. The required data were also extracted from DrugBank, KEGG, and DrugR+ databases. The findings indicated that based on the different statistical criteria, the proposed method outperformed the other state-of-the-art approaches. In contrast to the previously proposed methods which had failed to discover a list on some datasets, our method could find a combination list for all of them. CONCLUSION: Since the proposed synthetic method uses medications in small dosages, it might revive some failed drug development projects and put forward a suitable plan for treating different diseases such as COVID-19 and HT. It is also worth noting that applying efficient computational methods helps to produce better results.

Identification of biomarkers in common chronic lung diseases by co-expression networks and drug-target interactions analysis.

BACKGROUND: asthma, chronic obstructive pulmonary disease (COPD), and idiopathic pulmonary fibrosis (IPF) are three serious pulmonary diseases that contain common and unique characteristics. Therefore, the identification of biomarkers that differentiate these diseases is of importance for preventing misdiagnosis. In this regard, the present study aimed to identify the disorders at the early stages, based on lung transcriptomics data and drug-target interactions. METHODS: To this end, the differentially expressed genes were found in each disease. Then, WGCNA was utilized to find specific and consensus gene modules among the three diseases. Finally, the disease-disease similarity was analyzed, followed by determining candidate drug-target interactions. RESULTS: The results confirmed that the asthma lung transcriptome was more similar to COPD than IPF. In addition, the biomarkers were found in each disease and thus were proposed for further clinical validations. These genes included RBM42, STX5, and TRIM41 in asthma, CYP27A1, GM2A, LGALS9, SPI1, and NLRC4 in COPD, ATF3, PPP1R15A, ZFP36, SOCS3, NAMPT, and GADD45B in IPF, LRRC48 and CETN2 in asthma-COPD, COL15A1, GIMAP6, and JAM2 in asthma-IPF and LMO7, TSPAN13, LAMA3, and ANXA3 in COPD-IPF. Finally, analyzing drug-target networks suggested anti-inflammatory candidate drugs for treating the above mentioned diseases. CONCLUSION: In general, the results revealed the unique and common biomarkers among three chronic lung diseases. Eventually, some drugs were suggested for treatment purposes.

Introducing a panel for early detection of lung adenocarcinoma by using data integration of genomics, epigenomics, transcriptomics and proteomics.

Lung Adenocarcinoma is one of the most leading causes of death worldwide. Early detection of this cancer could enhance the survival chance of patients and even lead to better and more effective treatment. One of the approaches to find out more about biological malfunctions is using "omics" data. Among diverse computational procedures, data integration is becoming a striking tool to deal with complicated diseases such as cancer, considering the defective and informative nature of each kind of "omics" data. Data integration as relates to lung adenocarcinoma can lead to finding molecular biomarkers that could solve early-stage detection and progression prediction alongside other screening technologies like low-dose spiral computed tomography. In the present study, we hypothesized that genes with multiple variations are essential to provoke lung adenocarcinoma and one may use them to predict tumor formation or even cancer development. We integrated the genomic, epigenomic, transcriptomic and proteomic data. Consequently, five genes were introduced and validated by different analyses including classification of patients and survival analysis. Furthermore, we constructed a bipartite mRNA-miRNA network to identify a set of miRNAs for further experimental analyses. Finally, a sensitive and specific diagnostic panel comprising CDKN2A, CX3CR1, COX4I2, SLC15A2 and TFRC genes were identified for early detection of Lung Adenocarcinoma.