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- Anelli, V. W., et al.
(author)
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RecSys 2021 challenge workshop : Fairness-aware engagement prediction at scale on Twiter's Home Timeline
- 2021
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In: RecSys 2021 - 15th ACM Conference on Recommender Systems. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450384582 ; , s. 819-824
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Conference paper (peer-reviewed)abstract
- The workshop features presentations of accepted contributions to the RecSys Challenge 2021, organized by Politecnico di Bari, ETH Zürich, Jönköping University, and the data set is provided by Twitter. The challenge focuses on a real-world task of tweet engagement prediction in a dynamic environment. For 2021, the challenge considers four different engagement types: Likes, Retweet, Quote, and replies. This year's challenge brings the problem even closer to Twitter's real recommender systems by introducing latency constraints. We also increases the data size to encourage novel methods. Also, the data density is increased in terms of the graph where users are considered to be nodes and interactions as edges. The goal is twofold: to predict the probability of different engagement types of a target user for a set of Tweets based on heterogeneous input data while providing fair recommendations. In fact, multi-goal optimization considering accuracy and fairness is particularly challenging. However, we believed that the recommendation community was nowadays mature enough to face the challenge of providing accurate and, at the same time, fair recommendations. To this end, Twitter has released a public dataset of close to 1 billion data points, > 40 million each day over 28 days. Week 1-3 will be used for training and week 4 for evaluation and testing. Each datapoint contains the tweet along with engagement features, user features, and tweet features. A peculiarity of this challenge is related to keeping the dataset updated with the platform: if a user deletes a Tweet, or their data from Twitter, the dataset is promptly updated. Moreover, each change in the dataset implied new evaluations of all submissions and the update of the leaderboard metrics. The challenge was well received with 578 registered users, and 386 submissions.
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| 2. |
- Egyud, M., et al.
(author)
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Detection of Circulating Tumor DNA in Plasma: A Potential Biomarker for Esophageal Adenocarcinoma
- 2019
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In: Annals of Thoracic Surgery. - : Elsevier BV. - 0003-4975. ; 108:2, s. 343-349
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Journal article (peer-reviewed)abstract
- Background. Recent literature has demonstrated the potential of "liquid biopsy" and detection of circulating tumor (ct)DNA as a cancer biomarker. However, to date there is a lack of data specific to esophageal adenocarcinoma (EAC). This study was conducted to determine how detection and quantification of ctDNA changes with disease burden in patients with EAC and evaluate its potential as a biomarker in this population. Methods. Blood samples were obtained from patients with stage I to IV EAC. Longitudinal blood samples were collected from a subset of patients. Imaging studies and pathology reports were reviewed to determine disease course. Tumor samples were sequenced to identify mutations. Mutations in plasma DNA were detected using custom, barcoded, patient-specific sequencing libraries. Mutations in plasma were quantified, and associations with disease stage and response to therapy were explored. Results. Plasma samples from a final cohort of 38 patients were evaluated. Baseline plasma samples were ctDNA positive for 18 patients (47%) overall, with tumor allele frequencies ranging from 0.05% to 5.30%. Detection frequency of ctDNA and quantity of ctDNA increased with stage. Data from longitudinal samples indicate that ctDNA levels correlate with and precede evidence of response to therapy or recurrence. Conclusions. ctDNA can be detected in plasma of EAC patients and correlates with disease burden. Detection of ctDNA in early-stage EAC is challenging and may limit diagnostic applications. However, our data demonstrate the potential of ctDNA as a dynamic biomarker to monitor treatment response and disease recurrence in patients with EAC. (C) 2019 by The Society of Thoracic Surgeons
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