Ordinary least-squares estimation is proved to be the best linear unbiased estimator according to the Gauss-Markov theorem. In the last two decades, however, some researchers criticized that least-squares was substantially inaccurate in fitting power-law distributions; such criticism has caused a strong bias in research community. In this paper, we conduct extensive experiments to rebut that such criticism is complete nonsense. Specifically, we sample different sizes of discrete and continuous data from power-law models, showing that even though the long-tailed noises are sampled from power-law models, they cannot be treated as power-law data. We define the correct way to bin continuous power-law data into data points and propose an average strategy for least-squares to fit power-law distributions. Experiments on both simulated and real-world data show that our proposed method fits power-law data perfectly. We uncover a fundamental flaw in the popular method proposed by Clauset et al. [12]: it tends to discard the majority of power-law data and fit the long-tailed noises. Experiments also show that the reverse cumulative distribution function is a bad idea to plot power-law data in practice because it usually hides the true probability distribution of data. We hope that our research can clean up the bias about least-squares fitting power-law distributions.

BACKGROUND: Protein-protein interaction (PPI) prediction is an important task towards the understanding of many bioinformatics functions and applications, such as predicting protein functions, gene-disease associations and disease-drug associations. However, many previous PPI prediction researches do not consider missing and spurious interactions inherent in PPI networks. To address these two issues, we define two corresponding tasks, namely missing PPI prediction and spurious PPI prediction, and propose a method that employs graph embeddings that learn vector representations from constructed Gene Ontology Annotation (GOA) graphs and then use embedded vectors to achieve the two tasks. Our method leverages on information from both term-term relations among GO terms and term-protein annotations between GO terms and proteins, and preserves properties of both local and global structural information of the GO annotation graph. RESULTS: We compare our method with those methods that are based on information content (IC) and one method that is based on word embeddings, with experiments on three PPI datasets from STRING database. Experimental results demonstrate that our method is more effective than those compared methods. CONCLUSION: Our experimental results demonstrate the effectiveness of using graph embeddings to learn vector representations from undirected GOA graphs for our defined missing and spurious PPI tasks.

Protein-protein interaction (PPI) prediction is a key step towards many bioinformatics applications including prediction of protein functions and drug-disease interactions. However, previous research on PPI prediction rarely considered missing and spurious interactions in PPI networks. To address these two issues, we define two corresponding tasks, namely missing PPI prediction and spurious PPI prediction, and propose a novel method that employs graph embeddings to learn vector representations from constructed Gene Ontology (GO) annotation graphs. Our method leverages the information from both term-term relations among GO terms and term-protein annotations between GO terms and proteins, and preserves properties of both local and global structural information of the GO annotation graph. We compare our method with methods based on information content and on word embeddings, using three PPI datasets from STRING database. Experimental results demonstrate that our method is more effective than those compared methods.

We find from four datasets that time expressions are formed by loose structure and the words used to express time information can differentiate time expressions from common text. The findings drive us to design a learning method named TOMN to model time expressions. TOMN defines a time-related tagging scheme named TOMN scheme with four tags, namely \tomnT,\tomnO, \tomnM,and \tomnN, indicating the constituents of time expression, namely \tomnT ime token, \tomnM odifier, \tomnN umeral, and the words \tomnO utside time expression. In modeling, TOMN assigns a word with a TOMN tag under conditional random fields with minimal features. Essentially, our constituent-based TOMN scheme overcomes the problem of inconsistent tag assignment that is caused by the conventional position-based tagging schemes (\eg BIO scheme and BILOU scheme). Experiments show that TOMN is equally or more effective than state-of-the-art methods on various datasets, and much more robust on cross-datasets. Moreover, our analysis can explain many empirical observations in other works about time expression recognition and named entity recognition.

Extracting time expressions from free text is a fundamental task for many applications.We analyze time expressions from four different datasets and find that only a small group of words are used to express time information and that the words in time expressions demonstrate similar syntactic behaviour.Based on the findings, we propose a type-based approach named SynTime 1 for time expression recognition.Specifically, we define three main syntactic token types, namely time token, modifier, and numeral, to group time-related token regular expressions.On the types we design general heuristic rules to recognize time expressions.In recognition, SynTime first identifies time tokens from raw text, then searches their surroundings for modifiers and numerals to form time segments, and finally merges the time segments to time expressions.As a lightweight rule-based tagger, SynTime runs in real time, and can be easily expanded by simply adding keywords for the text from different domains and different text types.Experiments on benchmark datasets and tweets data show that SynTime outperforms state-of-the-art methods.