OBJECTIVE: This study aims to review the recent advances in community challenges for biomedical text mining in China. METHODS: We collected information of evaluation tasks released in community challenges of biomedical text mining, including task description, dataset description, data source, task type and related links. A systematic summary and comparative analysis were conducted on various biomedical natural language processing tasks, such as named entity recognition, entity normalization, attribute extraction, relation extraction, event extraction, text classification, text similarity, knowledge graph construction, question answering, text generation, and large language model evaluation. RESULTS: We identified 39 evaluation tasks from 6 community challenges that spanned from 2017 to 2023. Our analysis revealed the diverse range of evaluation task types and data sources in biomedical text mining. We explored the potential clinical applications of these community challenge tasks from a translational biomedical informatics perspective. We compared with their English counterparts, and discussed the contributions, limitations, lessons and guidelines of these community challenges, while highlighting future directions in the era of large language models. CONCLUSION: Community challenge evaluation competitions have played a crucial role in promoting technology innovation and fostering interdisciplinary collaboration in the field of biomedical text mining. These challenges provide valuable platforms for researchers to develop state-of-the-art solutions.

The Long Branch attraction (LBA) artefact induced by fast evolving Urochordata had hindered the interpretation of relationships among 3 subphyla of Chordata. Although Olfactores hypothesis which placed Urochordata rather than Cephalochordata as the closest relatives to Craniata was gradually accepted, every step of phylogenetic reconstruction had to be treated prudential to minimize LBA phenomenon. MiRNAs (microRNAs) are well known for their 1) adherence to organism development, 2) high conservation, and 3) rarity of secondary loss, parallel evolution, and convergence among metazoan. Therefore we suppose miRNAs to be promising candidates to dispel LBA phenomenon. We performed a phylogenetic study upon 35 pre-miRNA datasets and reconstruct Chordata phylogeny which supported Olfactores hypothesis in a more toilless way by applying fewer datasets and unspecified substitution model. This is the first attempt to apply miRNA sequences in interpreting Chordata phylogeny, and we reckon miRNAs as promising phylogenetic markers for illuminating deuterostome evolution.

OBJECTIVE: This paper presents Lancet, a supervised machine-learning system that automatically extracts medication events consisting of medication names and information pertaining to their prescribed use (dosage, mode, frequency, duration and reason) from lists or narrative text in medical discharge summaries. DESIGN: Lancet incorporates three supervised machine-learning models: a conditional random fields model for tagging individual medication names and associated fields, an AdaBoost model with decision stump algorithm for determining which medication names and fields belong to a single medication event, and a support vector machines disambiguation model for identifying the context style (narrative or list). MEASUREMENTS: The authors, from the University of Wisconsin-Milwaukee, participated in the third i2b2 shared-task for challenges in natural language processing for clinical data: medication extraction challenge. With the performance metrics provided by the i2b2 challenge, the micro F1 (precision/recall) scores are reported for both the horizontal and vertical level. RESULTS: Among the top 10 teams, Lancet achieved the highest precision at 90.4% with an overall F1 score of 76.4% (horizontal system level with exact match), a gain of 11.2% and 12%, respectively, compared with the rule-based baseline system jMerki. By combining the two systems, the hybrid system further increased the F1 score by 3.4% from 76.4% to 79.0%. CONCLUSIONS: Supervised machine-learning systems with minimal external knowledge resources can achieve a high precision with a competitive overall F1 score.Lancet based on this learning framework does not rely on expensive manually curated rules. The system is available online at http://code.google.com/p/lancet/.

The team at the University of Wisconsin-Milwaukee developed an information retrieval and machine learning framework. Our framework requires only the standardized training data and depends upon minimal external knowledge resources and minimal parsing. Within the framework, we built our text mining systems and participated for the first time in all three BioCreative II.5 Challenge tasks. The results show that our systems performed among the top five teams for raw F1 scores in all three tasks and came in third place for the homonym ortholog F1 scores for the INT task. The results demonstrated that our IR-based framework is efficient, robust, and potentially scalable.

As the Human Genome Project and other genome projects experience remarkable success and a flood of biological data is produced by means of high-throughout sequencing techniques, detection of horizontal gene transfer (HGT) becomes a promising field in bioinformatics. This review describes two freeware programs: T-REX for MS Windows and RHOM for Linux. T-REX is a graphical user interface program that offers functions to reconstruct the HGT network among the donor and receptor hosts from the gene and species distance matrices. RHOM is a set of command-line driven programs used to detect HGT in genomes. While T-REX impresses with a user-friendly interface and drawing of the reticulation network, the strength of RHOM is an extensive statistical framework of genome and the graphical display of the estimated sequence position probabilities for the candidate horizontally transferred genes.

The authors focus on the investigation of maximum applicable force/torque by multiple robot arms in coordinated motion, i.e., the dynamic workspace analysis of multiple cooperating robot arms. Simple procedures to solve the dynamic workspace analysis problem for multiple cooperating robot arms with or without the internal force/torque constraint are presented. These procedures model the cooperating robot arms as a closed mechanical chain and are based on the theory of linear transformations and the properties of mechanics. For low degree-of-freedom robot arms, the maximum force/torque problem can be solved by geometrically using the proposed procedures, as illustrated by examples.>

A nonlinear feedback robot controller that incorporates the robot manipulator dynamics and the robot joint motor dynamics is proposed. The manipulator dynamics and the motor dynamics are coupled to obtain a third-order-dynamic model, and differential geometry control theory is applied to produce a linearized and decoupled robot controller. The derived robot controller operates in the robot task space, thus eliminating the need for decomposition of motion commands into robot joint space commands. Computer simulations are performed to verify the feasibility of the proposed robot controller. The controller is further experimentally evaluated on the PUMA 560 robot arm. The experiments show that the proposed controller produces good trajectory tracking performances and is robust in the presence of model inaccuracies. Compared with a nonlinear feedback robot controller based on the manipulator dynamics only, the proposed robot controller yields conspicuously improved performance.>

The nonlinear feedback method of robot control has been experimentally implemented on two PUMA 560 robot arms. The feasibility of the proposed controller, which is already known to be viable, is stressed. The servomechanism operates in task space, and the nonlinear feedback takes care of the necessary transformations to compute the necessary joint currents. A discussion is presented of the implementation with details of the experiments performed. The performance of the controller is encouraging but was limited to 100 Hz sampling frequency and to derived velocity information at the time of the experimentation. The setup of the lab, the software aspects, results, and the control hardware architecture that has recently been implemented are discussed.>