Uniqueness of one-parameter exponential curves fitted by non-linear least-squares to non-negative data in monotone non-increasing blocks
Sign Up to like & getrecommendations! Published in 2019 at "Communications in Statistics - Theory and Methods"
DOI: 10.1080/03610926.2019.1628274
Abstract: Abstract For normalized non-negative data in monotone non-increasing blocks sufficiently close to test exponential functions computable in closed form, there is a unique one-parameter exponential decay curve fitted by weighted non-linear least squares. Upper and… read more here.
Keywords: increasing blocks; negative data; data monotone; non negative ... See more keywords
Benchmarks in antimicrobial peptide prediction are biased due to the selection of negative data
Sign Up to like & getrecommendations! Published in 2022 at "Briefings in Bioinformatics"
DOI: 10.1093/bib/bbac343
Abstract: Abstract Antimicrobial peptides (AMPs) are a heterogeneous group of short polypeptides that target not only microorganisms but also viruses and cancer cells. Due to their lower selection for resistance compared with traditional antibiotics, AMPs have… read more here.
Keywords: amp prediction; prediction; model; data sampling ... See more keywords
Deep Learning-Based microRNA Target Prediction Using Experimental Negative Data
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Access"
DOI: 10.1109/access.2020.3034681
Abstract: MicroRNAs (miRNAs) are small non-coding RNA molecules that control the function of their target messenger RNAs (mRNAs). As miRNAs regulate their target genes by binding them, investigating miRNAs is important to understand various biological processes.… read more here.
Keywords: target prediction; experimental negative; negative data; target ... See more keywords
A Sphere-Description-Based Approach for Multiple-Instance Learning
Sign Up to like & getrecommendations! Published in 2017 at "IEEE Transactions on Pattern Analysis and Machine Intelligence"
DOI: 10.1109/tpami.2016.2539952
Abstract: Multiple-instance learning (MIL) is a generalization of supervised learning which addresses the classification of bags. Similar to traditional supervised learning, most of the existing MIL work is proposed based on the assumption that a representative… read more here.
Keywords: instance learning; negative data; training set; multiple instance ... See more keywords
Classification From Positive and Biased Negative Data With Skewed Labeled Posterior Probability
Sign Up to like & getrecommendations! Published in 2022 at "Neural Computation"
DOI: 10.1162/neco_a_01580
Abstract: Abstract The binary classification problem has a situation where only biased data are observed in one of the classes. In this letter, we propose a new method to approach the positive and biased negative (PbN)… read more here.
Keywords: posterior probability; classification; biased negative; positive biased ... See more keywords
Neglog: Homology-Based Negative Data Sampling Method for Genome-Scale Reconstruction of Human Protein–Protein Interaction Networks
Sign Up to like & getrecommendations! Published in 2019 at "International Journal of Molecular Sciences"
DOI: 10.3390/ijms20205075
Abstract: Rapid reconstruction of genome-scale protein–protein interaction (PPI) networks is instrumental in understanding the cellular processes and disease pathogenesis and drug reactions. However, lack of experimentally verified negative data (i.e., pairs of proteins that do not… read more here.
Keywords: protein; protein interaction; genome scale; negative data ... See more keywords