Comparison of Surgical Invasiveness Between Microdiscectomy and 3 Different Endoscopic Discectomy Techniques for Lumbar Disc Herniation.

Objective: Muscle injury is inevitable during surgical exposure of the spine. This study compared paraspinal muscle injury after 4 surgical techniques: microdiscectomy (MD), percutaneous endoscopic lumbar discectomy (PELD), percutaneous endoscopic interlaminar discectomy (PEID), unilateral biportal endoscopic discectomy (UBED).

Methods: Eighty patients who underwent MD, PELD, PEID, and UBED were prospectively observed.

Application of gap-constraints given sequential frequent pattern mining for protein function prediction.

Objectives: Predicting protein function from the protein-protein interaction network is challenging due to its complexity and huge scale of protein interaction process along with inconsistent pattern. Previously proposed methods such as neighbor counting, network analysis, and graph pattern mining has predicted functions by calculating the rules and probability of patterns inside network. Although these methods have shown good prediction, difficulty still exists in searching several functions that are exceptional from simple rules and patterns as a result of not considering the inconsistent aspect of the interaction network.

Incorporating domain knowledge in chemical and biomedical named entity recognition with word representations.

Background: Chemical and biomedical Named Entity Recognition (NER) is an essential prerequisite task before effective text mining can begin for biochemical-text data. Exploiting unlabeled text data to leverage system performance has been an active and challenging research topic in text mining due to the recent growth in the amount of biomedical literature. We present a semi-supervised learning method that efficiently exploits unlabeled data in order to incorporate domain knowledge into a named entity recognition model and to leverage system performance.