bluuebunny/crossref_metadata_2025_split huggingface dataset:
Stuff, Quality, Structure
Authors: Strawson Galen 
Published: June 2024

AbstractThis book argues for identity metaphysics. It argues, in other words, for categorial monism in metaphysics: the view that there is, so far as concrete reality is concerned, only one truly fundamental metaphysical category: stuff. The use of the word ‘stuff’ is an attempt to escape some of the problematic connotations of more traditional terms like ‘substance’. That said, the meaning of ‘stuff’ may be considered to be close to the meaning of ‘substance’ once ‘substance’ has been completely detached from any ideas of substance as something that ‘bears’ or ‘has’ properties or qualities or ‘underlies’ anything. In more detail: the book argues that in fundamental metaphysics [1] the categories of substance and quality, or object and property, which are traditionally understood to be irreducibly distinct, fuse into a single ontological category. So too [2] the notions of power, force, energy, cause, laws of nature, and natural necessity fuse into a single ontological category. Finally [3] there is a fusion of fusions [1] and [2]. For just as there is no fundamental ontological distinction between the quality being of a thing x and the power being of x, so too there is no fundamental ontological distinction between the quality being of x and its being full stop. The final part of the book argues for the view that if two things x and y are structurally identical, where this means identical under all possible true structural descriptions, then they are stuff identical.
Identity Metaphysics: Set Up
Authors: Strawson Galen 
Published: June 2024

AbstractChapter 2 introduces ‘identity metaphysics’: the categorial-monist view that there is only one fundamental metaphysical category. It prepares the way for the arguments of Chapters 4, 5, and 6, according to which two supposedly fundamental metaphysical distinctions are in fact superficial: the distinction between substance and quality (object and property) and the distinction between categorical being and dispositional or power being. It argues that, in philosophy, description is at least as important as argument. It introduces a general notion of the dimensionality of a world, arguing that space–time is just one possible kind of dimensionality. It assumes the truth of stuff monism, according to which there is only one kind of fundamental stuff or substance, and argues that causal interaction should be taken to be a sufficient condition of same substancehood. It briefly discusses the view that ‘radical emergence’ is impossible, and that some form of panpsychism is plausible.
A Twelve-Word Metaphysics
Authors: Strawson Galen 
Published: June 2024

AbstractChapter 4 offers a twelve-word metaphysics: [M1] Sein ist Sosein: being is quality; [M2] Stoff ist Kraft: matter/stuff is force/energy; [M3] Wesen ist Werden: being is becoming; [M4] Ansichsein ist Fürsichsein: being is mind/consciousness. It then puts [M4] aside in order to gloss [M1]–[M3], by way of further preparation for Chapters 5 and 6. (Chapter 5 focuses on [M1], Chapter 6 on [M2] and [M3].) It returns to the notion of dimensionality, existence-room or Existenzraum, also by way of preparation for Chapters 5 and 6. It proposes the adoption of something close to a (scientifically informed but) common-sense realist view of time. It expresses a preference for thing monism (the view that there is fundamentally only one thing) in addition to the stuff monism endorsed in Chapter 2.
Teaching bioinformatics through the analysis of SARS-CoV-2: project-based training for computer science students
Authors: Poličar Pavlin G , Špendl Martin , Curk Tomaž , Zupan Blaž 
Published: June 2024

Abstract

Motivation
We learn more effectively through experience and reflection than through passive reception of information. Bioinformatics offers an excellent opportunity for project-based learning. Molecular data are abundant and accessible in open repositories, and important concepts in biology can be rediscovered by reanalyzing the data.

Results
In the manuscript, we report on five hands-on assignments we designed for master’s computer science students to train them in bioinformatics for genomics. These assignments are the cornerstones of our introductory bioinformatics course and are centered around the study of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). They assume no prior knowledge of molecular biology but do require programming skills. Through these assignments, students learn about genomes and genes, discover their composition and function, relate SARS-CoV-2 to other viruses, and learn about the body’s response to infection. Student evaluation of the assignments confirms their usefulness and value, their appropriate mastery-level difficulty, and their interesting and motivating storyline.

Availability and Implementation
The course materials are freely available on GitHub at https://github.com/IB-ULFRI.
Representing mutations for predicting cancer drug response
Authors: Wall Patrick , Ideker Trey 
Published: June 2024

Abstract

Motivation
Predicting cancer drug response requires a comprehensive assessment of many mutations present across a tumor genome. While current drug response models generally use a binary mutated/unmutated indicator for each gene, not all mutations in a gene are equivalent.

Results
Here, we construct and evaluate a series of predictive models based on leading methods for quantitative mutation scoring. Such methods include VEST4 and CADD, which score the impact of a mutation on gene function, and CHASMplus, which scores the likelihood a mutation drives cancer. The resulting predictive models capture cellular responses to dabrafenib, which targets BRAF-V600 mutations, whereas models based on binary mutation status do not. Performance improvements generalize to other drugs, extending genetic indications for PIK3CA, ERBB2, EGFR, PARP1, and ABL1 inhibitors. Introducing quantitative mutation features in drug response models increases performance and mechanistic understanding.

Availability and implementation
Code and example datasets are available at https://github.com/pgwall/qms.
Efficient parameter estimation for ODE models of cellular processes using semi-quantitative data
Authors: Dorešić Domagoj , Grein Stephan , Hasenauer Jan 
Published: June 2024

Abstract

Motivation
Quantitative dynamical models facilitate the understanding of biological processes and the prediction of their dynamics. The parameters of these models are commonly estimated from experimental data. Yet, experimental data generated from different techniques do not provide direct information about the state of the system but a nonlinear (monotonic) transformation of it. For such semi-quantitative data, when this transformation is unknown, it is not apparent how the model simulations and the experimental data can be compared.

Results
We propose a versatile spline-based approach for the integration of a broad spectrum of semi-quantitative data into parameter estimation. We derive analytical formulas for the gradients of the hierarchical objective function and show that this substantially increases the estimation efficiency. Subsequently, we demonstrate that the method allows for the reliable discovery of unknown measurement transformations. Furthermore, we show that this approach can significantly improve the parameter inference based on semi-quantitative data in comparison to available methods.

Availability and implementation
Modelers can easily apply our method by using our implementation in the open-source Python Parameter EStimation TOolbox (pyPESTO) available at https://github.com/ICB-DCM/pyPESTO.
Sigmoni: classification of nanopore signal with a compressed pangenome index
Authors: Shivakumar Vikram S , Ahmed Omar Y , Kovaka Sam , Zakeri Mohsen , Langmead Ben 
Published: June 2024

Abstract

Summary
Improvements in nanopore sequencing necessitate efficient classification methods, including pre-filtering and adaptive sampling algorithms that enrich for reads of interest. Signal-based approaches circumvent the computational bottleneck of basecalling. But past methods for signal-based classification do not scale efficiently to large, repetitive references like pangenomes, limiting their utility to partial references or individual genomes. We introduce Sigmoni: a rapid, multiclass classification method based on the r-index that scales to references of hundreds of Gbps. Sigmoni quantizes nanopore signal into a discrete alphabet of picoamp ranges. It performs rapid, approximate matching using matching statistics, classifying reads based on distributions of picoamp matching statistics and co-linearity statistics, all in linear query time without the need for seed-chain-extend. Sigmoni is 10–100× faster than previous methods for adaptive sampling in host depletion experiments with improved accuracy, and can query reads against large microbial or human pangenomes. Sigmoni is the first signal-based tool to scale to a complete human genome and pangenome while remaining fast enough for adaptive sampling applications.

Availability and implementation
Sigmoni is implemented in Python, and is available open-source at https://github.com/vshiv18/sigmoni.
Oncotree2vec — a method for embedding and clustering of tumor mutation trees
Authors: Baciu-Drăgan Monica-Andreea , Beerenwinkel Niko 
Published: June 2024

Abstract

Motivation
Understanding the genomic heterogeneity of tumors is an important task in computational oncology, especially in the context of finding personalized treatments based on the genetic profile of each patient’s tumor. Tumor clustering that takes into account the temporal order of genetic events, as represented by tumor mutation trees, is a powerful approach for grouping together patients with genetically and evolutionarily similar tumors and can provide insights into discovering tumor subtypes, for more accurate clinical diagnosis and prognosis.

Results
Here, we propose oncotree2vec, a method for clustering tumor mutation trees by learning vector representations of mutation trees that capture the different relationships between subclones in an unsupervised manner. Learning low-dimensional tree embeddings facilitates the visualization of relations between trees in large cohorts and can be used for downstream analyses, such as deep learning approaches for single-cell multi-omics data integration. We assessed the performance and the usefulness of our method in three simulation studies and on two real datasets: a cohort of 43 trees from six cancer types with different branching patterns corresponding to different modes of spatial tumor evolution and a cohort of 123 AML mutation trees.

Availability and implementation
https://github.com/cbg-ethz/oncotree2vec.
A learned score function improves the power of mass spectrometry database search
Authors: Ananth Varun , Sanders Justin , Yilmaz Melih , Wen Bo , Oh Sewoong , Noble William Stafford 
Published: June 2024

Abstract

Motivation
One of the core problems in the analysis of protein tandem mass spectrometry data is the peptide assignment problem: determining, for each observed spectrum, the peptide sequence that was responsible for generating the spectrum. Two primary classes of methods are used to solve this problem: database search and de novo peptide sequencing. State-of-the-art methods for de novo sequencing use machine learning methods, whereas most database search engines use hand-designed score functions to evaluate the quality of a match between an observed spectrum and a candidate peptide from the database. We hypothesized that machine learning models for de novo sequencing implicitly learn a score function that captures the relationship between peptides and spectra, and thus may be re-purposed as a score function for database search. Because this score function is trained from massive amounts of mass spectrometry data, it could potentially outperform existing, hand-designed database search tools.

Results
To test this hypothesis, we re-engineered Casanovo, which has been shown to provide state-of-the-art de novo sequencing capabilities, to assign scores to given peptide-spectrum pairs. We then evaluated the statistical power of this Casanovo score function, Casanovo-DB, to detect peptides on a benchmark of three mass spectrometry runs from three different species. In addition, we show that re-scoring with the Percolator post-processor benefits Casanovo-DB more than other score functions, further increasing the number of detected peptides.
Maximum likelihood phylogeographic inference of cell motility and cell division from spatial lineage tracing data
Authors: Mai Uyen , Hu Gary , Raphael Benjamin J 
Published: June 2024

Abstract

Motivation
Recently developed spatial lineage tracing technologies induce somatic mutations at specific genomic loci in a population of growing cells and then measure these mutations in the sampled cells along with the physical locations of the cells. These technologies enable high-throughput studies of developmental processes over space and time. However, these applications rely on accurate reconstruction of a spatial cell lineage tree describing both past cell divisions and cell locations. Spatial lineage trees are related to phylogeographic models that have been well-studied in the phylogenetics literature. We demonstrate that standard phylogeographic models based on Brownian motion are inadequate to describe the spatial symmetric displacement (SD) of cells during cell division.

Results
We introduce a new model—the SD model for cell motility that includes symmetric displacements of daughter cells from the parental cell followed by independent diffusion of daughter cells. We show that this model more accurately describes the locations of cells in a real spatial lineage tracing of mouse embryonic stem cells. Combining the spatial SD model with an evolutionary model of DNA mutations, we obtain a phylogeographic model for spatial lineage tracing. Using this model, we devise a maximum likelihood framework—MOLLUSC (Maximum Likelihood Estimation Of Lineage and Location Using Single-Cell Spatial Lineage tracing Data)—to co-estimate time-resolved branch lengths, spatial diffusion rate, and mutation rate. On both simulated and real data, we show that MOLLUSC accurately estimates all parameters. In contrast, the Brownian motion model overestimates spatial diffusion rate in all test cases. In addition, the inclusion of spatial information improves accuracy of branch length estimation compared to sequence data alone. On real data, we show that spatial information has more signal than sequence data for branch length estimation, suggesting augmenting lineage tracing technologies with spatial information is useful to overcome the limitations of genome-editing in developmental systems.

Availability and Implementation
The python implementation of MOLLUSC is available at https://github.com/raphael-group/MOLLUSC.
Partial RNA design
Authors: Runge Frederic , Franke Jörg , Fertmann Daniel , Backofen Rolf , Hutter Frank 
Published: June 2024

AbstractMotivationRNA design is a key technique to achieve new functionality in fields like synthetic biology or biotechnology. Computational tools could help to find such RNA sequences but they are often limited in their formulation of the search space.ResultsIn this work, we propose partial RNA design, a novel RNA design paradigm that addresses the limitations of current RNA design formulations. Partial RNA design describes the problem of designing RNAs from arbitrary RNA sequences and structure motifs with multiple design goals. By separating the design space from the objectives, our formulation enables the design of RNAs with variable lengths and desired properties, while still allowing precise control over sequence and structure constraints at individual positions. Based on this formulation, we introduce a new algorithm, libLEARNA, capable of efficiently solving different constraint RNA design tasks. A comprehensive analysis of various problems, including a realistic riboswitch design task, reveals the outstanding performance of libLEARNA and its robustness.Availability and ImplementationlibLEARNA is open-source and publicly available at: https://github.com/automl/learna_tools.
Towards more accurate microbial source tracking via non-negative matrix factorization (NMF)
Authors: Huang Ziyi , Cai Dehan , Sun Yanni 
Published: June 2024

Abstract

Motivation
The microbiome of a sampled habitat often consists of microbial communities from various sources, including potential contaminants. Microbial source tracking (MST) can be used to discern the contribution of each source to the observed microbiome data, thus enabling the identification and tracking of microbial communities within a sample. Therefore, MST has various applications, from monitoring microbial contamination in clinical labs to tracing the source of pollution in environmental samples. Despite promising results in MST development, there is still room for improvement, particularly for applications where precise quantification of each source’s contribution is critical.

Results
In this study, we introduce a novel tool called SourceID-NMF towards more precise microbial source tracking. SourceID-NMF utilizes a non-negative matrix factorization (NMF) algorithm to trace the microbial sources contributing to a target sample. By leveraging the taxa abundance in both available sources and the target sample, SourceID-NMF estimates the proportion of available sources present in the target sample. To evaluate the performance of SourceID-NMF, we conducted a series of benchmarking experiments using simulated and real data. The simulated experiments mimic realistic yet challenging scenarios for identifying highly similar sources, irrelevant sources, unknown sources, low abundance sources, and noise sources. The results demonstrate the superior accuracy of SourceID-NMF over existing methods. Particularly, SourceID-NMF accurately estimated the proportion of irrelevant and unknown sources while other tools either over- or under-estimated them. In addition, the noise sources experiment also demonstrated the robustness of SourceID-NMF for MST.

Availability and implementation
SourceID-NMF is available online at https://github.com/ZiyiHuang0708/SourceID-NMF.
Approximating facial expression effects on diagnostic accuracy via generative AI in medical genetics
Authors: Patel Tanviben , Othman Amna A , Sümer Ömer , Hellman Fabio , Krawitz Peter , André Elisabeth , Ripper Molly E , Fortney Chris , Persky Susan , Hu Ping , Tekendo-Ngongang Cedrik , Hanchard Suzanna Ledgister , Flaharty Kendall A , Waikel Rebekah L , Duong Dat , Solomon Benjamin D 
Published: June 2024

Abstract
Summary
Artificial intelligence (AI) is increasingly used in genomics research and practice, and generative AI has garnered significant recent attention. In clinical applications of generative AI, aspects of the underlying datasets can impact results, and confounders should be studied and mitigated. One example involves the facial expressions of people with genetic conditions. Stereotypically, Williams (WS) and Angelman (AS) syndromes are associated with a “happy” demeanor, including a smiling expression. Clinical geneticists may be more likely to identify these conditions in images of smiling individuals. To study the impact of facial expression, we analyzed publicly available facial images of approximately 3500 individuals with genetic conditions. Using a deep learning (DL) image classifier, we found that WS and AS images with non-smiling expressions had significantly lower prediction probabilities for the correct syndrome labels than those with smiling expressions. This was not seen for 22q11.2 deletion and Noonan syndromes, which are not associated with a smiling expression. To further explore the effect of facial expressions, we computationally altered the facial expressions for these images. We trained HyperStyle, a GAN-inversion technique compatible with StyleGAN2, to determine the vector representations of our images. Then, following the concept of InterfaceGAN, we edited these vectors to recreate the original images in a phenotypically accurate way but with a different facial expression. Through online surveys and an eye-tracking experiment, we examined how altered facial expressions affect the performance of human experts. We overall found that facial expression is associated with diagnostic accuracy variably in different genetic conditions.
Deciphering high-order structures in spatial transcriptomes with graph-guided Tucker decomposition
Authors: Broadbent Charles , Song Tianci , Kuang Rui 
Published: June 2024

Abstract

Spatial transcripome (ST) profiling can reveal cells’ structural organizations and functional roles in tissues. However, deciphering the spatial context of gene expressions in ST data is a challenge—the high-order structure hiding in whole transcriptome space over 2D/3D spatial coordinates requires modeling and detection of interpretable high-order elements and components for further functional analysis and interpretation. This paper presents a new method GraphTucker—graph-regularized Tucker tensor decomposition for learning high-order factorization in ST data. GraphTucker is based on a nonnegative Tucker decomposition algorithm regularized by a high-order graph that captures spatial relation among spots and functional relation among genes. In the experiments on several Visium and Stereo-seq datasets, the novelty and advantage of modeling multiway multilinear relationships among the components in Tucker decomposition are demonstrated as opposed to the Canonical Polyadic Decomposition and conventional matrix factorization models by evaluation of detecting spatial components of gene modules, clustering spatial coefficients for tissue segmentation and imputing complete spatial transcriptomes. The results of visualization show strong evidence that GraphTucker detect more interpretable spatial components in the context of the spatial domains in the tissues.

Availability and implementation
https://github.com/kuanglab/GraphTucker.
Closing the computational biology ‘knowledge gap’: Spanish Wikipedia as a case study
Authors: Sélem-Mojica Nelly , Karakulak Tülay , Anjum Audra , Pashkov Antón , Pérez-Estrada Rafael , Enriquez-Guillén Karina , DeBlasio Dan , Ferreira-Gonzalez Sofia , Medina-Rivera Alejandra , Rodrigo-Torres Daniel , Kilpatrick Alastair M , Welch Lonnie R , Rahman Farzana 
Published: June 2024

Abstract

Motivation
Wikipedia is a vital open educational resource in computational biology. The quality of computational biology coverage in English-language Wikipedia has improved steadily in recent years. However, there is an increasingly large ‘knowledge gap’ between computational biology resources in English-language Wikipedia, and Wikipedias in non-English languages. Reducing this knowledge gap by providing educational resources in non-English languages would reduce language barriers which disadvantage non-native English speaking learners across multiple dimensions in computational biology.

Results
Here, we provide a comprehensive assessment of computational biology coverage in Spanish-language Wikipedia, the second most accessed Wikipedia worldwide. Using Spanish-language Wikipedia as a case study, we generate quantitative and qualitative data before and after a targeted educational event, specifically, a Spanish-focused student editing competition. Our data demonstrates how such events and activities can narrow the knowledge gap between English and non-English educational resources, by improving existing articles and creating new articles. Finally, based on our analysis, we suggest ways to prioritize future initiatives to improve open educational resources in other languages.

Availability and Implementation
Scripts for data analysis are available at: https://github.com/ISCBWikiTeam/spanish.