Active semi-supervised framework with data editing

In order to address the insufficient training data problem, many active semi-supervised algorithms have been proposed. The self-labeled training data in semi-supervised learning may contain much noise due to the insufficient training data. Such noise may snowball themselves in the following learning process and thus hurt the generalization ability of the final hypothesis. Extremely few labeled training data in sparsely labeled text classification aggravate such situation. If such noise could be identified and removed by some strategy, the performance of the active semi-supervised algorithms should be improved. However, such useful techniques of identifying and removing noise have been seldom explored in existing active semi-supervised algorithms. In this paper, we propose an active semi-supervised framework with data editing (we call it ASSDE) to improve sparsely labeled text classification. A data editing technique is used to identify and remove noise introduced by semi-supervised labeling. We carry out the data editing technique by fully utilizing the advantage of active learning, which is novel according to our knowledge. The fusion of active learning with data editing makes ASSDE more robust to the sparsity and the distribution bias of the training data. It further simplifies the design of semi-supervised learning which makes ASSDE more efficient. Extensive experimental study on several real-world text data sets shows the encouraging results of the proposed framework for sparsely labeled text classification, compared with several state-of-the-art methods.Computer Science, Information SystemsComputer Science, Software EngineeringSCI(E)0ARTICLE4,SI1513-1532

DeepHE: Accurately predicting human essential genes based on deep learning.

Accurately predicting essential genes using computational methods can greatly reduce the effort in finding them via wet experiments at both time and resource scales, and further accelerate the process of drug discovery. Several computational methods have been proposed for predicting essential genes in model organisms by integrating multiple biological data sources either via centrality measures or machine learning based methods. However, the methods aiming to predict human essential genes are still limited and the performance still need improve. In addition, most of the machine learning based essential gene prediction methods are lack of skills to handle the imbalanced learning issue inherent in the essential gene prediction problem, which might be one factor affecting their performance. We propose a deep learning based method, DeepHE, to predict human essential genes by integrating features derived from sequence data and protein-protein interaction (PPI) network. A deep learning based network embedding method is utilized to automatically learn features from PPI network. In addition, 89 sequence features were derived from DNA sequence and protein sequence for each gene. These two types of features are integrated to train a multilayer neural network. A cost-sensitive technique is used to address the imbalanced learning problem when training the deep neural network. The experimental results for predicting human essential genes show that our proposed method, DeepHE, can accurately predict human gene essentiality with an average performance of AUC higher than 94%, the area under precision-recall curve (AP) higher than 90%, and the accuracy higher than 90%. We also compare DeepHE with several widely used traditional machine learning models (SVM, Naïve Bayes, Random Forest, and Adaboost) using the same features and utilizing the same cost-sensitive technique to against the imbalanced learning issue. The experimental results show that DeepHE significantly outperforms the compared machine learning models. We have demonstrated that human essential genes can be accurately predicted by designing effective machine learning algorithm and integrating representative features captured from available biological data. The proposed deep learning framework is effective for such task

Predicting essential proteins by integrating orthology, gene expressions, and PPI networks

<div><p>Identifying essential proteins is very important for understanding the minimal requirements of cellular life and finding human disease genes as well as potential drug targets. Experimental methods for identifying essential proteins are often costly, time-consuming, and laborious. Many computational methods for such task have been proposed based on the topological properties of protein-protein interaction networks (PINs). However, most of these methods have limited prediction accuracy due to the noisy and incomplete natures of PINs and the fact that protein essentiality may relate to multiple biological factors. In this work, we proposed a new centrality measure, OGN, by integrating orthologous information, gene expressions, and PINs together. OGN determines a protein’s essentiality by capturing its co-clustering and co-expression properties, as well as its conservation in the evolution process. The performance of OGN was tested on the species of <i>Saccharomyces cerevisiae</i>. Compared with several published centrality measures, OGN achieves higher prediction accuracy in both working alone and ensemble.</p></div

Precision-recall curves of OGN with different α.

<p>Precision-recall curves of OGN with different α.</p

The number of true essential proteins identified by OGN with different α.

<p>The number of true essential proteins identified by OGN with different α.</p

The protein interaction network for the top 100 selected proteins by OGN (alpha = 0.3).

<p>The protein interaction network for the top 100 selected proteins by OGN (alpha = 0.3).</p

Performance of ensemble method with different top <i>n</i> and threshold <i>T</i>.

<p>Performance of ensemble method with different top <i>n</i> and threshold <i>T</i>.</p

Comparison of OGN, CoEWC, SON, LBCC, and five common used centrality measures (BC, CC, DC, EC, and SC) using Jackknife method.

<p>Comparison of OGN, CoEWC, SON, LBCC, and five common used centrality measures (BC, CC, DC, EC, and SC) using Jackknife method.</p

The number of essential proteins predicted by OGN, BC, CC, DC, EC, SC, CoEWC, SON, and LBCC.

<p>(a)-(f) show the results of these methods when select top 100 to 600 ranked proteins as candidate essential proteins.</p