BACKGROUND: The expression changes of some proteins are associated with cancer progression, and can be used as biomarkers in cancer diagnosis. Automated systems have been frequently applied in the large-scale detection of protein biomarkers and have provided a valuable complement for wet-laboratory experiments. For example, our previous work used an immunohistochemical image-based machine learning classifier of protein subcellular locations to screen biomarker proteins that change locations in colon cancer tissues. The tool could recognize the location of biomarkers but did not consider the effect of protein expression level changes on the screening process. RESULTS: In this study, we built an automated classification model that recognizes protein expression levels in immunohistochemical images, and used the protein expression levels in combination with subcellular locations to screen cancer biomarkers. To minimize the effect of non-informative sections on the immunohistochemical images, we employed the representative image patches as input and applied a Wasserstein distance method to determine the number of patches. For the patches and the whole images, we compared the ability of color features, characteristic curve features, and deep convolutional neural network features to distinguish different levels of protein expression and employed deep learning and conventional classification models. Experimental results showed that the best classifier can achieve an accuracy of 73.72% and an F1-score of 0.6343. In the screening of protein biomarkers, the detection accuracy improved from 63.64 to 95.45% upon the incorporation of the protein expression changes. CONCLUSIONS: Machine learning can distinguish different protein expression levels and speed up their annotation in the future. Combining information on the expression patterns and subcellular locations of protein can improve the accuracy of automatic cancer biomarker screening. This work could be useful in discovering new cancer biomarkers for clinical diagnosis and research.

BACKGROUND: Protein biomarkers play important roles in cancer diagnosis. Many efforts have been made on measuring abnormal expression intensity in biological samples to identity cancer types and stages. However, the change of subcellular location of proteins, which is also critical for understanding and detecting diseases, has been rarely studied. RESULTS: In this work, we developed a machine learning model to classify protein subcellular locations based on immunohistochemistry images of human colon tissues, and validated the ability of the model to detect subcellular location changes of biomarker proteins related to colon cancer. The model uses representative image patches as inputs, and integrates feature engineering and deep learning methods. It achieves 92.69% accuracy in classification of new proteins. Two validation datasets of colon cancer biomarkers derived from published literatures and the human protein atlas database respectively are employed. It turns out that 81.82 and 65.66% of the biomarker proteins can be identified to change locations. CONCLUSIONS: Our results demonstrate that using image patches and combining predefined and deep features can improve the performance of protein subcellular localization, and our model can effectively detect biomarkers based on protein subcellular translocations. This study is anticipated to be useful in annotating unknown subcellular localization for proteins and discovering new potential location biomarkers.

Collaborative tagging systems are now deployed extensivelyto help users share and organize resources.Tag prediction and recommendation systems generallymodel user behavior as research has shown that accuracycan be significantly improved by modeling users’preferences. However, these preferences are usuallytreated as constant over time, neglecting the temporalfactor within users’ interests. On the other hand, littleis known about how this factor may influence predictionin social bookmarking systems. In this paper, weinvestigate the temporal dynamics of user interests intagging systems and propose a user-tag-specific temporalinterests model for tracking users’ interests overtime. Additionally, we analyze the phenomenon of topicswitches in social bookmarking systems, showing that atemporal interests model can benefit from the integrationof topic switch detection and that temporal characteristicsof social tagging systems are different fromtraditional concept drift problems. We conduct experimentson three public datasets, demonstrating the importanceof personalization and user-tag specializationin tagging systems. Experimental results show that ourmethod can outperform state-of-the-art tag predictionalgorithms. We also incorporate our model within existingcontent-based methods yielding significant improvementsin performance.

A taxonomy organizes concepts or topics in a hierarchical structure and can be created manually or via automated systems. A major drawback of taxonomies is that they require users to have the same view of the topics as the taxonomy creator. Users who do not share that mental taxonomy are likely to have difficulty in finding the desired topic. In this paper, we propose a new approach to taxonomy expansion which is able to provide more flexible views. Based on an existing taxonomy, our algorithm finds possible alternative paths and generates an expanded taxonomy with flexibility in user browsing choices. In experiments on the dmoz Open Directory Project, the rebuilt taxonomies provide more alternative paths and shorter paths to information. User studies show that our expanded taxonomies are preferred compared to the original

Social tagging systems have become increasingly popular for sharing and organizing web resources. Tag prediction is a common feature of social tagging systems. Social tagging by nature is an incremental process, meaning that once a user has saved a web page with tags, the tagging system can provide more accurate predictions for the user, based on user's incremental behaviors. However, existing tag prediction methods do not consider this important factor, in which their training and test datasets are either split by a fixed time stamp or randomly sampled from a larger corpus. In our temporal experiments, we perform a time-sensitive sampling on an existing public dataset, resulting in a new scenario which is much closer to "real-world".