Human cognition excels at extending knowledge through analogy, where word meanings evolve along structured pathways from concrete prototypes to abstract senses via metaphor and metonymy.Do Large Language Models (LLMs) internalize this generative logic, or merely mimic statistical patterns?To investigate this, we introduce CogEvolve, a cognitive linguistic benchmark designed to test these evolutionary pathways across textual and visual modalities.Our evaluation reveals a distinct cognitive profile: models function as "Super-Associators" expert at static recognition yet fail at causal reasoning.In text, they exhibit a Frequency-Primacy Conflation, confusing statistical prevalence with cognitive basicness.Crucially, this reasoning collapses further in the visual domain.We term this deficit the Ungrounded Arrow: models possess high-fidelity concept representations (the "dots") but lack the transformational operators (the "arrows") essential for true relational understanding 1 .

Bragging, as a pervasive social-linguistic phenomenon, reflects complex human interaction patterns.However, the understanding and generation of appropriate bragging behavior in large language models (LLMs) remains underexplored.In this paper, we propose a comprehensive study that combines analytical and controllable approaches to examine bragging in LLMs.We design three tasks, bragging recognition, bragging explanation, and bragging generation, along with novel evaluation metrics to assess the models' ability to identify bragging intent, social appropriateness, and account for context sensitivity.Our analysis reveals the challenges of bragging in the social context, such as recognizing bragging and responding appropriately with bragging in conversation.This work provides new insights into how LLMs process bragging and highlights the need for more research on generating contextually appropriate behavior in LLMs 1 .

Implicit hate speech has become a significant challenge for online platforms, as it often avoids detection by large language models (LLMs) due to its indirectly expressed hateful intent.This study identifies the limitations of LLMs in detecting implicit hate speech, particularly when disguised as seemingly harmless expressions in a rhetorical device.To address this challenge, we employ a Jailbreaking strategy and Energy-based Constrained Decoding techniques, and design a small model for measuring the energy of metaphorical rhetoric.This approach can lead to LLMs generating metaphorical implicit hate speech.Our research reveals that advanced LLMs, like GPT-4o, frequently misinterpret metaphorical implicit hate speech, and fail to prevent its propagation effectively.Even specialized models, like ShieldGemma and LlamaGuard, demonstrate inadequacies in blocking such content, often misclassifying it as harmless speech.This work points out the vulnerability of current LLMs to implicit hate speech, and emphasizes the improvements to address hate speech threats better.

Major depressive disorder (MDD) is a significant global health burden; timely and precise diagnosis is essential to reduce relapse and mortality. However, existing approaches typically frame depression diagnosis as a monolithic classification problem, neglecting the multi-dimensional and hierarchical reasoning that underpins clinical interviews. We introduce DALE, a modular framework that mirrors psychiatrists'reasoning by explicitly disentangling four dimensions of patient narratives-psychological symptoms, somatic symptoms, protective factors and stressors. Leveraging GPT-4, we augment the dataset with dialogue-level annotations that map patient interview onto a list of attributes. On this auxiliary data, we train four domain-specialised LoRA adapters atop a frozen LLM; each adapter conducts a brief diagnostic dialogue and produces a concise report of its domain. A lightweight classifier then integrates these reports to generate a final summary and predict depression and suicide risk. Experiments on the D4 psychiatric dialogue benchmark show that DALE showing strong performance, while requiring far fewer trainable parameters, and yields interpretable, attribute-level evidence for its predictions.

Accurate recognition of biomedical named entities is critical for medical information extraction and knowledge discovery. However, existing methods often struggle with nested entities, entity boundary ambiguity, and cross-lingual generalization. In this paper, we propose a unified Biomedical Named Entity Recognition (BioNER) framework based on Large Language Models (LLMs). We first reformulate BioNER as a text generation task and design a symbolic tagging strategy to jointly handle both flat and nested entities with explicit boundary annotation. To enhance multilingual and multi-task generalization, we perform bilingual joint fine-tuning across multiple Chinese and English datasets. Additionally, we introduce a contrastive learning-based entity selector that filters incorrect or spurious predictions by leveraging boundary-sensitive positive and negative samples. Experimental results on four benchmark datasets and two unseen corpora show that our method achieves state-of-the-art performance and robust zero-shot generalization across languages. The source codes are freely available at https://github.com/dreamer-tx/LLMNER.

Biomedical question answering (QA) requires precise interpretation of complex medical knowledge. Large language models (LLMs) and retrieval-augmented generation (RAG) leverage external medical literature but often produce hallucinations due to noisy retrieval and insufficient verification. We propose MedTrust-Guided Iterative RAG, a framework that improves factual consistency and reduces hallucinations in medical QA. It introduces three innovations. First, citation-aware reasoning grounds generation in retrieved documents and uses Negative Knowledge Assertions when evidence is missing. Second, an iterative retrieval-verification process refines queries through Medical Gap Analysis. Third, the MedTrust-Align Module (MTAM) applies Direct Preference Optimization to align generation with verified evidence and suppress hallucination-prone patterns.

News recommender systems aim to provide personalized news reading experiences for users based on their reading history. Behavioral science studies suggest that screen-based news reading contains three successive steps: scanning, title reading, and then clicking. Adhering to these steps, we find that intra-news entity interest dominates the scanning stage, while the inter-news entity interest guides title reading and influences click decisions. Unfortunately, current methods overlook the unique utility of entities in news recommendation. To this end, we propose a novel method called IP2 to probe entity-guided reading interest at both intra- and inter-news levels. At the intra-news level, a Transformer-based entity encoder is devised to aggregate mentioned entities in the news title into one signature entity. Then, a signature entity-title contrastive pre-training is adopted to initialize entities with proper meanings using the news story context, which in the meantime facilitates us to probe for intra-news entity interest. As for the inter-news level, a dual tower user encoder is presented to capture inter-news reading interest from both the title meaning and entity sides. In addition to highlighting the contribution of inter-news entity guidance, a cross-tower attention link is adopted to calibrate title reading interest using inter-news entity interest, thus further aligning with real-world behavior. Extensive experiments on two real-world datasets demonstrate that our IP2 achieves state-of-the-art performance in news recommendation.

Named Entity Recognition (NER) in specialized domains for low-resource languages remains a significant challenge due to data scarcity and the complexity of domain-specific terminology. Existing cross-lingual approaches—spanning model-transfer and data-transfer paradigms—often suffer from semantic drift and inadequate domain adaptation. To address these limitations, we introduce BiLegalNERD, the first bilingual Chinese–Uyghur legal NER dataset, constructed via a semantics-aware label transfer strategy. We further propose CUTLM, a cross-lingual annotation method that combines dual translation with Levenshtein-based alignment to ensure high-fidelity preservation of entity boundaries across languages. In addition, we present BiLegalNER, a domain-adapted multilingual NER model incorporating vocabulary expansion and bilingual fine-tuning, significantly enhancing performance on Uyghur legal texts. Experiments demonstrate that BiLegalNER achieves state-of-the-art results, with F1-scores of 86.65% on automatically generated training data and 89.11% on fully human-annotated data—outperforming the strongest multilingual baseline by 4.18% and 4.64%, respectively. Moreover, CUTLM surpasses prior cross-lingual transfer methods by up to 9.89%, confirming its effectiveness in preserving entity integrity during label projection. These findings establish a new benchmark for Uyghur legal NER and provide a scalable framework for cross-lingual NER in low-resource settings.

Temporal network embedding (TNE) has promoted the research of knowledge discovery and reasoning on networks. It aims to embed vertices of temporal networks into a low-dimensional vector space while preserving network structures and temporal properties. However, most existing methods have limitations in capturing dynamics over long distances, which makes it difficult to explore multihop topological associations among vertices. To tackle this challenge, we propose LongTNE, which learns the long-range dynamics of vertices to endow TNE with the ability to capture high-order proximity (HP) of networks. In LongTNE, we employ graph self-supervised learning (Graph SSL) to optimize the establishment probability of deep links in each network snapshot. We also present an accumulated forward update (AFU) module to fathom global temporal evolution among multiple network snapshots. The empirical results on six temporal networks demonstrate that, in addition to achieving state-of-the-art performance on network mining tasks, LongTNE can be handily extended to existing TNE methods.

Retrieving appropriate records from the external knowledge base to generate informative responses is the core capability of end-to-end task-oriented dialogue systems (EToDs). Most of the existing methods additionally train the retrieval model or use the memory network to retrieve the knowledge base, which decouples the knowledge retrieval task from the response generation task, making it difficult to jointly optimize and failing to capture the internal relationship between the two tasks. In this paper, we propose a simple and unified generative model for task-oriented dialogue systems, which recasts the EToDs task as a single sequence generation task and uses maximum likelihood training to train the two tasks in a unified manner. To prevent the generation of non-existent records, we design the prefix trie to constrain the model generation, which ensures consistency between the generated records and the existing records in the knowledge base. Experimental results on three public benchmark datasets demonstrate that our method achieves robust performance on generating system responses and outperforms the baseline systems. To facilitate future research in this area, the code is available at https://github.com/dzy1011/Uni-ToD.

In the biomedical field, information is widely distributed across numerous pieces of literature. Extracting events between entities from biomedical texts has garnered significant attention in recent years. However, previous research primarily focus on extracting flat biomedical events, with less attention given to nested biomedical events. Moreover, existing methods for extracting nested events often overlook the long-distance dependencies and global information between trigger words and arguments within events, and they lack sufficient interaction with event type information. To address these issues, we propose a semantic segmentation-based method for extracting nested biomedical events. We introduce U-Net to capture global information and interdependencies between event entities. Additionally, we map event types to natural language text and combine them with sentences for encoding to enhance interaction. We also employ two auxiliary tasks to improve the identification of trigger words and arguments. Finally, events are extracted by identifying the four vertices of the segmented region. Experimental results on two benchmark datasets show that our method excels in recognizing nested biomedical events and outperforms current state-of-the-art methods.

The task of document-level biomedical relation extraction involves identifying relational facts between entities across sentences, given specific entities. However, most current methods overlook the associations between entity pairs and generate fixed entity representations merely through mentions, leading to irrelevant mentions interfering with the determination of relational facts. Additionally, these methods fail to consider the global information and dependencies between relational entities. To address these issues, we propose a document-level relation extraction model based on relation-guided entity-level graphs. Our model aggregates all mentions of the same entity through a relation-guided attention mechanism to obtain flexible entity representations. Furthermore, by using U-Net to generate entity-level feature graphs, it facilitates global interactions and dependency capture between entity pairs. Experimental results on two benchmark datasets demonstrate the advantages of our approach in document-level biomedical relation extraction.

Large language models (LLMs) perform well in many NLP tasks but frequently generate inaccurate information in the biomedical domain, due to hallucination issues. Retrieval-Augmented Generation (RAG) has been introduced to address this issue by integrating external knowledge, enhancing the factual accuracy of outputs. However, naive RAG encounters challenges in effectively utilizing retrieved content, particularly in specialized domains like biomedicine. LLMs often struggle to integrate retrieved content as irrelevant information can interfere with the model’s judgment. Even if relevant documents are retrieved, the model may be unable to accurately comprehend and utilize the domain-specific features due to its inherent knowledge limitations. To overcome these limitations, we propose Document Embeddings Enhanced Biomedical RAG (DEEB-RAG), a framework that incorporates document embeddings along with the original retrieved text. DEEB-RAG uses MedCPT to generate document embeddings and these embeddings are then aligned with the LLM’s semantic space using a two-stage training process on a simple projector. Experimental results on biomedical QA datasets show that DEEB-RAG improves accuracy, with an average performance increase of 2.3% over naive RAG. This demonstrates DEEB-RAG’s ability to mitigate the challenges of utilizing complex biomedical information, thereby enhancing the reliability and effectiveness of LLMs in biomedical domain.

Biomedical document-level relation extraction is a crucial technology for mining the biomedical relationships necessary for clinical diagnosis, treatment, and medical discovery. Although existing intrasentential relation extraction methods have achieved significant results, the complexity and scattered nature of information in biomedical literature require relation extraction techniques to effectively handle cross-sentence information. For example, existing methods have not been able to explicitly model the phenomena of coreference and anaphor in documents, thus affecting the model’s understanding of complex semantics within the document. To address this issue, we propose a new document-level relation extraction model with coreference and anaphor graphs. By abstracting the document into an undirected graph that includes coreference and anaphor information, the framework effectively models the interactions between entities and leverages graph convolutional network in conjunction with pretrained language model to dynamically understand graph structures. Additionally, the shift from fine-grained entity-pair level to coarse-grained document-level training and inference significantly enhances the model’s efficiency while maintaining high extraction performance. Extensive experiments demonstrate that our model achieves a 5.3% increase in F1-score over baseline models on the BioRED dataset with higher efficiency, confirming its effectiveness in handling relation extraction tasks in complex biomedical literature.

OBJECTIVE: Most existing fine-tuned biomedical large language models (LLMs) focus on enhancing performance in monolingual biomedical question answering and conversation tasks. To investigate the effectiveness of the fine-tuned LLMs on diverse biomedical natural language processing (NLP) tasks in different languages, we present Taiyi, a bilingual fine-tuned LLM for diverse biomedical NLP tasks. MATERIALS AND METHODS: We first curated a comprehensive collection of 140 existing biomedical text mining datasets (102 English and 38 Chinese datasets) across over 10 task types. Subsequently, these corpora were converted to the instruction data used to fine-tune the general LLM. During the supervised fine-tuning phase, a 2-stage strategy is proposed to optimize the model performance across various tasks. RESULTS: Experimental results on 13 test sets, which include named entity recognition, relation extraction, text classification, and question answering tasks, demonstrate that Taiyi achieves superior performance compared to general LLMs. The case study involving additional biomedical NLP tasks further shows Taiyi's considerable potential for bilingual biomedical multitasking. CONCLUSION: Leveraging rich high-quality biomedical corpora and developing effective fine-tuning strategies can significantly improve the performance of LLMs within the biomedical domain. Taiyi shows the bilingual multitasking capability through supervised fine-tuning. However, those tasks such as information extraction that are not generation tasks in nature remain challenging for LLM-based generative approaches, and they still underperform the conventional discriminative approaches using smaller language models.

The Relational Triple Extraction (RTE) task is a fundamental and essential information extraction task.Recently, the table-filling RTE methods have received lots of attention.Despite their success, they suffer from some inherent problems such as underutilizing regional information of triple.In this work, we treat the RTE task based on table-filling method as an Object Detection task and propose a one-stage Object Detection framework for Relational Triple Extraction (OD-RTE).In this framework, the vertices-based bounding box detection, coupled with auxiliary global relational triple region detection, ensuring that regional information of triple could be fully utilized.Besides, our proposed decoding scheme could extract all types of triples.In addition, the negative sampling strategy of relations in the training stage improves the training efficiency while alleviating the imbalance of positive and negative relations.The experimental results show that 1) OD-RTE achieves the state-of-the-art performance on two widely used datasets (i.e., NYT and WebNLG).2) Compared with the best performing table-filling method, OD-RTE achieves faster training and inference speed with lower GPU memory usage.To facilitate future research in this area, the codes are publicly available at https://github.com/NingJinzhong/ODRTE.

The task of extracting overlapping and nested events has received significant attention in recent times, as prior research has primarily focused on extracting flat events, overlooking the intricacies of overlapping and nested occurrences. In this work, we present a new approach to Event Extraction (EE) by reformulating it as an object detection task on a table of token pairs. Our proposed one-stage event extractor, called ODEE, can handle overlapping and nested events. The model is designed with a vertex-based tagging scheme and two auxiliary tasks of predicting the spans and types of event trigger words and argument entities, leveraging the full span information of event elements. Furthermore, in the training stage, we introduce a negative sampling method for table cells to address the imbalance problem of positive and negative table cell tags, meanwhile improving computational efficiency. Empirical evaluations demonstrate that ODEE achieves the state-of-the-art performance on three benchmarks for overlapping and nested EE (i.e., FewFC, Genia11, and Genia13). Furthermore, ODEE outperforms current state-of-the-art methods in terms of both number of parameters and inference speed, indicating its high computational efficiency. To facilitate future research in this area, the codes are publicly available at https://github.com/NingJinzhong/ODEE.

Biomedical named entity recognition (Bio-NER) is an essential task for biomedical information extraction. In this paper, we regard word-level features and character-level features as two different modalities from a novel perspective and propose a biomedical named entity recognition model based on character feature enhancement with multimodal method (called BioNER-CFEM). BioNER-CFEM can not only capture interactions between modalities, but also learn interactions within modalities. In addition, our proposed cross-attention based sparse selection mechanism can effectively alleviate the noise in the interaction process of the two ‘modalities’. Experimental results show the effectiveness of BioNER-CFEM for the Bio-NER task: it achieves performance boost over SOTA models with competitive efficiency on all six Bio-NER datasets, i.e., $+0.89, +0.64, +0.40$, $+1.40, +5.57, +2.81$ on NCBI-Disease, BC5CDR-Disease, BC5CDR-Chem, BC2GM, JNLPBA, BC4CHEMD, respectively.

Chinese Named Entity Recognition (NER) has continued to attract research attention. However, most existing studies only explore the internal features of the Chinese language but neglect other lingual modal features. Actually, as another modal knowledge of the Chinese language, English contains rich prompts about entities that can potentially be applied to improve the performance of Chinese NER. Therefore, in this study, we explore the bilingual enhancement for Chinese NER and propose a unified bilingual interaction module called the Adapted Cross-Transformers with Global Sparse Attention (ACT-S) to capture the interaction of bilingual information. We utilize a model built upon several different ACT-Ss to integrate the rich English information into the Chinese representation. Moreover, our model can learn the interaction of information between bilinguals (inter-features) and the dependency information within Chinese (intra-features). Compared with existing Chinese NER methods, our proposed model can better handle entities with complex structures. The English text that enhances the model is automatically generated by machine translation, avoiding high labour costs. Experimental results on four well-known benchmark datasets demonstrate the effectiveness and robustness of our proposed model.

BACKGROUND: Electronic medical records (EMR) contain detailed information about patient health. Developing an effective representation model is of great significance for the downstream applications of EMR. However, processing data directly is difficult because EMR data has such characteristics as incompleteness, unstructure and redundancy. Therefore, preprocess of the original data is the key step of EMR data mining. The classic distributed word representations ignore the geometric feature of the word vectors for the representation of EMR data, which often underestimate the similarities between similar words and overestimate the similarities between distant words. This results in word similarity obtained from embedding models being inconsistent with human judgment and much valuable medical information being lost. RESULTS: In this study, we propose a biomedical word embedding framework based on manifold subspace. Our proposed model first obtains the word vector representations of the EMR data, and then re-embeds the word vector in the manifold subspace. We develop an efficient optimization algorithm with neighborhood preserving embedding based on manifold optimization. To verify the algorithm presented in this study, we perform experiments on intrinsic evaluation and external classification tasks, and the experimental results demonstrate its advantages over other baseline methods. CONCLUSIONS: Manifold learning subspace embedding can enhance the representation of distributed word representations in electronic medical record texts. Reduce the difficulty for researchers to process unstructured electronic medical record text data, which has certain biomedical research value.

Semantic network is a network of concepts connected by semantic relations. It contains two forms ofbinary semantic networkandmultiplex semantic network. The associated fact prediction is a link prediction task that aims to infer the implicitly connected facts by mining the high-level representation of the network. Previous methods for associated fact prediction put much emphasis on the topological feature of network but not utilize the information of semantic expression. This paper proposes aSemanticNetworkEncoder (SemNE), which learns a feature mapping function from the binary semantic networks and can be applied to the multiplex semantic networks in a pre-training manner. SemNE is a two-stage framework that contains an embedding encoder and a prediction decoder. It jointly models the semantic information and network topology to enrich the network representation. A word self-organization method based on the factual boundary is proposed to unify the topological feature and the semantic feature representations. Experimental results on binary semantic networks show that SemNE achieves the state-of-the-art results in associated fact prediction and experimental results on multiplex semantic networks show that SemNE is scalable and can effectively improve the performance of existing models.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the strain of coronavirus that causes coronavirus disease 2019 (COVID-19), which leads to over 800,000 deaths and is still no specific medicines. Drug repositioning aiming to infer potential drugs for diseases and achieve much attention during the SARS-CoV-2 epidemic. However, find a specific drug of SARS-CoV-2 is still a large challenge that cannot be addressed well with current methods. To overcome this problem, we present a novel drug repositioning framework of heterogeneous graph convolutional networks for SARS-CoV2. The deep2CoV model can effectively search the potential drugs for SARS-CoV-2, which reduce the number of clinical trials and drug development cycles. The experimental results demonstrate the effectiveness and feasibility of our proposed deep2CoV framework.

In recent years, the plentiful information contained in Chinese legal documents has attracted a great deal of attention because of the large-scale release of the judgment documents on China Judgments Online.It is in great need of enabling machines to understand the semantic information stored in the documents which are transcribed in the form of natural language.The technique of information extraction provides a way of mining the valuable information implied in the unstructured judgment documents.We propose a Legal Triplet Extraction System for drug-related criminal judgment documents.The system extracts the entities and the semantic relations jointly and benefits from the proposed entity feature and multi-task learning framework.Furthermore, we manually annotate a dataset for Named Entity Recognition and Relation Extraction in Chinese legal domain, which contributes to training supervised triplet extraction models and evaluating the model performance.Our experimental results show that the entity feature introduction and multi-task learning framework are feasible and effective for the Legal Triplet Extraction System.The F1 score of triplet extraction finally reaches 0.836 on the legal dataset.

BACKGROUND: Both intra- and inter-sentential semantic relations in biomedical texts provide valuable information for biomedical research. However, most existing methods either focus on extracting intra-sentential relations and ignore inter-sentential ones or fail to extract inter-sentential relations accurately and regard the instances containing entity relations as being independent, which neglects the interactions between relations. We propose a novel sequence labeling-based biomedical relation extraction method named Bio-Seq. In the method, sequence labeling framework is extended by multiple specified feature extractors so as to facilitate the feature extractions at different levels, especially at the inter-sentential level. Besides, the sequence labeling framework enables Bio-Seq to take advantage of the interactions between relations, and thus, further improves the precision of document-level relation extraction. RESULTS: Our proposed method obtained an F1-score of 63.5% on BioCreative V chemical disease relation corpus, and an F1-score of 54.4% on inter-sentential relations, which was 10.5% better than the document-level classification baseline. Also, our method achieved an F1-score of 85.1% on n2c2-ADE sub-dataset. CONCLUSION: Sequence labeling method can be successfully used to extract document-level relations, especially for boosting the performance on inter-sentential relation extraction. Our work can facilitate the research on document-level biomedical text mining.

The explosive growth of biomedical literature has created a rich source of knowledge, such as that on protein-protein interactions (PPIs) and drug-drug interactions (DDIs), locked in unstructured free text. Biomedical relation classification aims to automatically detect and classify biomedical relations, which has great benefits for various biomedical research and applications. In the past decade, significant progress has been made in biomedical relation classification. With the advance of neural network methodology, neural network-based approaches have been applied in biomedical relation classification and achieved state-of-the-art performance for some public datasets and shared tasks. In this review, we describe the recent advancement of neural network-based approaches for classifying biomedical relations. We summarize the available corpora and introduce evaluation metrics. We present the general framework for neural network-based approaches in biomedical relation extraction and pretrained word embedding resources. We discuss neural network-based approaches, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). We conclude by describing the remaining challenges and outlining future directions.

Biomedical relation extraction can automatically extract high-quality biomedical relations from biomedical texts, which is a vital step for the mining of biomedical knowledge hidden in the literature. Recurrent neural networks (RNNs) and convolutional neural networks (CNNs) are two major neural network models for biomedical relation extraction. Neural network-based methods for biomedical relation extraction typically focus on the sentence sequence and employ RNNs or CNNs to learn the latent features from sentence sequences separately. However, RNNs and CNNs have their own advantages for biomedical relation extraction. Combining RNNs and CNNs may improve biomedical relation extraction. In this paper, we present a hybrid model for the extraction of biomedical relations that combines RNNs and CNNs. First, the shortest dependency path (SDP) is generated based on the dependency graph of the candidate sentence. To make full use of the SDP, we divide the SDP into a dependency word sequence and a relation sequence. Then, RNNs and CNNs are employed to automatically learn the features from the sentence sequence and the dependency sequences, respectively. Finally, the output features of the RNNs and CNNs are combined to detect and extract biomedical relations. We evaluate our hybrid model using five public (protein-protein interaction) PPI corpora and a (drug-drug interaction) DDI corpus. The experimental results suggest that the advantages of RNNs and CNNs in biomedical relation extraction are complementary. Combining RNNs and CNNs can effectively boost biomedical relation extraction performance.

The strategies for important node detection according to topological structures are widely explored in complex networks. The diameter is a very important topological parameter among various network topological indicators. However, it is seldom utilized in important node searching methods. In this study, we defined the nodes on the diameter paths as central nodes and proposed a Diameter Center Detection (DCD) method to search the central nodes. In the experiments, the DCD method is applied to three deterministic networks, a series of small-world networks, scale-free networks and five real networks, respectively. The experimental results show that the central nodes searched by DCD have advantages over the nodes of the whole network in the evaluations of various centrality measures, e.g. Betweenness Centrality (BC), Closeness Centrality (CC), Degree Centrality (DC) and k-shell decomposition results. In addition, after deleting central nodes, the network structure changes a lot on the perspective from both diameter and giant component. Furthermore, the experimental results show that the edge deleting policy based on DCD is effective in the way that deleting fewer edges disrupting more node pair connectivity.

There is a growing interest in multilabel image classification. In this study, we proposed a novel texture classification approach combining the fractal theory and the LBP complex networks (FLCN). The complex networks were constructed based on the LBP features and the pixel relationships of the image. The suitable parameters and the combination of statistical properties on the complex networks were investigated to represent the texture of the image. The experimental results show that the FLCN method has good performance in the classifications of the segmented images and the biomedical images. In addition, the approach is more robust compared with other methods.

Biomedical events can reveal crucial processes in biomedical research. As an important step in biomedical event extraction, biomedical event trigger detection has become a research hotspot. Traditional machine learning methods, which aim to manually design powerful features fed to the classifiers, greatly depend on the understanding of the specific task. In this paper, we propose an approach to automatically learn good features from raw input without manual intervention. The approach is based on dependency-based word embedding and first learns dependency-based word embedding from all available PubMed abstracts. The word embedding contains rich functional and semantic information. Then neural network architecture is used to learn better feature representation based on raw dependency-based word embedding. Meanwhile, we dynamically adjust the embedding while training for adapting to the trigger classification task. Finally, softmax classifier labels the examples by specific trigger class using the features learned by the model. The experimental results show that our approach achieves a micro F1 score of 78.27% and a macro F1 score of 76.94% in significant trigger classes, and performs better than baseline methods. In addition, we can achieve the semantic distributed representation of every trigger word.

BACKGROUND: Revealing protein complexes are important for understanding principles of cellular organization and function. High-throughput experimental techniques have produced a large amount of protein interactions, which makes it possible to predict protein complexes from protein-protein interaction (PPI) networks. However, the small amount of known physical interactions may limit protein complex detection. METHODS: The new PPI networks are constructed by integrating PPI datasets with the large and readily available PPI data from biomedical literature, and then the less reliable PPI between two proteins are filtered out based on semantic similarity and topological similarity of the two proteins. Finally, the supervised learning protein complex detection (SLPC), which can make full use of the information of available known complexes, is applied to detect protein complex on the new PPI networks. RESULTS: The experimental results of SLPC on two different categories yeast PPI networks demonstrate effectiveness of the approach: compared with the original PPI networks, the best average improvements of 4.76, 6.81 and 15.75 percentage units in the F-score, accuracy and maximum matching ratio (MMR) are achieved respectively; compared with the denoising PPI networks, the best average improvements of 3.91, 4.61 and 12.10 percentage units in the F-score, accuracy and MMR are achieved respectively; compared with ClusterONE, the start-of the-art complex detection method, on the denoising extended PPI networks, the average improvements of 26.02 and 22.40 percentage units in the F-score and MMR are achieved respectively. CONCLUSIONS: The experimental results show that the performances of SLPC have a large improvement through integration of new receivable PPI data from biomedical literature into original PPI networks and denoising PPI networks. In addition, our protein complexes detection method can achieve better performance than ClusterONE.

In this paper, we analyze six protein interaction networks widely used for protein complex detection, and compare the performance of six classic computational methods on them in order to find the relations between network characteristics and the performances of these complex detection methods. Furthermore, we explore the difference among the two complexes detected by different methods and the real complex by using a visualization approach which can easily find the difference of two methods and locate the undetected proteins.

In this paper, we present an approach to automatically construct disease related knowledge summarization from biomedical literature. In this approach, first Kullback-Leibler divergence combined with mutual information metric is used to extract disease salient information. Then deep search based on depth first search (DFS) is applied to find hidden relations between biomedical entities. Finally random walk algorithm is exploited to filter out the weak relations. The experimental results show that our approach achieves a precision of 60% and a recall of 61% on salient information extraction, and outperforms the method of Combo. In addition, the method of deep search obtains more hidden relations than the original correlation extraction methods.

Indirect immunofluorescence (IIF) with HEp-2 cells is considered as a powerful, sensitive and comprehensive technique for analyzing antinuclear autoantibodies (ANAs). Fractal dimension can be used on the analysis of image representing and also on the property quantification like texture complexity and spatial occupation. In this study, we apply the fractal theory in the application of HEp-2 cell staining pattern classification, utilizing fractal descriptor firstly in the HEp-2 cell pattern classification with the help of morphological descriptor and pixel difference descriptor. The method is applied to the data set of MIVIA and uses the support vector machine (SVM) classifier. Experimental results show that the fractal descriptor combining with other two descriptors makes the precisions of six patterns more stable, all above 50%, achieving 62.04% overall accuracy at best with relatively simple feature vectors.

The rapidly growing biomedical literature provides a significantly large and readily available source of PPI data. In this paper, we present supervised learning and data integration based complex detection approach. In this approach, a sophisticated natural language processing system, PPIExtractor, is employed to extract new PPI interactions from biomedical literature which are then integrated into original PPI networks. Then a supervised learning model, built by via of the information of available known complexes, is used in the multiple complex detection stages, e.g. the cliques filtering, growth, and candidate complex filtering. The experimental results on three yeast PPI networks demonstrate the effectiveness of our approach.