Rank-Similarity Measures for Comparing Gene Prioritizations: A Case Study in Autism

We discuss the challenge of comparing three gene prioritization methods: network propagation, integer linear programming rank aggregation (RA), and statistical RA. These methods are based on different biological categories and estimate disease?gene association. Previously proposed comparison schemes are based on three measures of performance: receiver operating curve, area under the curve, and median rank ratio. Although they may capture important aspects of gene prioritization performance, they may fail to capture important differences in the rankings of individual genes. We suggest that comparison schemes could be improved by also considering recently proposed measures of similarity between gene rankings. We tested this suggestion on comparison schemes for prioritizations of genes associated with autism that were obtained using brain- and tissue-specific data. Our results show the effectiveness of our measures of similarity in clustering brain regions based on their relevance to autism

Lego-MT: Towards Detachable Models in Massively Multilingual Machine Translation

Multilingual neural machine translation (MNMT) aims to build a unified model for many language directions. Existing monolithic models for MNMT encounter two challenges: parameter interference among languages and inefficient inference for large models. In this paper, we revisit the classic multi-way structures and develop a detachable model by assigning each language (or group of languages) to an individual branch that supports plug-and-play training and inference. To address the needs of learning representations for all languages in a unified space, we propose a novel efficient training recipe, upon which we build an effective detachable model, Lego-MT. For a fair comparison, we collect data from OPUS and build a translation benchmark covering 433 languages and 1.3B parallel data. Experiments show that Lego-MT with 1.2B parameters brings an average gain of 3.2 spBLEU. It even outperforms M2M-100 with 12B parameters. The proposed training recipe brings a 28.2$\times$ speedup over the conventional multi-way training method.\footnote{ \url{https://github.com/CONE-MT/Lego-MT}.}Comment: ACL 2023 Finding

KELM: Knowledge Enhanced Pre-Trained Language Representations with Message Passing on Hierarchical Relational Graphs

Incorporating factual knowledge into pre-trained language models (PLM) such as BERT is an emerging trend in recent NLP studies. However, most of the existing methods combine the external knowledge integration module with a modified pre-training loss and re-implement the pre-training process on the large-scale corpus. Re-pretraining these models is usually resource-consuming, and difficult to adapt to another domain with a different knowledge graph (KG). Besides, those works either cannot embed knowledge context dynamically according to textual context or struggle with the knowledge ambiguity issue. In this paper, we propose a novel knowledge-aware language model framework based on fine-tuning process, which equips PLM with a unified knowledge-enhanced text graph that contains both text and multi-relational sub-graphs extracted from KG. We design a hierarchical relational-graph-based message passing mechanism, which can allow the representations of injected KG and text to mutually update each other and can dynamically select ambiguous mentioned entities that share the same text. Our empirical results show that our model can efficiently incorporate world knowledge from KGs into existing language models such as BERT, and achieve significant improvement on the machine reading comprehension (MRC) task compared with other knowledge-enhanced models.Comment: ICLR 2022 on DLG4NL

TOB1 Deficiency Enhances the Effect of Bone Marrow-Derived Mesenchymal Stem Cells on Tendon-Bone Healing in a Rat Rotator Cuff Repair Model

Background/Aims: This study investigated the effect of silencing TOB1 (Transducer of ERBB2, 1) expression in bone marrow-derived mesenchymal stem cells (MSCs) on MSC-facilitated tendon-bone healing in a rat supraspinatus repair model. Methods: Rat MSCs were transduced with a recombinant lentivirus encoding short hairpin RNA (shRNA) against TOB1. MSC cell proliferation was analyzed by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assays. The effect of MSCs with TOB1 deficiency on tendon-bone healing in a rat rotator cuff repair model was evaluated by biomechanical testing, histological analysis and collagen type I and II gene expression. An upstream regulator (miR-218) of TOB1 was determined in MSCs. Results: We found that knockdown of TOB1 significantly increased the proliferative activity of rat MSCs in vitro. When MSCs with TOB1 deficiency were injected into injured rat supraspinatus tendon-bone junctions, the effect on tendon-bone healing was enhanced compared to treatment with control MSCs with normal TOB1 expression, as evidenced by elevated levels of ultimate load to failure and stiffness, increased amount of fibrocartilage and augmented expression of collagen type I and type II genes. In addition, we found that the TOB1 3′ untranslated region is a direct target of miR-218. Similar to the effect of TOB1 deficiency, overexpression of miR-218 effectively promoted tendon-bone healing in rat. Conclusion: These results suggest that TOB1 may play a negative role in the effect of MSCs on tendon-bone healing, and imply that expression of TOB1 may be regulated by miR-218