26 papers · ranked by Valyu relevance
Alejandro Moreo, Andrea Esuli, Fabrizio Sebastiani
QuaPy is an open-source framework for performing quantification (a.k.a. supervised prevalence estimation), written in Python. Quantification is the task of training quantifiers via supervised learning, where a quantifier is a predictor that estimates the relative frequencies (a.k.a. prevalence values) of the classes of…
Fabrizio Sebastiani
Quantification is the task of estimating, given a set σ of unlabelled items and a set of classes C = {c1, . . . , c|C|}, the prevalence (or "relative frequency") in σ of each class ci ∈ C. While quantification may in principle be solved by classifying each item in σ and counting how many such items have been labelled…
Authors not listed
Quantification is a fundamental component of everyday language use, yet little is known about how speakers decide whether and how to quantify in naturalistic production. We investigate quantification in Mandarin Chinese using a picture-based elicited description task in which speakers freely described scenes containing…
Alejandro Moreo, Fabrizio Sebastiani, Sergio Consoli
Sentiment quantification is the task of training, by means of supervised learning, estimators of the relative frequency (also called “prevalence”) of sentiment-related classes (such as Positive, Neutral, Negative) in a sample of unlabelled texts. This task is especially important when these texts are tweets, since the…
Andrea Esuli, Alejandro Moreo, Fabrizio Sebastiani
Quantification is a supervised learning task that consists in predicting, given a set of classes C and a set D of unlabelled items, the prevalence (or relative frequency) pc (D) of each class c ∈ C in D. Quantification can in principle be solved by classifying all the unlabelled items and counting how many of them have…
Tobias Schumacher, Markus Strohmaier, Florian Lemmerich
Quantification represents the problem of predicting class distributions in a dataset. It also represents a growing research field in supervised machine learning, for which a large variety of different algorithms has been proposed in recent years. However, a comprehensive empirical comparison of quantification methods…
Alejandro Moreo, Fabrizio Sebastiani
Sentiment quantification is the task of training, by means of supervised learning, estimators of the relative frequency (also called "prevalence") of sentiment-related classes (such as Positive, Neutral, Negative) in a sample of unlabelled texts. This task is especially important when these texts are tweets, since the…
Andreas M Boehm, Stephanie Pütz, Daniela Altenhöfer, Albert Sickmann + 1 more
'Michael Falk'] Background Mass spectrometry based quantification of peptides can be performed using the iTRAQ™ reagent in conjunction with mass spectrometry. This technology yields information about the relative abundance of single peptides. A method for the calculation of reliable quantification information is…
Xiangrong Tang, Yunqing Wen, Rong Qin, Jishen Zhang + 4 more
Quantitative polymerase chain reaction (qPCR) is limited in measuring absolute nucleic acid copy numbers due to the inherent variability of calibrators. Here, we introduce the Quantal PCR (quPCR), a novel method that eliminates the need for calibrators by defining an intrinsic quantal unit derived from the…
Sharon S. Newman, Linus A. Hein, Alexandra M. Adams, H. Tom Soh
Gold standard immunoassays depend on specific affinity reagents for accurate molecular quantification. Any cross-reactivity of affinity reagents, wherein the reagent non-specifically binds to unintended molecules, can create false positive binding signals and result in inaccurate quantification of analytes. Mitigating…
Eszter Csibra, Guy-Bart Stan
This paper presents a generalisable method for the calibration of fluorescence readings on microplate readers, in order to convert arbitrary fluorescence units into absolute units. FPCountR relies on the generation of bespoke fluorescent protein (FP) calibrants, assays to determine protein concentration and activity…
Min-Jung Kang, Hannah Yu, Sook-Kyung Kim, Sang-Ryoul Park + 2 more
Quantification of trace amounts of DNA is a challenge in analytical applications where the concentration of a target DNA is very low or only limited amounts of samples are available for analysis. PCR-based methods including real-time PCR are highly sensitive and widely used for quantification of low-level DNA samples.…
Authors not listed
The gut microbiota produces metabolites that are important for host physiology and have critical roles in the development of diseases, such as metabolic disorders, cardiovascular diseases, and cancer. Here, we developed a gas chromatography-coupled to tandem mass spectrometry (GC-MS/MS) method for the quantification of…
Authors not listed
The Single-probe is a multifunctional device that can be coupled to mass spectrometry (MS) for molecular analysis of microscale samples, such as single cells, tissue slices, and multicellular spheroids, under ambient conditions. In Single-probe single cell MS (SCMS) studies, this technique leverages direct sampling and…
Authors not listed
Proton nuclear magnetic resonance (1H NMR) spectroscopy offers rapid quantification of saturated (SFA), monounsaturated (MUFA), and polyunsaturated (PUFA) fatty acids in oils. While high-field NMR has been widely applied, its high operational costs limit accessibility. In contrast, benchtop NMR provides a more…
Jiaqi Yuan, Hui Yin Tan, Yue Huang, Anton Rosenbaum
Antibody-drug conjugate (ADC) is a therapeutic modality that aims to improve payload delivery specificity and reduce systemic toxicity. Considering the complex structure of ADCs, various bioanalytical methods by liquid chromatography coupled with mass spectrometry (LC-MS) and ligand binding assay (LBA) as well as…
Itay Gelber
Quantifying protein number using the ratio between the variance and the mean of the protein distribution is a straightforward calibration method in the experimental conditions for microscopy imaging. Recently the model has been expanded to decaying processes with binomial distribution. In this paper, we examine the…
Authors not listed
The rapid growth of worldwide computing power has transformed in silico chemistry into a discipline that is integrated into the daily work of many chemists. Nowadays, researchers find it increasingly straightforward to predict a wide range of molecular properties and chemi- cal processes at reasonable computational…
Martin Walker, María-Gloria Basáñez, André Lin Ouédraogo, Cornelus Hermsen + 2 more
'Cornelus Hermsen' 'Teun Bousema' 'Thomas S Churcher'] Background Quantitative molecular methods (QMMs) such as quantitative real-time polymerase chain reaction (q-PCR), reverse-transcriptase PCR (qRT-PCR) and quantitative nucleic acid sequence-based amplification (QT-NASBA) are increasingly used to estimate pathogen…
Brandon D. Wilson, Michael Eisenstein, H. Tom Soh
Many assay developers focus on limit of detection (LOD) as a primary performance metric, and LOD is indeed useful for assays designed to determine the presence or absence of an analyte. However, LOD is less useful for ‘continuous assays’ designed to discriminate between concentrations of an analyte—e.g., glucose…
Zhe Du, Sebastian F. Behrens, Zhe-Xue Quan
Quantitative real-time PCR of phylogenetic and functional marker genes is among the most commonly used techniques to quantify the abundance of microbial taxa in environmental samples. However, in most environmental applications, the approach is a rough assessment of population abundance rather than an exact absolute…
Yulia Panina, Arno Germond, Brit G. David, Tomonobu M. Watanabe
The real-time quantitative polymerase chain reaction (qPCR) is routinely used for quantification of nucleic acids and is considered the gold standard in the field of relative nucleic acid measurements. The efficiency of the qPCR reaction is one of the most important parameters that needs to be determined, reported, and…
Brandon D. Wilson, H. Tom Soh
Analytical technologies based on binding assays have evolved substantially since their inception nearly 60 years ago, but our conceptual understanding of molecular recognition has not kept pace. Indeed, contemporary technologies such as single-molecule and digital measurements have challenged, or even rendered…
Maria H. Rasmussen, Chenru Duan, Heather J. Kulik, Jan Halborg Jensen
With the increasingly more important role of machine learning (ML) models in chemical research, the need for putting a level of confidence to the model predictions naturally arises. Several methods for obtaining uncertainty estimates have been proposed in recent years but consensus on the evaluation of these have yet…
R. S. Ogden, F. R. Simmons, J. H. Wearden
Performance similarities on tasks requiring the processing of different domains of magnitude (e.g. time, numerosity, and length) have led to the suggestion that humans possess a common processing system for all domains of magnitude (Bueti and Walsh in Philos Trans R Soc B 364:1831-1840, 2009). In light of this, the…
Marco Bertamini
There are many situations in which we interact with collections of objects, from a crowd of people to a bowl of blackberries. There is an experience of the quantity of these items, although not a precise number, and we have this impression quickly and effortlessly. It can be described as an expressive property of the…