Bioinformatics and biomedical informatics with ChatGPT:Year one review

The year 2023 marked a significant surge in the exploration of applying large language model chatbots,notably Chat Generative Pre-trained Transformer(ChatGPT),across various disciplines.We surveyed the application of ChatGPT in bioinformatics and biomedical informatics throughout the year,covering omics,genetics,biomedical text mining,drug discovery,biomedical image understanding,bioinformatics programming,and bioinformatics education.Our survey delineates the current strengths and limitations of this chatbot in bioinformatics and offers insights into potential avenues for future developments.

The Theory of Biomedical Knowledge Integration (Ⅶ)──The Non-Euclid Macro-Micro Transform Law

This article continued to do the scholastic pursuits on some profound mechanisms in the life systems, which are believed to be related to the further development of Medical Informatics. It discussed at first the structural nature of things, then probed a principle which is a basis for both of the fractal theory and the wavelet analysis, being called the shape-constancy law of the basic constructors at the different scale levels. And the paper also ventured the equivalency between the shape of wave and matrix, thus presented a new concept "shaped-number", being expected to work in the operations of some bio-medical functions or shapes.

Sequence-Based Predicting Bacterial Essential ncRNAs Algorithm by Machine Learning

Essential ncRNA is a type of ncRNAwhich is indispensable for the sur-vival of organisms.Although essential ncRNAs cannot encode proteins,they are as important as essential coding genes in biology.They have got wide variety of applications such as antimicrobial target discovery,minimal genome construction and evolution analysis.At present,the number of species required for the deter-mination of essential ncRNAs in the whole genome scale is still very few due to the traditional methods are time-consuming,laborious and costly.In addition,tra-ditional experimental methods are limited by the organisms as less than 1%of bacteria can be cultured in the laboratory.Therefore,it is important and necessary to develop theories and methods for the recognition of essential non-coding RNA.In this paper,we present a novel method for predicting essential ncRNA by using both compositional and derivative features calculated by information theory of ncRNA sequences.The method was developed with Support Vector Machine(SVM).The accuracy of the method was evaluated through cross-species cross-vali-dation and found to be between 0.69 and 0.81.It shows that the features we selected have good performance for the prediction of essential ncRNA using SVM.Thus,the method can be applied for discovering essential ncRNAs in bacteria.

Translation in Data Mining to Advance Personalized Medicine for Health Equity

Personalized medicine is the development of “tailored” therapies that reflect traditional medical approaches with the incorporation of the patient’s unique genetic profile and the environmental basis of the disease. These individualized strategies encompass disease prevention and diagnosis, as well as treatment strategies. Today’s healthcare workforce is faced with the availability of massive amounts of patient- and disease-related data. When mined effectively, these data will help produce more efficient and effective diagnoses and treatment, leading to better prognoses for patients at both the individual and population level. Designing preventive and therapeutic interventions for those patients who will benefit most while minimizing side effects and controlling healthcare costs requires bringing diverse data sources together in an analytic paradigm. A resource to clinicians in the development and application of personalized medicine is largely facilitated, perhaps even driven, by the analysis of “big data”. For example, the availability of clinical data warehouses is a significant resource for clinicians in practicing personalized medicine. These “big data” repositories can be queried by clinicians, using specific questions, with data used to gain an understanding of challenges in patient care and treatment. Health informaticians are critical partners to data analytics including the use of technological infrastructures and predictive data mining strategies to access data from multiple sources, assisting clinicians’ interpretation of data and development of personalized, targeted therapy recommendations. In this paper, we look at the concept of personalized medicine, offering perspectives in four important, influencing topics: 1) the availability of “big data” and the role of biomedical informatics in personalized medicine, 2) the need for interdisciplinary teams in the development and evaluation of personalized therapeutic approaches, and 3) the impact of electronic medical record systems and clinical data warehouses on the field of personalized medicine. In closing, we present our fourth perspective, an overview to some of the ethical concerns related to personalized medicine and health equity.

Deep Learning and Its Applications in Biomedicine

Advances in biological and medical technologies have been providing us explosive vol- umes of biological and physiological data, such as medical images, electroencephalography, geno- mic and protein sequences. Learning from these data facilitates the understanding of human health and disease. Developed from artificial neural networks, deep learning-based algorithms show great promise in extracting features and learning patterns from complex data. The aim of this paper is to provide an overview of deep learning techniques and some of the state-of-the-art applications in the biomedical field. We first introduce the development of artificial neural network and deep learning. We then describe two main components of deep learning, i.e., deep learning architectures and model optimization. Subsequently, some examples are demonstrated for deep learning