Biomedical Relations Research Articles

MeSH2Matrix: combining MeSH keywords and machine learning for biomedical relation classification based on PubMed

Biomedical relation classification has been significantly improved by the application of advanced machine learning techniques on the raw texts of scholarly publications. Despite this improvement, the reliance on large chunks of raw text makes these algorithms suffer in terms of generalization, precision, and reliability. The use of the distinctive characteristics of bibliographic metadata can prove effective in achieving better performance for this challenging task. In this research paper, we introduce an approach for biomedical relation classification using the qualifiers of co-occurring Medical Subject Headings (MeSH). First of all, we introduce MeSH2Matrix, our dataset consisting of 46,469 biomedical relations curated from PubMed publications using our approach. Our dataset includes a matrix that maps associations between the qualifiers of subject MeSH keywords and those of object MeSH keywords. It also specifies the corresponding Wikidata relation type and the superclass of semantic relations for each relation. Using MeSH2Matrix, we build and train three machine learning models (Support Vector Machine [SVM], a dense model [D-Model], and a convolutional neural network [C-Net]) to evaluate the efficiency of our approach for biomedical relation classification. Our best model achieves an accuracy of 70.78% for 195 classes and 83.09% for five superclasses. Finally, we provide confusion matrix and extensive feature analyses to better examine the relationship between the MeSH qualifiers and the biomedical relations being classified. Our results will hopefully shed light on developing better algorithms for biomedical ontology classification based on the MeSH keywords of PubMed publications. For reproducibility purposes, MeSH2Matrix, as well as all our source codes, are made publicly accessible at https://github.com/SisonkeBiotik-Africa/MeSH2Matrix.

HeteroKGRep: Heterogeneous Knowledge Graph based Drug Repositioning

The process of developing new drugs is both time-consuming and costly, often taking over a decade and billions of dollars to obtain regulatory approval. Additionally, the complexity of patent protection for novel compounds presents challenges for pharmaceutical innovation. Drug repositioning offers an alternative strategy to uncover new therapeutic uses for existing medicines. Previous repositioning models have been limited by their reliance on homogeneous data sources, failing to leverage the rich information available in heterogeneous biomedical knowledge graphs. We propose HeteroKGRep, a novel drug repositioning model that utilizes heterogeneous graphs to address these limitations. HeteroKGRep is a multi-step framework that first generates a similarity graph from hierarchical concept relations. It then applies SMOTE over-sampling to address class imbalance before generating node sequences using a heterogeneous graph neural network. Drug and disease embeddings are extracted from the network and used for prediction. We evaluated HeteroKGRep on a graph containing biomedical concepts and relations from ontologies, pathways and literature. It achieved state-of-the-art performance with 99% accuracy, 95% AUC ROC and 94% average precision on predicting repurposing opportunities. Compared to existing homogeneous approaches, HeteroKGRep leverages diverse knowledge sources to enrich representation learning. Based on heterogeneous graphs, HeteroKGRep can discover new drug-disease associations, leveraging de novo drug development. This work establishes a promising new paradigm for knowledge-guided drug repositioning using multimodal biomedical data.

A framework for integrating biomedical knowledge in Wikidata with open biological and biomedical ontologies and MeSH keywords

This study presents a comprehensive framework to enhance Wikidata as an open and collaborative knowledge graph by integrating Open Biological and Biomedical Ontologies (OBO) and Medical Subject Headings (MeSH) keywords from PubMed publications. The primary data sources include OBO ontologies and MeSH keywords, which were collected and classified using SPARQL queries for RDF knowledge graphs. The semantic alignment between OBO ontologies and Wikidata was evaluated, revealing significant gaps and distorted representations that necessitate both automated and manual interventions for improvement. We employed pointwise mutual information to extract biomedical relations among the 5000 most common MeSH keywords in PubMed, achieving an accuracy of 89.40 % for superclass-based classification and 75.32 % for relation type-based classification. Additionally, Integrated Gradients were utilized to refine the classification by removing irrelevant MeSH qualifiers, enhancing overall efficiency. The framework also explored the use of MeSH keywords to identify PubMed reviews supporting unsupported Wikidata relations, finding that 45.8 % of these relations were not present in PubMed, indicating potential inconsistencies in Wikidata. The contributions of this study include improved methodologies for enriching Wikidata with biomedical information, validated semantic alignments, and efficient classification processes. This work enhances the interoperability and multilingual capabilities of biomedical ontologies and demonstrates the critical role of MeSH keywords in verifying semantic relations, thereby contributing to the robustness and accuracy of collaborative biomedical knowledge graphs.

Towards discovery: an end-to-end system for uncovering novel biomedical relations.

Biomedical relation extraction is an ongoing challenge within the natural language processing community. Its application is important for understanding scientific biomedical literature, with many use cases, such as drug discovery, precision medicine, disease diagnosis, treatment optimization and biomedical knowledge graph construction. Therefore, the development of a tool capable of effectively addressing this task holds the potential to improve knowledge discovery by automating the extraction of relations from research manuscripts. The first track in the BioCreative VIII competition extended the scope of this challenge by introducing the detection of novel relations within the literature. This paper describes that our participation system initially focused on jointly extracting and classifying novel relations between biomedical entities. We then describe our subsequent advancement to an end-to-end model. Specifically, we enhanced our initial system by incorporating it into a cascading pipeline that includes a tagger and linker module. This integration enables the comprehensive extraction of relations and classification of their novelty directly from raw text. Our experiments yielded promising results, and our tagger module managed to attain state-of-the-art named entity recognition performance, with a micro F1-score of 90.24, while our end-to-end system achieved a competitive novelty F1-score of 24.59. The code to run our system is publicly available at https://github.com/ieeta-pt/BioNExt. Database URL: https://github.com/ieeta-pt/BioNExt.

Relation-aware deep neural network enables more efficient biomedical knowledge acquisition from massive literature

Biomedical knowledge is typically organized in a relational scheme, such as chemical-disease relation, gene-disease relation, and gene-pathway relation. Biomedical scientists heavily rely on search engines to acquire up-to-date relational knowledge from massive biomedical articles. The navigation efficiency of the retrieval process, however, is significantly restricted by keyword matching techniques unaware of the biomedical relations of these keywords in articles. To bridge the gap between existing retrieval techniques and practical access demands for relational knowledge, we present a novel framework, Biomedical Relation-Aware Document Ranking (BioRADR), capable of retrieving articles expressing specific relations with respect to the queried entity pair. Based on a deep neural network, BioRADR can be trained from large-scale data automatically annotated via distant supervision, and empirical evaluation reveals that it outperforms the strongest baseline by over 8 points in NDCG@1. We implement an online system (http://bioradr.ai.thunlp.org/) based on BioRADR, enabling more efficient relation-oriented retrieval of biomedical articles.

BioEGRE: a linguistic topology enhanced method for biomedical relation extraction based on BioELECTRA and graph pointer neural network

BackgroundAutomatic and accurate extraction of diverse biomedical relations from literature is a crucial component of bio-medical text mining. Currently, stacking various classification networks on pre-trained language models to perform fine-tuning is a common framework to end-to-end solve the biomedical relation extraction (BioRE) problem. However, the sequence-based pre-trained language models underutilize the graphical topology of language to some extent. In addition, sequence-oriented deep neural networks have limitations in processing graphical features.ResultsIn this paper, we propose a novel method for sentence-level BioRE task, BioEGRE (BioELECTRA and Graph pointer neural net-work for Relation Extraction), aimed at leveraging the linguistic topological features. First, the biomedical literature is preprocessed to retain sentences involving pre-defined entity pairs. Secondly, SciSpaCy is employed to conduct dependency parsing; sentences are modeled as graphs based on the parsing results; BioELECTRA is utilized to generate token-level representations, which are modeled as attributes of nodes in the sentence graphs; a graph pointer neural network layer is employed to select the most relevant multi-hop neighbors to optimize representations; a fully-connected neural network layer is employed to generate the sentence-level representation. Finally, the Softmax function is employed to calculate the probabilities. Our proposed method is evaluated on three BioRE tasks: a multi-class (CHEMPROT) and two binary tasks (GAD and EU-ADR). The results show that our method achieves F1-scores of 79.97% (CHEMPROT), 83.31% (GAD), and 83.51% (EU-ADR), surpassing the performance of existing state-of-the-art models.ConclusionThe experimental results on 3 biomedical benchmark datasets demonstrate the effectiveness and generalization of BioEGRE, which indicates that linguistic topology and a graph pointer neural network layer explicitly improve performance for BioRE tasks.

SnorkelPlus: A Novel Approach for Identifying Relationships Among Biomedical Entities Within Abstracts

AbstractIdentifying relationships between biomedical entities from unstructured biomedical text is a challenging task. SnorkelPlus has been proposed to provide the flexibility to extract these biomedical relations without any human effort. Our proposed model, SnorkelPlus, is aimed at finding connections between gene and disease entities. We achieved three objectives: (i) extract only gene and disease articles from NCBI’s, PubMed or PubMed central database, (ii) define reusable label functions and (iii) ensure label function accuracy using generative and discriminative models. We utilized deep learning methods to achieve label training data and achieved an AUROC of 85.60% for the generated gene and disease corpus from PubMed articles. Snorkel achieved an AUPR of 45.73%, which is +2.3% higher than the baseline model. We created a gene–disease relation database using SnorkelPlus from approximately 29 million scientific abstracts without involving annotated training datasets. Furthermore, we demonstrated the generalizability of our proposed application on abstracts of PubMed articles enriched with different gene and disease relations. In the future, we plan to design a graphical database using Neo4j.

JCBIE: a joint continual learning neural network for biomedical information extraction

Extracting knowledge from heterogeneous data sources is fundamental for the construction of structured biomedical knowledge graphs (BKGs), where entities and relations are represented as nodes and edges in the graphs, respectively. Previous biomedical knowledge extraction methods simply considered limited entity types and relations by using a task-specific training set, which is insufficient for large-scale BKGs development and downstream task applications in different scenarios. To alleviate this issue, we propose a joint continual learning biomedical information extraction (JCBIE) network to extract entities and relations from different biomedical information datasets. By empirically studying different joint learning and continual learning strategies, the proposed JCBIE can learn and expand different types of entities and relations from different datasets. JCBIE uses two separated encoders in joint-feature extraction, hence can effectively avoid the feature confusion problem comparing with using one hard-parameter sharing encoder. Specifically, it allows us to adopt entity augmented inputs to establish the interaction between named entity recognition and relation extraction. Finally, a novel evaluation mechanism is proposed for measuring cross-corpus generalization errors, which was ignored by traditional evaluation methods. Our empirical studies show that JCBIE achieves promising performance when continual learning strategy is adopted with multiple corpora.

The Generation of a Lung Cancer Health Factor Distribution Using Patient Graphs Constructed From Electronic Medical Records: Retrospective Study.

Electronic medical records (EMRs) of patients with lung cancer (LC) capture a variety of health factors. Understanding the distribution of these factors will help identify key factors for risk prediction in preventive screening for LC. We aimed to generate an integrated biomedical graph from EMR data and Unified Medical Language System (UMLS) ontology for LC, and to generate an LC health factor distribution from a hospital EMR of approximately 1 million patients. The data were collected from 2 sets of 1397 patients with and those without LC. A patient-centered health factor graph was plotted with 108,000 standardized data, and a graph database was generated to integrate the graphs of patient health factors and the UMLS ontology. With the patient graph, we calculated the connection delta ratio (CDR) for each of the health factors to measure the relative strength of the factor's relationship to LC. The patient graph had 93,000 relations between the 2794 patient nodes and 650 factor nodes. An LC graph with 187 related biomedical concepts and 188 horizontal biomedical relations was plotted and linked to the patient graph. Searching the integrated biomedical graph with any number or category of health factors resulted in graphical representations of relationships between patients and factors, while searches using any patient presented the patient's health factors from the EMR and the LC knowledge graph (KG) from the UMLS in the same graph. Sorting the health factors by CDR in descending order generated a distribution of health factors for LC. The top 70 CDR-ranked factors of disease, symptom, medical history, observation, and laboratory test categories were verified to be concordant with those found in the literature. By collecting standardized data of thousands of patients with and those without LC from the EMR, it was possible to generate a hospital-wide patient-centered health factor graph for graph search and presentation. The patient graph could be integrated with the UMLS KG for LC and thus enable hospitals to bring continuously updated international standard biomedical KGs from the UMLS for clinical use in hospitals. CDR analysis of the graph of patients with LC generated a CDR-sorted distribution of health factors, in which the top CDR-ranked health factors were concordant with the literature. The resulting distribution of LC health factors can be used to help personalize risk evaluation and preventive screening recommendations.

An automatic hypothesis generation for plausible linkage between xanthium and diabetes

There has been a significant increase in text mining implementation for biomedical literature in recent years. Previous studies introduced the implementation of text mining and literature-based discovery to generate hypotheses of potential candidates for drug development. By conducting a hypothesis-generation step and using evidence from published journal articles or proceedings, previous studies have managed to reduce experimental time and costs. First, we applied the closed discovery approach from Swanson’s ABC model to collect publications related to 36 Xanthium compounds or diabetes. Second, we extracted biomedical entities and relations using a knowledge extraction engine, the Public Knowledge Discovery Engine for Java or PKDE4J. Third, we built a knowledge graph using the obtained bio entities and relations and then generated paths with Xanthium compounds as source nodes and diabetes as the target node. Lastly, we employed graph embeddings to rank each path and evaluated the results based on domain experts’ opinions and literature. Among 36 Xanthium compounds, 35 had direct paths to five diabetes-related nodes. We ranked 2,740,314 paths in total between 35 Xanthium compounds and three diabetes-related phrases: type 1 diabetes, type 2 diabetes, and diabetes mellitus. Based on the top five percentile paths, we concluded that adenosine, choline, beta-sitosterol, rhamnose, and scopoletin were potential candidates for diabetes drug development using natural products. Our framework for hypothesis generation employs a closed discovery from Swanson’s ABC model that has proven very helpful in discovering biological linkages between bio entities. The PKDE4J tools we used to capture bio entities from our document collection could label entities into five categories: genes, compounds, phenotypes, biological processes, and molecular functions. Using the BioPREP model, we managed to interpret the semantic relatedness between two nodes and provided paths containing valuable hypotheses. Lastly, using a graph-embedding algorithm in our path-ranking analysis, we exploited the semantic relatedness while preserving the graph structure properties.

PubmedKB: an interactive web server for exploring biomedical entity relations in the biomedical literature.

With the proliferation of genomic sequence data for biomedical research, the exploration of human genetic information by domain experts requires a comprehensive interrogation of large numbers of scientific publications in PubMed. However, a query in PubMed essentially provides search results sorted only by the date of publication. A search engine for retrieving and interpreting complex relations between biomedical concepts in scientific publications remains lacking. Here, we present pubmedKB, a web server designed to extract and visualize semantic relationships between four biomedical entity types: variants, genes, diseases, and chemicals. pubmedKB uses state-of-the-art natural language processing techniques to extract semantic relations from the large number of PubMed abstracts. Currently, over 2 million semantic relations between biomedical entity pairs are extracted from over 33 million PubMed abstracts in pubmedKB. pubmedKB has a user-friendly interface with an interactive semantic graph, enabling the user to easily query entities and explore entity relations. Supporting sentences with the highlighted snippets allow to easily navigate the publications. Combined with a new explorative approach to literature mining and an interactive interface for researchers, pubmedKB thus enables rapid, intelligent searching of the large biomedical literature to provide useful knowledge and insights. pubmedKB is available at https://www.pubmedkb.cc/.

Investigation of improving the pre-training and fine-tuning of BERT model for biomedical relation extraction

BackgroundRecently, automatically extracting biomedical relations has been a significant subject in biomedical research due to the rapid growth of biomedical literature. Since the adaptation to the biomedical domain, the transformer-based BERT models have produced leading results on many biomedical natural language processing tasks. In this work, we will explore the approaches to improve the BERT model for relation extraction tasks in both the pre-training and fine-tuning stages of its applications. In the pre-training stage, we add another level of BERT adaptation on sub-domain data to bridge the gap between domain knowledge and task-specific knowledge. Also, we propose methods to incorporate the ignored knowledge in the last layer of BERT to improve its fine-tuning.ResultsThe experiment results demonstrate that our approaches for pre-training and fine-tuning can improve the BERT model performance. After combining the two proposed techniques, our approach outperforms the original BERT models with averaged F1 score improvement of 2.1% on relation extraction tasks. Moreover, our approach achieves state-of-the-art performance on three relation extraction benchmark datasets.ConclusionsThe extra pre-training step on sub-domain data can help the BERT model generalization on specific tasks, and our proposed fine-tuning mechanism could utilize the knowledge in the last layer of BERT to boost the model performance. Furthermore, the combination of these two approaches further improves the performance of BERT model on the relation extraction tasks.

Malariology and decolonization: Eastern European experts from the League of Nations to the World Health Organization

AbstractThe article de-centres the global history of disease by examining the agency of Eastern European expertise at international organizations and during decolonization. It challenges accounts of anti-malaria policies at the League of Nations Health Organization and at the World Health Organization written from a Western, particularly North American perspective, or on the basis of local reactions to Western interventions. The contribution proposes an analysis of circulations and ideas across multiple cultural, social and political spaces: post-imperial European states, (post)colonial territories and bureaucracies of international organizations. From the 1920s to the 1960s, Eastern European experts played a crucial role in the transformation of malaria from an imperial disease that tested governance over ‘tropical’ peoples into an issue of global health and nation-state building. However, regional representatives reproduced civilizational hierarchies intrinsic to North–South biomedical relations. The global entanglements of Eastern European malariology show that liberation from disease was less about communism or liberalism, and more about national renewal, statehood and world hierarchies.

SUBJECTS OF PRIVATE LAW BIOMEDICAL RELATIONS

SUBJECTS OF PRIVATE LAW BIOMEDICAL RELATIONS

MRC4BioER: Joint extraction of biomedical entities and relations in the machine reading comprehension framework

Extracting entities and their relations from unstructured literature to form structured triplets is essential for biomedical knowledge extraction. Because sentences in biomedical datasets usually have many special overlapping triplets, it is difficult to use previous work to extract these triplets effectively. In this work, we propose a novel tagging strategy to achieve joint extraction in the machine reading comprehension framework. On the one hand, our method uses Query in the machine reading comprehension framework to introduce the information of the specific relation. On the other hand, our method introduces a tagging strategy for overlapping triplets in the biomedical domain. We use CHEMPROT and DDIExtraction2013 datasets to evaluate our method. The experimental results demonstrate that our proposed method can enhance the model’s ability to deal with overlapping triplets, improving extraction performance.

Deep learning models in detection of dietary supplement adverse event signals from Twitter.

ObjectiveThe objective of this study is to develop a deep learning pipeline to detect signals on dietary supplement-related adverse events (DS AEs) from Twitter.Materials and MethodsWe obtained 247 807 tweets ranging from 2012 to 2018 that mentioned both DS and AE. We designed a tailor-made annotation guideline for DS AEs and annotated biomedical entities and relations on 2000 tweets. For the concept extraction task, we fine-tuned and compared the performance of BioClinical-BERT, PubMedBERT, ELECTRA, RoBERTa, and DeBERTa models with a CRF classifier. For the relation extraction task, we fine-tuned and compared BERT models to BioClinical-BERT, PubMedBERT, RoBERTa, and DeBERTa models. We chose the best-performing models in each task to assemble an end-to-end deep learning pipeline to detect DS AE signals and compared the results to the known DS AEs from a DS knowledge base (ie, iDISK).ResultsDeBERTa-CRF model outperformed other models in the concept extraction task, scoring a lenient microaveraged F1 score of 0.866. RoBERTa model outperformed other models in the relation extraction task, scoring a lenient microaveraged F1 score of 0.788. The end-to-end pipeline built on these 2 models was able to extract DS indication and DS AEs with a lenient microaveraged F1 score of 0.666.ConclusionWe have developed a deep learning pipeline that can detect DS AE signals from Twitter. We have found DS AEs that were not recorded in an existing knowledge base (iDISK) and our proposed pipeline can as sist DS AE pharmacovigilance.

Distantly supervised biomedical relation extraction using piecewise attentive convolutional neural network and reinforcement learning

There have been various methods to deal with the erroneous training data in distantly supervised relation extraction (RE), however, their performance is still far from satisfaction. We aimed to deal with the insufficient modeling problem on instance-label correlations for predicting biomedical relations using deep learning and reinforcement learning. In this study, a new computational model called piecewise attentive convolutional neural network and reinforcement learning (PACNN+RL) was proposed to perform RE on distantly supervised data generated from Unified Medical Language System with MEDLINE abstracts and benchmark datasets. In PACNN+RL, PACNN was introduced to encode semantic information of biomedical text, and the RL method with memory backtracking mechanism was leveraged to alleviate the erroneous data issue. Extensive experiments were conducted on 4 biomedical RE tasks. The proposed PACNN+RL model achieved competitive performance on 8 biomedical corpora, outperforming most baseline systems. Specifically, PACNN+RL outperformed all baseline methods with the F1-score of 0.5592 on the may-prevent dataset, 0.6666 on the may-treat dataset, and 0.3838 on the DDI corpus, 2011. For the protein-protein interaction RE task, we obtained new state-of-the-art performance on 4 out of 5 benchmark datasets. The performance on many distantly supervised biomedical RE tasks was substantially improved, primarily owing to the denoising effect of the proposed model. It is anticipated that PACNN+RL will become a useful tool for large-scale RE and other downstream tasks to facilitate biomedical knowledge acquisition. We also made the demonstration program and source code publicly available at http://112.74.48.115:9000/.

CROssBAR: comprehensive resource of biomedical relations with knowledge graph representations.

Systemic analysis of available large-scale biological/biomedical data is critical for studying biological mechanisms, and developing novel and effective treatment approaches against diseases. However, different layers of the available data are produced using different technologies and scattered across individual computational resources without any explicit connections to each other, which hinders extensive and integrative multi-omics-based analysis. We aimed to address this issue by developing a new data integration/representation methodology and its application by constructing a biological data resource. CROssBAR is a comprehensive system that integrates large-scale biological/biomedical data from various resources and stores them in a NoSQL database. CROssBAR is enriched with the deep-learning-based prediction of relationships between numerous data entries, which is followed by the rigorous analysis of the enriched data to obtain biologically meaningful modules. These complex sets of entities and relationships are displayed to users via easy-to-interpret, interactive knowledge graphs within an open-access service. CROssBAR knowledge graphs incorporate relevant genes-proteins, molecular interactions, pathways, phenotypes, diseases, as well as known/predicted drugs and bioactive compounds, and they are constructed on-the-fly based on simple non-programmatic user queries. These intensely processed heterogeneous networks are expected to aid systems-level research, especially to infer biological mechanisms in relation to genes, proteins, their ligands, and diseases.

Pattern-based bootstrapping framework for biomedical relation extraction

The progress made in the realm of ‘-omics’ technologies has led to a tremendous increase in the quantum of biomedical research published. Information extraction from this huge unstructured mass of data needs automation through text mining methods. Biomedical relation extraction is one such vital automation processes for extracting biomedical relations hidden in scientific literature. In the recent past, several supervised machine learning methods have been used to identify biomedical relations. However, given the variations in textual expression, huge corpus size and small task-specific training data, semi-supervised techniques appear to perform better. To this end, we propose a system that uses the semi-supervised bootstrapping algorithm to extract biomedical relations from text. The unlabelled corpus used contains sentences with biomedical entities represented as patterns with the dependency tree feature. Bootstrapping starts with a seed set and iteratively learns new patterns from the unlabelled corpus. We have designed a three-level masking technique to generate new patterns, and incorporated three types of scoring to help select appropriate patterns. The pattern-based bootstrapping method performs well with a minimum seed set. The system is able to extract 37,450 patterns from the unlabelled corpus that represents different biomedical relations. These patterns, in turn, are able to identify 460,886 relation pairs with 1327 single, and 1012 coupled, trigger words that convey the semantics of the biomedical relation. More than 64% of the identified relations have evidence in the CTD database.

Abstract 5235: In vitro validation of drug-target interactions revealed in silico by Comprehensive Resource of Biomedical Relations with Network Representations and Deep Learning (CROssBAR) in HCC

Abstract Computational methods are more commonly employed for our understanding of cellular signaling mechanisms with the aim to develop cures for diseases including cancer. Identification of disease specific targeted drugs requires systems level analysis of cellular networks. Our research group developed a comprehensive resource, called CROssBAR, which connects various biological data sources in order to provide a well-connected resource with a focus on drug discovery and precision medicine. CROssBAR aims to predict and reveal novel drug-target interactions using deep learning algorithms. CROssBAR predictions were validated on hepatocellular carcinoma cells which is the 5th most prevalent and 3th deadliest cancer worldwide. We have selected 2 new small molecule kinase inhibitors Lenvatinib, Sunitinib along with Sorafenib and Regorafenib which are currently used in clinics. CROssBAR identifies novel and known gene targets for Lenvatinib, Sunitinib in comparison with Sorafenib and Regorafenib. Genes from SAPK/JNK and PI3K/Akt pathway that were not previously associated with hepatocellular carcinoma therapeutics were determined. All selected small molecules were bioactive on Huh7 and Mahlavu cells by SRB assay. Cell death was characterized by Annexin V which was due to G1/S cell cycle arrest. Further analysis of proteins involved in the SAPK/JNK, PI3K/Akt pathways and apoptotic markers such as PARP and Caspases were examined with immunoblotting upon treatment. As a result, cellular stress mechanisms activated PI3K/Akt and SAPK/JNK pathways which caused programmed cell death in HCC. With our deep learning approach, we were able to demonstrate a correlation between Lenvatinib and Sunitinib and their novel targets in HCC. Our validation results have illustrated that the comprehensive CROssBAR tool developed by our research group achieved in vitro significant predictions of new drugs and their targets against HCC that were not previously reported. Hence with this study we were able to demonstrate the use and validity of the CROssBAR tool along with new targets for primary liver cancer therapeutics. Citation Format: Esra Nalbat, Ahmet Sureyya Rifaioglu, Tunca Dogan, Maria Jesus Martin, Rengul Cetin-Atalay, Volkan Atalay. In vitro validation of drug-target interactions revealed in silico by Comprehensive Resource of Biomedical Relations with Network Representations and Deep Learning (CROssBAR) in HCC [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5235.