A multi-dimensional semantic pseudo-relevance feedback framework for information retrieval.

Pre-trained models have garnered significant attention in the field of information retrieval, particularly for improving document ranking. Typically, an initial retrieval step using sparse methods such as BM25 is employed to obtain a set of pseudo-relevant documents, followed by re-ranking with a pre-trained model. However, the semantic information captured by pre-trained models from sentences or passages is usually only applied to document ranking, with limited use in query expansion.

A comprehensive evaluation of large Language models on benchmark biomedical text processing tasks.

Recently, Large Language Models (LLMs) have demonstrated impressive capability to solve a wide range of tasks. However, despite their success across various tasks, no prior work has investigated their capability in the biomedical domain yet. To this end, this paper aims to evaluate the performance of LLMs on benchmark biomedical tasks.

Term dependency extraction using rule-based Bayesian Network for medical image retrieval.

Text-Based Medical Image Retrieval (TBMIR) has been known to be successful in retrieving medical images with textual descriptions. Usually, these descriptions are very brief and cannot express the whole visual content of the image in words, hence negatively affect the retrieval performance. One of the solutions offered in the literature is to form a Bayesian Network thesaurus taking advantage of some medical terms extracted from the image datasets.

Image Translation by Ad CycleGAN for COVID-19 X-Ray Images: A New Approach for Controllable GAN.

We propose a new generative model named adaptive cycle-consistent generative adversarial network, or Ad CycleGAN to perform image translation between normal and COVID-19 positive chest X-ray images. An independent pre-trained criterion is added to the conventional Cycle GAN architecture to exert adaptive control on image translation. The performance of Ad CycleGAN is compared with the Cycle GAN without the external criterion.

GFCNet: Utilizing graph feature collection networks for coronavirus knowledge graph embeddings.

In response to fighting COVID-19 pandemic, researchers in machine learning and artificial intelligence have constructed some medical knowledge graphs (KG) based on existing COVID-19 datasets, however, these KGs contain a considerable amount of semantic relations which are incomplete or missing. In this paper, we focus on the task of knowledge graph embedding (KGE), which serves an important solution to infer the missing relations. In the past, there have been a collection of knowledge graph embedding models with different scoring functions to learn entity and relation embeddings published.

CycleGAN with Dynamic Criterion for Malaria Blood Cell Image Synthetization.

We present a cycle-consistent adversarial network (Cycle GAN) with dynamic criterion to synthesize blood cells parasitized by malaria plasmodia. The result shows 100% of the synthetic images are correctly classified by the pretrained classifier compared to 99.61% of the real images, 76.

Deep generative learning for automated EHR diagnosis of traditional Chinese medicine.

Background: Computer-aided medical decision-making (CAMDM) is the method to utilize massive EMR data as both empirical and evidence support for the decision procedure of healthcare activities. Well-developed information infrastructure, such as hospital information systems and disease surveillance systems, provides abundant data for CAMDM. However, the complexity of EMR data with abstract medical knowledge makes the conventional model incompetent for the analysis.

Fast Screening Technology for Drug Emergency Management: Predicting Suspicious SNPs for ADR with Information Theory-based Models.

Objective: The genetic polymorphism of Cytochrome P450 (CYP 450) is considered as one of the main causes for adverse drug reactions (ADRs). In order to explore the latent correlations between ADRs and potentially corresponding single-nucleotide polymorphism (SNPs) in CYP450, three algorithms based on information theory are used as the main method to predict the possible relation.

Methods: The study uses a retrospective case-control study to explore the potential relation of ADRs to specific genomic locations and single-nucleotide polymorphism (SNP).

DL-ADR: a novel deep learning model for classifying genomic variants into adverse drug reactions.

Background: Genomic variations are associated with the metabolism and the occurrence of adverse reactions of many therapeutic agents. The polymorphisms on over 2000 locations of cytochrome P450 enzymes (CYP) due to many factors such as ethnicity, mutations, and inheritance attribute to the diversity of response and side effects of various drugs. The associations of the single nucleotide polymorphisms (SNPs), the internal pharmacokinetic patterns and the vulnerability of specific adverse reactions become one of the research interests of pharmacogenomics.

Enhancing genomics information retrieval through dimensional analysis.

We propose a novel dimensional analysis approach to employing meta information in order to find the relationships within the unstructured or semi-structured document/passages for improving genomics information retrieval performance. First, we make use of the auxiliary information as three basic dimensions, namely "temporal", "journal", and "author". The reference section is treated as a commensurable quantity of the three basic dimensions.

Modeling and mining term association for improving biomedical information retrieval performance.

Background: The growth of the biomedical information requires most information retrieval systems to provide short and specific answers in response to complex user queries. Semantic information in the form of free text that is structured in a way makes it straightforward for humans to read but more difficult for computers to interpret automatically and search efficiently. One of the reasons is that most traditional information retrieval models assume terms are conditionally independent given a document/passage.

A LDA-based approach to promoting ranking diversity for genomics information retrieval.

Background: In the biomedical domain, there are immense data and tremendous increase of genomics and biomedical relevant publications. The wealth of information has led to an increasing amount of interest in and need for applying information retrieval techniques to access the scientific literature in genomics and related biomedical disciplines. In many cases, the desired information of a query asked by biologists is a list of a certain type of entities covering different aspects that are related to the question, such as cells, genes, diseases, proteins, mutations, etc.

Re-ranking with context for high-performance biomedical information retrieval.

In this paper, we present a context-sensitive approach to re-ranking retrieved documents for further improving the effectiveness of high-performance biomedical literature retrieval systems. For each topic, a two-dimensional positive context is learnt from the top N retrieved documents and a group of negative contexts are learnt from the last N' documents in initial retrieval ranked list. The contextual space contains lexical context and conceptual context.

Promoting ranking diversity for genomics search with relevance-novelty combined model.

Background: In the biomedical domain, the desired information of a question (query) asked by biologists usually is a list of a certain type of entities covering different aspects that are related to the question, such as genes, proteins, diseases, mutations, etc. Hence it is important for a biomedical information retrieval system to be able to provide comprehensive and diverse answers to fulfill biologists' information needs. However, traditional retrieval models assume that the relevance of a document is independent of the relevance of other documents.

A robust approach to optimizing multi-source information for enhancing genomics retrieval performance.

Background: The users desire to be provided short, specific answers to questions and put them in context by linking original sources from the biomedical literature. Through the use of information retrieval technologies, information systems retrieve information to index data based on all kinds of pre-defined searching techniques/functions such that various ranking strategies are designed depending on different sources. In this paper, we propose a robust approach to optimizing multi-source information for improving genomics retrieval performance.