Related papers: Analyzing LC-MS/MS data by spectral count and ion …
Two-dimensional mass spectrometry (2D MS) is a method for tandem mass spectrometry that enables the correlation between precursor and fragment ions without the need for ion isolation. On a Fourier transform ion cyclotron resonance mass…
Measuring inter-dataset similarity is an important task in machine learning and data mining with various use cases and applications. Existing methods for measuring inter-dataset similarity are computationally expensive, limited, or…
Mass spectrometry (MS) based single-cell proteomics (SCP) explores cellular heterogeneity by focusing on the functional effectors of the cells - proteins. However, extracting meaningful biological information from MS data is far from…
Background. Emerging technologies now allow for mass spectrometry based profiling of up to thousands of small molecule metabolites (metabolomics) in an increasing number of biosamples. While offering great promise for revealing insight into…
Isochronous mass spectrometry (IMS) in storage rings is a powerful tool for mass measurements of exotic nuclei with very short half-lives down to several tens of microseconds, using a multicomponent secondary beam separated in-flight…
High throughput metabolomics data are fraught with both non-ignorable missing observations and unobserved factors that influence a metabolite's measured concentration, and it is well known that ignoring either of these complications can…
Clinical dataset labels are rarely certain as annotators disagree and confidence is not uniform across cases. Typical aggregation procedures, such as majority voting, obscure this variability. In simple experiments on medical imaging…
Multiscale entropy (MSE) has been widely used to examine nonlinear systems involving multiple time scales, such as biological and economic systems. Conversely, Allan variance has been used to evaluate the stability of oscillators, such as…
The integration of data from multiple sources is increasingly used to achieve larger sample sizes and enhance population diversity. Our previous work established that, under random sampling from the same underlying population, integrating…
The concept of isochronous mass spectrometry (IMS) applying two time-of-flight (TOF) detectors originated many years ago at GSI. However, the corresponding method for data analysis has never been discussed in detail. Recently, two TOF…
Mass spectrometry proteomics, characterized by spiky, spatially heterogeneous functional data, can be used to identify potential cancer biomarkers. Existing mass spectrometry analyses utilize mean regression to detect spectral regions that…
The initial assessment of skin lesions is typically based on dermoscopic images. As this is a difficult and time-consuming task, machine learning methods using dermoscopic images have been proposed to assist human experts. Other approaches…
The VAMOS++ magnetic spectrometer is a multi-parametric system that integrates ion optical magnetic elements with a multi-detector stack. The magnetic elements, along with the tracking and timing detectors and the trajectory reconstruction…
Missing data can pose a challenge for machine learning (ML) modeling. To address this, current approaches are categorized into feature imputation and label prediction and are primarily focused on handling missing data to enhance ML…
Background: Cancer drugs receive licensing extensions to include additional indications as trial evidence on treatment effectiveness accumulates. We investigate how sharing information across indications can strengthen the inferences…
Mass spectrometry provides a high-throughput approach to identify proteins in biological samples. A key step in the analysis of mass spectrometry data is to identify the peptide sequence that, most probably, gave rise to each observed…
In Proteomics, only the de novo peptide sequencing approach allows a partial amino acid sequence of a peptide to be found from a MS/MS spectrum. In this article a preliminary work is presented to discover a complete protein sequence from…
New developments in empirical analyses of the proton-proton differential cross section data at high energies are reported. Making use of an unconstrained model-independent parametrization for the scattering amplitude and two different fit…
Compartmental models based on tracer mass balance are extensively used in clinical and pre-clinical nuclear medicine in order to obtain quantitative information on tracer metabolism in the biological tissue. This paper is the first of a…
Spectral densities encode non-perturbative information that enters the calculation of a plethora of physical observables in strongly coupled field theories. Phenomenological applications encompass aspects of standard-model hadronic physics,…