Inferring Protein Sequence-Function Relationships with Large-Scale Positive-Unlabeled Learning
نویسندگان
چکیده
منابع مشابه
Sequence Prediction with Unlabeled Data by Reward Function Learning
Reinforcement learning (RL), which has been successfully applied to sequence prediction, introduces reward as sequence-level supervision signal to evaluate the quality of a generated sequence. Existing RL approaches use the ground-truth sequence to define reward, which limits the application of RL techniques to labeled data. Since labeled data is usually scarce and/or costly to collect, it is d...
متن کاملMulti-Positive and Unlabeled Learning
Yixing Xu†, Chang Xu‡, Chao Xu†, Dacheng Tao‡ †Key Laboratory of Machine Perception (MOE), Cooperative Medianet Innovation Center, School of Electronics Engineering and Computer Science, PKU, Beijing 100871, China ‡UBTech Sydney AI Institute, The School of Information Technologies, The University of Sydney, J12, 1 Cleveland St, Darlington, NSW 2008, Australia [email protected], [email protected]...
متن کاملA fast, large-scale learning method for protein sequence classification
Motivation: Establishing structural and functional relationships between sequences in the presence of only the primary sequence information is a key task in biological sequence analysis. This ability can be critical for tasks such as making inferences of the structural class of unannotated proteins when no secondary or tertiary structure is available. Recent computational methods based on profi...
متن کاملGOLabeler: Improving Sequence-based Large-scale Protein Function Prediction by Learning to Rank.
Motivation Gene Ontology (GO) has been widely used to annotate functions of proteins and understand their biological roles. Currently only <1% of more than 70 million proteins in UniProtKB have experimental GO annotations, implying the strong necessity of automated function prediction (AFP) of proteins, where AFP is a hard multilabel classification problem due to one protein with a diverse numb...
متن کاملTheoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning
In PU learning, a binary classifier is trained from positive (P) and unlabeled (U) data without negative (N) data. Although N data is missing, it sometimes outperforms PN learning (i.e., ordinary supervised learning). Hitherto, neither theoretical nor experimental analysis has been given to explain this phenomenon. In this paper, we theoretically compare PU (and NU) learning against PN learning...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Cell Systems
سال: 2021
ISSN: 2405-4712
DOI: 10.1016/j.cels.2020.10.007