Biology subjects

Jacobs, M.

Publications and source records attributed to Jacobs, M..

18 recordsLinked to original sources

bioRxiv2025

An Interactive Brain Atlas of Knowledge

Biomedical knowledge about the brain increases every day, with a rapidly growing number of scientific publications, datasets, and software tools. While this informational plethora is not merely comprehensible by human beings, recent developments in information science and computational linguistics aim to make this knowledge programmatically accessible by literature mining. However, integrating these semantic methods into neuroimaging standards remains insufficient, hindering researchers from unraveling their full potential. Therefore, we developed the semantic meta-analysis platform The Virtual Brain adapter of semantics (TVBase) that projects biomedical knowledge preserved in over 36 million scientific articles onto a 3D standardized brain. The literature-mining platform SCAIView was used to extract ontologically defined biomedical entities and their associations with brain anatomy from the PubMed database. By querying a specific concept, the association strength with each anatomical term was calculated using entropy. To project the data onto a standardized brain, we created a unique transformation matrix that links over 800 anatomical terms to voxel coordinates of a parcellated standard brain. This novel method of knowledge projection extracts region-specific information about biomedical concepts from the literature to support translational multi-scale approaches to computational neuroscience. The multi-purpose software framework TVBase is openly available as a Python library. It aims for hypothesis-free neuroimaging pattern interpretation, hypothesis generation, and applications in personalized medicine.

neuroscience↗

bioRxiv2025

Leveraging Multimodal Large Language Models to Extract Mechanistic Insights from Biomedical Visuals: A Case Study on COVID-19 and Neurodegenerative Diseases

BackgroundThe COVID-19 pandemic has intensified concerns about its long-term neurological impact, with growing evidence linking SARS-CoV-2 infection to neurodegenerative diseases (NDDs) such as Alzheimers (AD) and Parkinsons (PD). Patients with these conditions not only face higher risk of severe COVID-19 outcomes but may also undergo accelerated cognitive and motor decline following infection. Proposed mechanisms--ranging from neuroinflammation and blood-brain barrier disruption to abnormal protein aggregation--closely mirror core features of neurodegenerative pathology. Yet, current knowledge is fragmented across text, figures, and pathway diagrams, hindering integration into computational models capable of uncovering systemic patterns. ResultsTo address this gap, we applied GPT-4 Omni (GPT-4o), a multimodal large language model, to extract mechanistic insights from biomedical figures. Over 10,000 images were retrieved through targeted searches on COVID-19 and neurodegeneration; after automated and manual filtering, a curated subset was analyzed. GPT-4o extracted biological relationships as semantic triples, which were grouped into six mechanistic categories--including microglial activation and barrier disruption--using ontology-guided similarity and assembled into a Neo4j knowledge graph. Accuracy was evaluated against a gold-standard dataset of expert-annotated images using BioBERT-based semantic matching. This evaluation also enabled prompt tuning, threshold optimization, and hyperparameter assessment. Results demonstrate that GPT-4o successfully recovers both established and novel mechanisms, yielding interpretable outputs that illuminate complex biological links between SARS-CoV-2 and neurodegeneration. ConclusionsThis study showcases the potential of multimodal LLMs to mine biomedical visual data at scale. By complementing text mining and integrating figure-derived knowledge, our framework advances understanding of COVID-19-related neurodegeneration and supports future translational research.

bioinformatics↗

bioRxiv2025

On the utilisation and characterisation of external biotransformation systems in in vitro toxicology: a critical review of the scientific literature with guidance recommendations

Incorporating biotransformation capabilities into in vitro assays represents one of the most critical challenges in toxicology, facilitating the transition from in vivo models to integrated in vitro strategies. Although emerging technologies show promise, their current limitations in scalability hinder high-throughput applications. In the short to mid-term, externally added biotransformation systems ("BTS": S9 and microsomal liver fractions) used together with in vitro assays offer viable alternatives. However, despite over fifty years of use, BTS are marred by reproducibility issues, raising concerns about their reliability, and raising the question: Are BTS inherently unreliable, or has their reputation been flawed by methodological oversights? This review critically evaluates BTS methodological rigour, applying a deep statistical analysis of the scientific literature. We employed Boolean operator searches across scientific literature repositories to curate a database on BTS research in conjunction with relevant in vitro assays, focusing on endocrine disruption, mutagenicity, and genotoxicity endpoints. Through systematic searches, screening, and eligibility criteria, we identified 229 bibliographic records. Data parameterisation and extraction were conducted across 24 domains of BTS relevance and reliability. Methodological reporting rigour was assessed via scoring (reported vs. non-reported data items) and revealed a lack of reproducible standards. Numerical measures associated with principal BTS reaction components were subjected to meta-regression analyses. No statistically significant correlations were found for BTS and related cofactor concentration-response relationships or time-related elements. Finally, descriptive statistics, multiple correspondence analysis, and Apriori algorithm-based relational networks identified qualitative patterns of methodological robustness and deficiencies. In conclusion, these results emphasise shortcomings across the scientific literature in complying with appropriate methodological reporting. We offer evidence-based recommendations, in the form of a conceptual regulatory guidance framework, to enhance research practices, quality, and reproducibility of BTS applications; designed to strengthen the robustness of BTS research and its integration into regulatory-relevant hazard and risk assessment of chemicals. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/677684v1_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@1a6dd9aorg.highwire.dtl.DTLVardef@1ecb22org.highwire.dtl.DTLVardef@1aa3ee3org.highwire.dtl.DTLVardef@6aaf6_HPS_FORMAT_FIGEXP M_FIG C_FIG

pharmacology and toxicology↗

bioRxiv2025

The human Flower isoform hFWE4 facilitates cornification in cutaneous squamous cell carcinoma

Cutaneous squamous cell carcinoma (cSCC) is the second most common human tumor and arises from keratinocytes that comprise the epidermis and its associated hair follicles. Remarkably, transformed keratinocytes within a cSCC retain some ability to execute the terminal differentiation program that typically generates a stratified epidermis. Instead of the polarized stratification towards the body surface seen in normal epidermis, cSCC keratinocytes differentiate concentrically inward to produce keratin pearls. It is well appreciated that cSCC differentiation status is correlated with degree of local invasion and potential for distant metastasis, with well differentiated tumors having a better prognosis. While molecular mechanisms that govern the differentiation process are incompletely understood, identifying candidate molecules associated with cSCC differentiation can provide new histologic markers for prognostic stratification. Here we demonstrate that Flower (FWE), a newly described regulator of lamellar body (LB) trafficking in normal epidermis, is specifically expressed within highly differentiated layers of cSCC. Genetic knockout of the hFWE gene dysregulates cornification and LB-related gene expression leading to abnormal keratinization patterns, while ectopic hFWE4 expression drives G1-arrest and exit from the proliferative basal keratinocyte population. Additionally, we find that poorly differentiated keratinocyte regions of human cSCC exhibit minimal FWE-positivity, while this population is readily detectable in well-differentiated regions. We propose that FWE represents a novel differentiation marker in cSCC that could be utilized for classifying differentiation status and prognostic stratifying these tumors.

cancer biology↗

bioRxiv2025

Unraveling the Co-Morbidity between COVID-19 and Neurodegenerative Diseases Through Multi-scale Graph Analysis: A Systematic Investigation of Biological Databases and Text Mining

The COVID-19 pandemic has generated a vast volume of research, yet much of it focuses on individual diseases, overlooking complex comorbidity relationships. While extensive literature exists on both neurodegenerative diseases (NDDs), such as Alzheimers and Parkinsons, and COVID-19, their intersection remains underexplored. Co-morbidity modeling is crucial, particularly for hospitalized patients often presenting with multiple conditions. This study investigates the interplay between COVID-19 and NDDs by integrating knowledge graphs (KGs) built from curated biomedical datasets and text mining tools. We performed comprehensive analyses--including path analysis, phenotype coverage, and mapping of cellular and genetic factors--across multiple KGs, such as PrimeKG, DrugBank, OpenTargets, and those generated via natural language processing (NLP) methods. Our findings reveal notable variability in graph density and connectivity, with each KG offering unique insights into molecular and phenotypic links between COVID-19 and NDDs. Key genetic and inflammatory markers, especially immune response genes, consistently appeared across graphs, suggesting a shared pathogenic basis. By unifying structured biological data with unstructured textual evidence, we enhance co-morbidity modeling and improve recall in identifying mechanisms underlying COVID-19- NDD interactions. This integrative framework supports the development of a co-morbidity hypothesis database aimed at facilitating therapeutic target discovery. All data, methods, and instructions for accessing the co-morbidity hypothesis database are publicly available at: https://github.com/SCAI-BIO/covid-NDD-comorbidity-NLP.

bioinformatics↗

bioRxiv2025

Frontoparietal activity related to neuropsychological assessment of working memory

Executive functions, including working memory, are typically assessed clinically with neuropsychological instruments. In contrast, computerized tasks are used to test these cognitive functions in laboratory human and animal studies. Little is known of how neural activity captured by laboratory tasks relates to ability measured by clinical instruments and, by extension, clinical diagnoses of pathological conditions. We therefore sought to determine what aspects of neural activity elicited in laboratory tasks are predictive of performance in neuropsychological instruments. We recorded neural activity from intracranial electrodes implanted in human epilepsy patients as they performed laboratory working memory tasks. These patients had completed neuropsychological instruments preoperatively, including the Weschler Adult Intelligent Scale and the Wisconsin Card Sorting test. Our results revealed that increased high-gamma (70-150 Hz) power in the prefrontal and parietal cortex after presentation of visual stimuli to be remembered was indicative of lower performance in the neuropsychological tasks. On the other hand, we observed a positive correlation between high-frequency power amplitude in the delay period of the laboratory tasks and neuropsychological performance. Our results demonstrate how neural activity around task events relates to executive function and may be associated with clinical diagnosis of specific cognitive deficits.

neuroscience↗

bioRxiv2024

Mapping the druggable targets displayed by human colonic enteroendocrine cells

Enteroendocrine cells (EECs) are specialized intestinal hormone-secreting cells that play critical roles in metabolic homeostasis, digestion, and gut-brain communication. They detect diverse stimuli including endocrine, immune, neuronal, microbial, and dietary signals, through a complex array of receptors, ion channels, and transporters, to modulate the release of over 20 hormones. These molecular sensors serve as potential drug targets to modulate hormone secretion, but until recently, catalogues of such targets in human colonic EECs have not been produced. To address this gap, we performed bulk and single-cell RNA sequencing on fluorescently labelled EECs isolated from human colonic organoids, identifying and cataloguing potential druggable targets. This catalogue includes receptors, orphan GPCRs, transporters, and hormones not previously reported in human colonic EECs. Comparison with murine EECs highlighted interspecies similarities and differences, key data to facilitate the design and optimise the predictive accuracy of pre-clinical models. We also functionally validated two receptors not previously identified in human EECs: IL-13R1, was expressed in both peptide-producing EECs and serotonin producing Enterochromaffin cells (ECs), and its ligand IL-13 stimulated the secretion of glucagon-like peptide-1 (GLP-1) and serotonin measured in real-time, and GPR173, which was selectively expressed in ECs and, when activated by its agonist Phoenixin-20, also promoted serotonin release. These analyses provide a valuable resource for therapeutic interventions aimed at modulating gut hormone secretion, with potential applications in treating gastrointestinal, metabolic, and other related disorders.

cell biology↗

bioRxiv2023

Adherent reformed islets: a long-term primary cell-based platform for exploring mouse and human islet biology.

Pancreatic islets are 3-dimensional micro-organs that maintain {beta}-cell functionality via cell-cell and cell-matrix communication. Isolated primary islets are the gold standard for in vitro models. However, native islets present experimental challenges for long-term mechanistic studies owing to their short culture life (approximately 1 week). We developed a novel long-term protocol to study the function of primary islets. The protocol employed reformed islets following dispersion and a fine-tuned culture environment. Reformed islets are highly similar to their primary counterparts across various physiological characteristics. Long-term culture of reformed islets enables high-resolution imaging, repeated functional assessment, and the study of cell-cell communication. Unlike other platforms such as stem cell-derived organoids, reformed islets retain their resident immune populations, making them ideal for studying both resident and infiltrating immune cells and their interactions with hormone-producing islet cells. Qualitative and quantitative analyses revealed that the composition and cytoarchitecture of the reformed islets mimicked those found in primary islets, including the presence of macrophages and CD4+ and CD8+ T cells, which are the key resident immune cell types. Reformed islets secrete insulin and are glucose-responsive, and their {beta}-cells can be stimulated to proliferate using GLP-1 receptor agonism. Furthermore, a comparison of the transcriptomic landscape of isolated human islets and reformed islets generated from the same donor demonstrated a high degree of similarity. Our reformed islets provide an ideal platform to study diabetes pathology. We recapitulated both the T1DM and T2DM disease milieu and validated our model for studying islet immune trafficking and invasion using activated macrophages and T cells. Our data illustrates that reformed islets are an anatomical and functional alternative to native human and mouse islets. Moreover, reformed islets have an advantage over mouse and human {beta}-cell lines, including MIN6 and EndoC-{beta}H1cells, that lack the signalling input of non-{beta}-endocrine cells and immune cell crosstalk. In this study, we showed that reformed islets are a durable paradigm (cell-based model) for islet-based exploration and a means of target discovery/validation for diabetes research. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=141 SRC="FIGDIR/small/568245v2_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@1201151org.highwire.dtl.DTLVardef@510ea8org.highwire.dtl.DTLVardef@19937c1org.highwire.dtl.DTLVardef@1b85a6_HPS_FORMAT_FIGEXP M_FIG C_FIG

physiology↗

bioRxiv2023

Cell type-specific gene expression dynamics during human brain maturation

The human brain undergoes protracted post-natal maturation, guided by dynamic changes in gene expression. Most studies exploring these processes have used bulk tissue analyses, which mask cell type-specific gene expression dynamics. Here, using single nucleus (sn)RNA-seq on temporal lobe tissue, including samples of African ancestry, we build a joint paediatric and adult atlas of 75 cell subtypes, which we verify with spatial transcriptomics. We explore the differences between paediatric and adult cell types, revealing the genes and pathways that change during brain maturation. Our results highlight excitatory neuron subtypes, including the LTK and FREM subtypes, that show elevated expression of genes associated with cognition and synaptic plasticity in paediatric tissue. The new resources we present here improve our understanding of the brain during its development and contribute to global efforts to build an inclusive brain cell map.

neuroscience↗

bioRxiv2023

A data-driven evaluation of Arabidopsis-centric research and the model species concept

The selection of Arabidopsis as a model organism played a pivotal role in advancing genomic science, firmly establishing the cornerstone of today s plant molecular biology. Competing frameworks to select an agricultural- or ecological-based model species, or to decentralize plant science and study a multitude of diverse species, were selected against in favor of building core knowledge in a species that would facilitate genome-enabled research that could assumedly be transferred to other plants. Here, we examine the ability of models based on Arabidopsis gene expression data to predict tissue identity in other flowering plant species. Comparing different machine learning algorithms, models trained and tested on Arabidopsis data achieved near perfect precision and recall values using the K-Nearest Neighbor method, whereas when tissue identity is predicted across the flowering plants using models trained on Arabidopsis data, precision values range from 0.69 to 0.74 and recall from 0.54 to 0.64, depending on the algorithm used. Below-ground tissue is more predictable than other tissue types, and the ability to predict tissue identity is not correlated with phylogenetic distance from Arabidopsis. This suggests that gene expression signatures rather than marker genes are more valuable to create models for tissue and cell type prediction in plants. Our data-driven results highlight that, in hindsight, the assertion that knowledge from Arabidopsis is translatable to other plants is not always true. Considering the current landscape of abundant sequencing data and computational resources, it may be prudent to reevaluate the scientific emphasis on Arabidopsis and to prioritize the exploration of plant diversity.

plant biology↗

bioRxiv2023

Blueberry and cranberry pangenomes as a resource for future genetic studies and breeding efforts

Domestication of cranberry and blueberry began in the United States in the early 1800s and 1900s, respectively, and in part owing to their flavors and health-promoting benefits are now cultivated and consumed worldwide. The industry continues to face a wide variety of production challenges (e.g. disease pressures) as well as a demand for higher-yielding cultivars with improved fruit quality characteristics. Unfortunately, molecular tools to help guide breeding efforts for these species have been relatively limited compared with those for other high-value crops. Here, we describe the construction and analysis of the first pangenome for both blueberry and cranberry. Our analysis of these pangenomes revealed both crops exhibit great genetic diversity, including the presence-absence variation of 48.4% genes in highbush blueberry and 47.0% genes in cranberry. Auxiliary genes, those not shared by all cultivars, are significantly enriched with molecular functions associated with disease resistance and the biosynthesis of specialized metabolites, including compounds previously associated with improving fruit quality traits. The discovery of thousands of genes, not present in the previous reference genomes for blueberry and cranberry, will serve as the basis of future research and as potential targets for future breeding efforts. The pangenome, as a multiple-sequence alignment, as well as individual annotated genomes, are publicly available for analysis on the Genome Database for Vaccinium - a curated and integrated web-based relational database. Lastly, the core-gene predictions from the pangenomes will serve useful to develop a community genotyping platform to guide future molecular breeding efforts across the family.

genomics↗

bioRxiv2023

Engineering living immunotherapeutic agents for improved cancer treatment

Bacteria-based biohybrid agents are emerging as a promising strategy for cancer therapy due to their ability to actively target tumors, trigger localized inflammation and induce tumor regression. There has been growing interest in using bacteria that are responsive to external cues, such as magnetic fields, to facilitate the formation of robust colonies in tumor the achieve the threshold for clinical efficacy. Several studied have demonstrated the potential of innately magnetically responsive bacteria, known as magnetotactic bacteria (MTB), as steerable agents, however, their immunostimulatory properties and therapeutic effects are yet to be explored. Here, we characterize key properties of human immune cell responses and the behavior of the MTB strain Magnetospirillum magneticum AMB-1 in physiological environments. This work investigates the ability of MTB to maintain magnetic properties, viability in whole blood, cytokine production by macrophages, and the ability to increase uptake of cancer cell material by dendritic cells. This study also explores the use of MTB-liposome complexes for effective delivery of therapeutic payloads. Overall, this study establishes the potential of MTB as a versatile, combined drug delivery platform for immune-mediated cancer therapy.

bioengineering↗

bioRxiv2021

Fine-mapping of nuclear compartments using ultra-deep Hi-C shows that active promoter and enhancer elements localize in the active A compartment even when adjacent sequences do not

Megabase-scale intervals of active, gene-rich and inactive, gene-poor chromatin are known to segregate, forming the A and B compartments. Fine mapping of the contents of these A and B compartments has been hitherto impossible, owing to the extraordinary sequencing depths required to distinguish between the long-range contact patterns of individual loci, and to the computational complexity of the associated calculations. Here, we generate the largest published in situ Hi-C map to date, spanning 33 billion contacts. We also develop a computational method, dubbed PCA of Sparse, SUper Massive Matrices (POSSUMM), that is capable of efficiently calculating eigenvectors for sparse matrices with millions of rows and columns. Applying POSSUMM to our Hi-C dataset makes it possible to assign loci to the A and B compartment at 500 bp resolution. We find that loci frequently alternate between compartments as one moves along the contour of the genome, such that the median compartment interval is only 12.5 kb long. Contrary to the findings in coarse-resolution compartment profiles, we find that individual genes are not uniformly positioned in either the A compartment or the B compartment. Instead, essentially all (95%) active gene promoters localize in the A compartment, but the likelihood of localizing in the A compartment declines along the body of active genes, such that the transcriptional termini of long genes (>60 kb) tend to localize in the B compartment. Similarly, nearly all active enhancers elements (95%) localize in the A compartment, even when the flanking sequences are comprised entirely of inactive chromatin and localize in the B compartment. These results are consistent with a model in which DNA-bound regulatory complexes give rise to phase separation at the scale of individual DNA elements.

genomics↗

bioRxiv2021

Symmetry breaking in the female germline cyst

In mammals and flies, only a limited number of cells in a multicellular female germline cyst become oocytes, but how the oocyte is selected is unknown. Here we show that the microtubule minus end-stabilizing protein, Patronin/CAMSAP marks the future Drosophila oocyte and is required for oocyte specification. The spectraplakin, Shot, recruits Patronin to the fusome, a branched structure extending into all cyst cells. Patronin stabilizes more microtubules in the cell with most fusome and this weak asymmetry is amplified by Dynein-dependent transport of Patronin-stabilized microtubules. This forms a polarized microtubule network, along which Dynein transports oocyte determinants into the presumptive oocyte. Thus, Patronin amplifies a weak fusome anisotropy to break cyst symmetry. These findings reveal a molecular mechanism of oocyte selection in the germline cyst.

developmental biology↗

bioRxiv2020

The COVID-19 PHARMACOME: Rational Selection of Drug Repurposing Candidates from Multimodal Knowledge Harmonization

The SARS-CoV-2 pandemic has challenged researchers at a global scale. The scientific communitys massive response has resulted in a flood of experiments, analyses, hypotheses, and publications, especially in the field of drug repurposing. However, many of the proposed therapeutic compounds obtained from SARS-CoV-2 specific assays are not in agreement and thus demonstrate the need for a singular source of COVID-19 related information from which a rational selection of drug repurposing candidates can be made. In this paper, we present the COVID-19 PHARMACOME, a comprehensive drug-target-mechanism graph generated from a compilation of 10 separate disease maps and sources of experimental data focused on SARS-CoV-2 / COVID-19 pathophysiology. By applying our systematic approach, we were able to predict the synergistic effect of specific drug pairs, such as Remdesivir and Thioguanosine or Nelfinavir and Raloxifene, on SARS-CoV-2 infection. Experimental validation of our results demonstrate that our graph can be used to not only explore the involved mechanistic pathways, but also to identify novel combinations of drug repurposing candidates.

bioinformatics↗

bioRxiv2020

Epigenomic Diversity of Cortical Projection Neurons in the Mouse Brain

Neuronal cell types are classically defined by their molecular properties, anatomy, and functions. While recent advances in single-cell genomics have led to high-resolution molecular characterization of cell type diversity in the brain, neuronal cell types are often studied out of the context of their anatomical properties. To better understand the relationship between molecular and anatomical features defining cortical neurons, we combined retrograde labeling with single-nucleus DNA methylation sequencing to link epigenomic properties of cell types to neuronal projections. We examined 11,827 single neocortical neurons from 63 cortico-cortical (CC) and cortico-subcortical long-distance projections. Our results revealed unique epigenetic signatures of projection neurons that correspond to their laminar and regional location and projection patterns. Based on their epigenomes, intra-telencephalic (IT) cells projecting to different cortical targets could be further distinguished, and some layer 5 neurons projecting to extra-telencephalic targets (L5-ET) formed separate subclusters that aligned with their axonal projections. Such separation varied between cortical areas, suggesting area-specific differences in L5-ET subtypes, which were further validated by anatomical studies. Interestingly, a population of CC projection neurons clustered with L5-ET rather than IT neurons, suggesting a population of L5-ET cortical neurons projecting to both targets (L5-ET+CC). We verified the existence of these neurons by labeling the axon terminals of CC projection neurons and observed clear labeling in ET targets including thalamus, superior colliculus, and pons. These findings highlight the power of single-cell epigenomic approaches to connect the molecular properties of neurons with their anatomical and projection properties.

neuroscience↗

bioRxiv2020

Monodisperse drops templated by 3D-structured microparticles

The ability to create uniform sub-nanoliter compartments using microfluidic control has enabled new approaches for analysis of single cells and molecules. However, specialized instruments or expertise have been required, slowing the adoption of these cutting-edge applications. Here, we show that 3D-structured microparticles with sculpted surface chemistries template uniformly-sized aqueous drops when simply mixed with two immiscible fluid phases. In contrast to traditional emulsions, particle-templated drops of a controlled volume occupy a minimum in the interfacial energy of the system, such that a stable monodisperse state results with simple and reproducible formation conditions. We describe techniques to manufacture microscale drop-carrier particles and show that emulsions created with these particles prevent molecular exchange, concentrating reactions within the drops, laying a foundation for sensitive compartmentalized assays with minimal instrumentation.

bioengineering↗

bioRxiv2018

DocClustering: An implementation of the novel PS-Document Clustering algorithm

AO_SCPCAPBSTRACTC_SCPCAPDocument clustering is widely used in science for data retrieval and organisation. DocClustering is developed to include and use a novel algorithm called PS-Document Clustering that has been first introduced in 2017. This method combines approaches of graph theory with state of the art NLP-technologies. This new heuristic has been shown to be superior to conventional algorithms and it provides - given a suiting similarity measure - a more accurate clustering on biological and medical data.\n\nSince the application is written for research on biomedical literature, interfaces for PubMed and SCAIView are available. In this brief report the source code as well as a short overview about the new features, novel heuristics and approaches are provided.\n\nThe software can be obtained from the authors or directly downloaded from GitHub, see https://github.com/jd-s/DocClustering.

bioinformatics↗