Biology subjects

Lock, C.

Publications and source records attributed to Lock, C..

6 recordsLinked to original sources

bioRxiv2026

Quantifying Evidence for Competing Biomedical Hypotheses using Large Language Models and Bayesian Analysis

Science fundamentally depends on the generation and testing of hypotheses, many of them controversial. An explosion in scientific literature has made evaluating hypotheses even within a domain a problem of scale, and risks slowing an already extensive consensus-building process. While this challenge has prompted interest in automated hypothesis evaluation tools, existing methods have not yet proven effective for comparing hypotheses. Here, we introduce KM-GPT-DCH, an algorithm that combines co-occurrence methods with large language models (LLMs) to develop a transparent and reproducible literature-based algorithm to compare controversial hypotheses using a structured scoring approach with Bayesian methods to estimate confidence. When testing the algorithm on historical controversial hypotheses previously decided, KM-GPT-DCH chooses the correct hypothesis with high confidence several years before the scientific community or public do so. We further apply the algorithm to compare twenty unresolved controversial hypothesis pairs providing guidance for future research. The method can help researchers and the public to evaluate biomedical hypotheses such as "Is it more likely that monoamine deficiency or inflammation causes depression?" It can also be used to assess and visualize historical trends in the scientific literature. A web-based implementation of the algorithm is freely available at https://skim.morgridge.org.

bioinformatics↗

bioRxiv2025

SKiM-GPT: Combining Biomedical Literature-Based Discovery with Large Language Model Hypothesis Evaluation

BackgroundGenerating and testing hypotheses is a critical aspect of biomedical science. Typically, researchers generate hypotheses by carefully analyzing available information and making logical connections, which are then tested. The accelerating growth of biomedical literature makes it increasingly difficult to keep pace with connections between biological entities emerging across biomedical research. Recently developed automated means of generating hypotheses can generate many more hypotheses than can be easily tested. One such approach involves literature based discovery (LBD) systems such as Serial KinderMiner (SKiM), which surfaces putative A B C links derived from term co occurrence. However, LBD systems leave three critical gaps: (i) they find statistical associations, not biological relationships; (ii) they can produce false positive leads; and (iii) they do not assess agreement with a hypothesis in question. As a result, LBD search results often require costly manual curation to be of practical utility to the researcher. Large language models (LLMs) have the potential to automate much of this curation step, but standalone LLMs are hampered by hallucinations, lack of transparency in information sources, and the inability to reference data not included in the training corpus. ResultsWe introduce SKiM-GPT, a retrieval-augmented generation (RAG) system that combines SKiMs co-occurrence search and retrieval with frontier LLMs to evaluate user- defined hypotheses. For every chosen A-B-C SKiM hit, SKiM-GPT retrieves appropriate PubMed abstract texts, filters out irrelevant abstracts with a fine-tuned relevance model, and prompts an LLM to evaluate the users hypothesis, given the relevant abstracts. Importantly, the SKiM-GPT system is transparent and human-verifiable: it displays the retrieved abstracts, the hypothesis score, and a justification for the score grounded in the texts and written in natural language. On a benchmark consisting of 14 disease-gene-drug hypotheses, SKiM-GPT achieves strong ordinal agreement with four expert biologists (Cohens {kappa} = 0.84), demonstrating its ability to replicate expert judgment. ConclusionsSKiM-GPT is open-source (https://github.com/stewart-lab/skimgpt) and available through a web interface (https://skim.morgridge.org), enabling both wet-lab and computational researchers to systematically and efficiently evaluate biomedical hypotheses at scale.

bioinformatics↗

bioRxiv2023

Transcriptomic signatures across a critical sedimentation threshold in a major reef-building coral

Sedimentation is a major cause of global near-shore coral reef decline. While negative impacts of sedimentation on coral reef community composition have been well documented, the effects of sedimentation on coral metabolism in situ have received comparatively little attention. Using transcriptomics, we identified gene expression patterns changing across a previously defined sedimentation threshold that was deemed critical due to changes in coral cover and community composition. We identified genes, pathways, and molecular processes associated with this transition that may allow corals, such as Porites lobata, to tolerate chronic, severe sedimentation and persist in turbid environments. Alternative energy generation pathways may help Porites lobata maintain a persistent stress response to survive while light and oxygen availability are diminished. We found evidence for the expression of genes linked to increased environmental sensing and cellular communication that likely allow Porites lobata to efficiently respond to sedimentation stress and associated pathogen challenges. Cell damage increases under stress; consequently, we found apoptosis pathways over-represented under severe sedimentation, a likely consequence of damaged cell removal to maintain colony integrity. The results presented here provide a framework for the response of Porites lobata to sedimentation stress under field conditions. Testing this framework and its related hypotheses using multi-omics approaches can further our understanding of metabolic plasticity and acclimation potential of corals to sedimentation and their resilience in turbid reef systems.

molecular biology↗

bioRxiv2023

Serial KinderMiner (SKiM) Discovers and Annotates Biomedical Knowledge Using Co-Occurrence and Transformer Models

BackgroundThe PubMed database contains more than 34 million articles; consequently, it is becoming increasingly difficult for a biomedical researcher to keep up-to-date with different knowledge domains. Computationally efficient and interpretable tools are needed to help researchers find and understand associations between biomedical concepts. The goal of literature-based discovery (LBD) is to connect concepts in isolated literature domains that would normally go undiscovered. This usually takes the form of an A-B-C relationship, where A and C terms are linked through a B term intermediate. Here we describe Serial KinderMiner (SKiM), an LBD algorithm for finding statistically significant links between an A term and one or more C terms through some B term intermediate(s). The development of SKiM is motivated by the the observation that there are only a few LBD tools that provide a functional web interface, and that the available tools are limited in one or more of the following ways: 1) they identify a relationship but not the type of relationship, 2) they do not allow the user to provide their own lists of B or C terms, hindering flexibility, 3) they do not allow for querying thousands of C terms (which is crucial if, for instance, the user wants to query connections between a disease and the thousands of available drugs), or 4) they are specific for a particular biomedical domain (such as cancer). We provide an open-source tool and web interface that improves on all of these issues. ResultsWe demonstrate SKiMs ability to discover useful A-B-C linkages in three control experiments: classic LBD discoveries, drug repurposing, and finding associations related to cancer. Furthermore, we supplement SKiM with a knowledge graph built with transformer machine-learning models to aid in interpreting the relationships between terms found by SKiM. Finally, we provide a simple and intuitive open-source web interface (https://skim.morgridge.org) with comprehensive lists of drugs, diseases, phenotypes, and symptoms so that anyone can easily perform SKiM searches. ConclusionsSKiM is a simple algorithm that can perform LBD searches to discover relationships between arbitrary user-defined concepts. SKiM is generalized for any domain, can perform searches with many thousands of C term concepts, and moves beyond the simple identification of an existence of a relationship; many relationships are given relationship type labels from our knowledge graph.

bioinformatics↗

bioRxiv2022

Photosystem regulation in coral-associated dinoflagellates (Symbiodiniaceae) is the primary mode for seasonal acclimation

Coral-associated dinoflagellates (Symbiodiniaceae) are photosynthetic endosymbionts that influence coral acclimation and adaptation, as indicated by photo-physiological plasticity (phenotypic variance) in response to environmental change. Symbiont shuffling (shifts in endosymbiont community composition), changes in endosymbiont cell density, and phenotypic plasticity have all been proposed as mechanisms to adjust to environmental change. However, few studies have been able to partition which of the three strategies were responsible for observed phenotypic variance. Therefore, we quantified the biodiversity, cell density, and phenotypic variance of single cells for Acropora pulchra-associated Symbiodiniaceae assemblages. Using a combination of metabarcoding and flow cytometry, we simultaneously characterized Symbiodiniaceae assemblages at the community (biodiversity), population (cell density), and individual level (phenotype) under natural environmental conditions to determine whether phenotypic variation of Symbiodiniaceae communities is concomitant with either symbiont shuffling, changes in cell density, or phenotypic plasticity. Symbiodiniaceae assemblages displayed season-specific phenotypic variance, while biodiversity was geographically structured and cell density showed limited data structure. Based on these patterns, we reveal that phenotypic plasticity of individual Symbiodiniaceae cells is the source of a phenotypic variation, thus indicating that phenotypic plasticity is a mechanism for rapid acclimation to mild environmental change.

ecology↗

bioRxiv2022

Calcium homeostasis disruption initiates rapid growth after micro-fragmentation in the scleractinian coral Porites lobata

Coral reefs are ecosystems under increasing threat from global climate change. Coral restoration is a tool for preserving biological and ecological function of coral reefs by mitigating coral loss and maintaining the structural integrity and complexity of reefs. To generate the necessary stock for coral restoration, larger coral colonies are usually fragmented to generate smaller specimens for outplanting, taking advantage of the high regenerative ability of corals. In this study, we utilized RNA-seq technology to understand the physiological responses of Porites lobata colonies to physical fragmentation and outplanting, which have thus far not been characterized. Our results demonstrate that P. lobata fragments undergoing physical injury recover through two distinct phases: rapid wound regeneration of the cut margins, followed by a slower growth phase that cements the colony to the substrate. Our study found rapid physiological responses to acute physical injury and outplanting in the coral host that involved significantly increased energy production, calcium homeostasis disruption, and Endoplasmic Reticulum (ER) stress leading to increased antioxidant expression and rates of protein turnover. Our results suggest that phosphoinositide-mediated acute calcium homeostasis disruption stimulates wound recovery processes in response to physical injury. Symbiont gene expression revealed extremely low gene differences in response to fragmentation, growth, and outplanting. These results provide insight into the physiological mechanisms that allow for rapid wound healing and stabilization in response to physical injury in corals.

genetics↗