Systematic comparison of ranking aggregation methods for gene lists in experimental results

A common experimental output in biomedical science is a list of genes implicated in a given biological process or disease. The results of a group of studies answering the same, or similar, questions can be combined by meta-analysis to find a consensus or a more reliable answer. Ranking aggregation methods can be used to combine gene lists from various sources in meta-analyses. Evaluating a ranking aggregation method on a specific type of dataset before using it is required to support the reliability of the result since the property of a dataset can influence the performance of an algorithm. Evaluation of aggregation methods is usually based on a simulated database especially for the algorithms designed for gene lists because of the lack of a known truth for real data. However, simulated datasets tend to be too small compared to experimental data and neglect key features, including heterogeneity of quality, relevance and the inclusion of unranked lists. In this study, a group of existing methods and their variations which are suitable for meta-analysis of gene lists are compared using simulated and real data. Simulated data was used to explore the performance of the aggregation methods as a function of emulating the common scenarios of real genomics data, with various heterogeneity of quality, noise level, and a mix of unranked and ranked data using 20000 possible entities. In addition to the evaluation with simulated data, a comparison using real genomic data on the SARS-CoV-2 virus, cancer (NSCLC), and bacteria (macrophage apoptosis) was performed. We summarise our evaluation results in terms of a simple flowchart to select a ranking aggregation method for genomics data.

MOLI: Smart Conversation Agent for Mobile Customer Service

Human agents in technical customer support provide users with instructional answers to solve a task that would otherwise require a lot of time, money, energy, physical costs. Developing a dialogue system in this domain is challenging due to the broad variety of user questions. Moreover, user questions are noisy (for example, spelling mistakes), redundant and have various natural language expressions. In this work, we introduce a conversational system, MOLI (the name of our dialogue system), to solve customer questions by providing instructional answers from a knowledge base. Our approach combines models for question type and intent category classification with slot filling and a back-end knowledge base for filtering and ranking answers, and uses a dialog framework to actively query the user for missing information. For answer-ranking we find that sequential matching networks and neural multi-perspective sentence similarity networks clearly outperform baseline models, achieving a 43% error reduction. The end-to-end P@1(Precision at top 1) of MOLI was 0.69 and the customers’ satisfaction was 0.73.