Learn More
In this paper we tackle the problem of recommendation in the scenarios with binary relevance data, when only a few (k) items are recommended to individual users. Past work on Collaborative Filtering (CF) has either not addressed the ranking problem for binary relevance datasets, or not specifically focused on improving top-k recommendations. To solve the(More)
A ranking approach, ListRank-MF, is proposed for collaborative filtering that combines a list-wise learning-to-rank algorithm with matrix factorization (MF). A ranked list of items is obtained by minimizing a loss function that represents the uncertainty between training lists and output lists produced by a MF ranking model. ListRank-MF enjoys the advantage(More)
N(6)-methyladenosine (m(6)A) is the most prevalent internal modification of messenger RNA (mRNA) in higher eukaryotes. Here we report ALKBH5 as another mammalian demethylase that oxidatively reverses m(6)A in mRNA in vitro and in vivo. This demethylation activity of ALKBH5 significantly affects mRNA export and RNA metabolism as well as the assembly of mRNA(More)
In this paper, we tackle the problem of top-N context-aware recommendation for implicit feedback scenarios. We frame this challenge as a ranking problem in collaborative filtering (CF). Much of the past work on CF has not focused on evaluation metrics that lead to good top-N recommendation lists in designing recommendation models. In addition, previous work(More)
The methyltransferase like 3 (METTL3)-containing methyltransferase complex catalyzes the N6-methyladenosine (m6A) formation, a novel epitranscriptomic marker; however, the nature of this complex remains largely unknown. Here we report two new components of the human m6A methyltransferase complex, Wilms' tumor 1-associating protein (WTAP) and(More)
Over the past two decades, a large amount of research effort has been devoted to developing algorithms that generate recommendations. The resulting research progress has established the importance of the user-item (U-I) matrix, which encodes the individual preferences of users for items in a collection, for recommender systems. The U-I matrix provides the(More)
The role of Fat Mass and Obesity-associated protein (FTO) and its substrate N6-methyladenosine (m6A) in mRNA processing and adipogenesis remains largely unknown. We show that FTO expression and m6A levels are inversely correlated during adipogenesis. FTO depletion blocks differentiation and only catalytically active FTO restores adipogenesis. Transcriptome(More)
The interactions between the bone marrow (BM) microenvironment and acute myeloid leukemia (AML) is known to promote survival of AML cells. In this study, we used reverse phase-protein array (RPPA) technology to measure changes in multiple proteins induced by stroma in leukemic cells. We then investigated the potential of an mTOR kinase inhibitor, PP242, to(More)
Context-aware recommendation seeks to improve recommendation performance by exploiting various information sources in addition to the conventional user-item matrix used by recommender systems. Recommendations should also usually strive to satisfy a specific purpose. Within the Moviepilot mood track of the context-aware movie recommendation challenge, we(More)
We present a novel rotation based method for detecting where a mobile device is worn on a user's body that utilizes the fusion of the data from accelerometer and gyroscope. Detecting the position of a mobile device could improve the performance of on-body sensor based human activity recognition and the adaptability of many mobile applications. In our(More)