pwc-archive/papers-with-abstracts huggingface dataset:
From FAIR to CURE: Guidelines for Computational Models of Biological Systems
Authors: Herbert M. Sauro, Eran Agmon, Michael L. Blinov, John H. Gennari, Joe Hellerstein, Adel Heydarabadipour, Peter Hunter, Bartholomew E. Jardine, Elebeoba May, David P. Nickerson, Lucian P. Smith, Gary D Bader, Frank Bergmann, Patrick M. Boyle, Andreas Drager, James R. Faeder, Song Feng, Juliana Freire, Fabian Frohlich, James A. Glazier, Thomas E. Gorochowski, Tomas Helikar, Stefan Hoops, Princess Imoukhuede, Sarah M. Keating, Matthias Konig, Reinhard Laubenbacher, Leslie M. Loew, Carlos F. Lopez, William W. Lytton, Andrew McCulloch, Pedro Mendes, Chris J. Myers, Jerry G. Myers, Lealem Mulugeta, Anna Niarakis, David D. van Niekerk, Brett G. Olivier, Alexander A. Patrie, Ellen M. Quardokus, Nicole Radde, Johann M. Rohwer, Sven Sahle, James C. Schaff, T. J. Sego, Janis Shin, Jacky L. Snoep, Rajanikanth Vadigepalli, H. Steve Wiley, Dagmar Waltemath, Ion Moraru
Date: 2025-02-21 00:00:00
Tasks: Uncertainty Quantification

Guidelines for managing scientific data have been established under the FAIR principles requiring that data be Findable, Accessible, Interoperable, and Reusable. In many scientific disciplines, especially computational biology, both data and models are key to progress. For this reason, and recognizing that such models are a very special type of 'data', we argue that computational models, especially mechanistic models prevalent in medicine, physiology and systems biology, deserve a complementary set of guidelines. We propose the CURE principles, emphasizing that models should be Credible, Understandable, Reproducible, and Extensible. We delve into each principle, discussing verification, validation, and uncertainty quantification for model credibility; the clarity of model descriptions and annotations for understandability; adherence to standards and open science practices for reproducibility; and the use of open standards and modular code for extensibility and reuse. We outline recommended and baseline requirements for each aspect of CURE, aiming to enhance the impact and trustworthiness of computational models, particularly in biomedical applications where credibility is paramount. Our perspective underscores the need for a more disciplined approach to modeling, aligning with emerging trends such as Digital Twins and emphasizing the importance of data and modeling standards for interoperability and reuse. Finally, we emphasize that given the non-trivial effort required to implement the guidelines, the community moves to automate as many of the guidelines as possible.
Cytogenetic, Hematobiochemical, and Histopathological Assessment of Albino Rats (Rattus norvegicus) Fed on Gluten Extracts
Authors: Tajudeen Yahaya, Esther Oladele, Ufuoma Shemishere, Daniel Anyebe, Haliru Abdullahi, Maryam Lawal, Rufai Ahmad
Date: 2025-02-20 00:00:00

Background: Literature shows that most of the information on the toxicity of gluten is generated from survey and observational studies, resulting in inconsistent outcomes and a decrease in the acceptability of gluten-rich foods. To determine gluten's safety, an in-depth in vitro and in vivo toxicological examination is required. This enables scientists to come up with ameliorative strategies if it turns out to have side effects, and consumers' trust can be restored. Objectives: The objective of this study was to assess the toxicity of gluten extracts on albino rats (Rattus norvegicus). Materials and Methods: Twenty-four rats were randomly selected and divided into four groups, each comprising six rats. Group 1 (control) rats were fed on pellet feeds and groups 2, 3, and 4 were fed on daily dosages of 0.5, 1.0, and 1.5 g gluten extracts, respectively. The rats' body weights and reactions were observed for 90 days before blood samples were collected for hematobiochemical and micronucleus tests. Histopathological examinations of the liver and kidneys were also performed. Results: There was no difference (P > 0.05) in body weight, blood glucose level, or micronuclei between the control and treated rats. The lymphocytes, alkaline phosphatase, alanine transaminase, total protein, and calcium ions of the test rats were all significantly (P < 0.05) altered but remained within the normal ranges. Other hematobiochemical parameters, including packed cell volume, hemoglobin, white and red blood cells, aspartate transaminase, albumin, sodium ions, potassium ions, chloride ions, and urea, revealed no marked changes. The treated rats' livers and kidneys showed no histopathological changes. Conclusion: Gluten had no adverse effects. However, it altered hematobiochemical parameters, particularly the lymphocytes, alkaline phosphatase, alanine transaminase, total protein, and calcium ions.
Utilizing Sequential Information of General Lab-test Results and Diagnoses History for Differential Diagnosis of Dementia
Authors: Yizong Xing, Dhita Putri Pratama, yuke wang, Yufan Zhang, Brian E. Chapman
Date: 2025-02-21 00:00:00
Tasks: Diagnostic

Early diagnosis of Alzheimer's Disease (AD) faces multiple data-related challenges, including high variability in patient data, limited access to specialized diagnostic tests, and overreliance on single-type indicators. These challenges are exacerbated by the progressive nature of AD, where subtle pathophysiological changes often precede clinical symptoms by decades. To address these limitations, this study proposes a novel approach that takes advantage of routinely collected general laboratory test histories for the early detection and differential diagnosis of AD. By modeling lab test sequences as "sentences", we apply word embedding techniques to capture latent relationships between tests and employ deep time series models, including long-short-term memory (LSTM) and Transformer networks, to model temporal patterns in patient records. Experimental results demonstrate that our approach improves diagnostic accuracy and enables scalable and costeffective AD screening in diverse clinical settings.
Uncertainty and sensitivity analysis of hair growth duration in human scalp follicles under normal and alopecic conditions
Authors: Atanaska Dobreva, Damon Comer, N. G. Cogan, Ralf Paus
Date: 2025-02-20 00:00:00
Tasks: Sensitivity, Uncertainty Quantification

Hair follicles constantly cycle through phases of growth, regression and rest, as matrix keratinocytes (MKs), the cells producing hair fibers, proliferate, and then undergo spontaneous apoptosis. Damage to MKs and perturbations in their normal dynamics result in a shortened growth phase, leading to hair loss. Two common factors causing such disruption are hormonal imbalance and attacks by the immune system. Androgenetic alopecia (AGA) is hair loss caused by high sensitivity to androgens, and alopecia areata (AA) is hair loss caused by an autoimmune reaction against MKs. In this study, we inform a mathematical model for the human hair cycle with experimental data for the lengths of hair cycle phases available from male control subjects and subjects with AGA. We also, connect a mathematical model for AA with estimates for the duration of hair cycle phases obtained from the literature. Subsequently, with each model we perform parameter screening, uncertainty quantification and global sensitivity analysis and compare the results within and between the control and AGA subject groups as well as among AA, control and AGA conditions. The findings reveal that in AGA subjects there is greater uncertainty associated with the duration of hair growth than in control subjects and that, compared to control and AGA conditions, in AA it is more certain that longer hair growth phase could not be expected. The comparison of results also indicates that in AA lower proliferation of MKs and weaker communication of the dermal papilla with MKs via signaling molecules could be expected than in normal and AGA conditions, and in AA stronger inhibition of MK proliferation by regulatory molecules could be expected than in AGA. Finally, the global sensitivity analysis highlights the process of MK apoptosis as highly impactful for the length of hair growth only in the AA case, but not for control and AGA conditions.
Reward-Guided Iterative Refinement in Diffusion Models at Test-Time with Applications to Protein and DNA Design
Authors: Masatoshi Uehara, Xingyu Su, Yulai Zhao, Xiner Li, Aviv Regev, Shuiwang Ji, Sergey Levine, Tommaso Biancalani
Date: 2025-02-20 00:00:00
Tasks: Denoising, Evolutionary Algorithms

To fully leverage the capabilities of diffusion models, we are often interested in optimizing downstream reward functions during inference. While numerous algorithms for reward-guided generation have been recently proposed due to their significance, current approaches predominantly focus on single-shot generation, transitioning from fully noised to denoised states. We propose a novel framework for inference-time reward optimization with diffusion models inspired by evolutionary algorithms. Our approach employs an iterative refinement process consisting of two steps in each iteration: noising and reward-guided denoising. This sequential refinement allows for the gradual correction of errors introduced during reward optimization. Besides, we provide a theoretical guarantee for our framework. Finally, we demonstrate its superior empirical performance in protein and cell-type-specific regulatory DNA design. The code is available at \href{https://github.com/masa-ue/ProDifEvo-Refinement}{https://github.com/masa-ue/ProDifEvo-Refinement}.
Fast and Accurate Blind Flexible Docking
Authors: Zizhuo Zhang, Lijun Wu, Kaiyuan Gao, Jiangchao Yao, Tao Qin, Bo Han
Date: 2025-02-20 00:00:00
Tasks: Blind Docking, Computational Efficiency, Drug Discovery, Molecular Docking, Multi-Task Learning

Molecular docking that predicts the bound structures of small molecules (ligands) to their protein targets, plays a vital role in drug discovery. However, existing docking methods often face limitations: they either overlook crucial structural changes by assuming protein rigidity or suffer from low computational efficiency due to their reliance on generative models for structure sampling. To address these challenges, we propose FABFlex, a fast and accurate regression-based multi-task learning model designed for realistic blind flexible docking scenarios, where proteins exhibit flexibility and binding pocket sites are unknown (blind). Specifically, FABFlex's architecture comprises three specialized modules working in concert: (1) A pocket prediction module that identifies potential binding sites, addressing the challenges inherent in blind docking scenarios. (2) A ligand docking module that predicts the bound (holo) structures of ligands from their unbound (apo) states. (3) A pocket docking module that forecasts the holo structures of protein pockets from their apo conformations. Notably, FABFlex incorporates an iterative update mechanism that serves as a conduit between the ligand and pocket docking modules, enabling continuous structural refinements. This approach effectively integrates the three subtasks of blind flexible docking-pocket identification, ligand conformation prediction, and protein flexibility modeling-into a unified, coherent framework. Extensive experiments on public benchmark datasets demonstrate that FABFlex not only achieves superior effectiveness in predicting accurate binding modes but also exhibits a significant speed advantage (208 $\times$) compared to existing state-of-the-art methods. Our code is released at https://github.com/tmlr-group/FABFlex.
Optimizing Gene-Based Testing for Antibiotic Resistance Prediction
Authors: David Hagerman, Anna Johnning, Roman Naeem, Fredrik Kahl, Erik Kristiansson, Lennart Svensson
Date: 2025-02-19 00:00:00
Tasks: Diagnostic, Prediction, Reinforcement Learning (RL)

Antibiotic Resistance (AR) is a critical global health challenge that necessitates the development of cost-effective, efficient, and accurate diagnostic tools. Given the genetic basis of AR, techniques such as Polymerase Chain Reaction (PCR) that target specific resistance genes offer a promising approach for predictive diagnostics using a limited set of key genes. This study introduces GenoARM, a novel framework that integrates reinforcement learning (RL) with transformer-based models to optimize the selection of PCR gene tests and improve AR predictions, leveraging observed metadata for improved accuracy. In our evaluation, we developed several high-performing baselines and compared them using publicly available datasets derived from real-world bacterial samples representing multiple clinically relevant pathogens. The results show that all evaluated methods achieve strong and reliable performance when metadata is not utilized. When metadata is introduced and the number of selected genes increases, GenoARM demonstrates superior performance due to its capacity to approximate rewards for unseen and sparse combinations. Overall, our framework represents a major advancement in optimizing diagnostic tools for AR in clinical settings.
UNGT: Ultrasound Nasogastric Tube Dataset for Medical Image Analysis
Authors: Zhaoshan Liu, Chau Hung Lee, Qiujie Lv, Nicole Kessa Wee, Lei Shen
Date: 2025-02-19 00:00:00
Tasks: Medical Image Analysis

We develop a novel ultrasound nasogastric tube (UNGT) dataset to address the lack of public nasogastric tube datasets. The UNGT dataset includes 493 images gathered from 110 patients with an average image resolution of approximately 879 $\times$ 583. Four structures, encompassing the liver, stomach, tube, and pancreas are precisely annotated. Besides, we propose a semi-supervised adaptive-weighting aggregation medical segmenter to address data limitation and imbalance concurrently. The introduced adaptive weighting approach tackles the severe unbalanced challenge by regulating the loss across varying categories as training proceeds. The presented multiscale attention aggregation block bolsters the feature representation by integrating local and global contextual information. With these, the proposed AAMS can emphasize sparse or small structures and feature enhanced representation ability. We perform extensive segmentation experiments on our UNGT dataset, and the results show that AAMS outperforms existing state-of-the-art approaches to varying extents. In addition, we conduct comprehensive classification experiments across varying state-of-the-art methods and compare their performance. The dataset and code will be available upon publication at https://github.com/NUS-Tim/UNGT.
Variational phylogenetic inference with products over bipartitions
Authors: Evan Sidrow, Alexandre Bouchard-Côté, Lloyd T. Elliott
Date: 2025-02-21 00:00:00

Bayesian phylogenetics requires accurate and efficient approximation of posterior distributions over trees. In this work, we develop a variational Bayesian approach for ultrametric phylogenetic trees. We present a novel variational family based on coalescent times of a single-linkage clustering and derive a closed-form density of the resulting distribution over trees. Unlike existing methods for ultrametric trees, our method performs inference over all of tree space, it does not require any Markov chain Monte Carlo subroutines, and our variational family is differentiable. Through experiments on benchmark genomic datasets and an application to SARS-CoV-2, we demonstrate that our method achieves competitive accuracy while requiring significantly fewer gradient evaluations than existing state-of-the-art techniques.
Judging It, Washing It: Scoring and Greenwashing Corporate Climate Disclosures using Large Language Models
Authors: Marianne Chuang, Gabriel Chuang, Cheryl Chuang, John Chuang
Date: 2025-02-20 00:00:00

We study the use of large language models (LLMs) to both evaluate and greenwash corporate climate disclosures. First, we investigate the use of the LLM-as-a-Judge (LLMJ) methodology for scoring company-submitted reports on emissions reduction targets and progress. Second, we probe the behavior of an LLM when it is prompted to greenwash a response subject to accuracy and length constraints. Finally, we test the robustness of the LLMJ methodology against responses that may be greenwashed using an LLM. We find that two LLMJ scoring systems, numerical rating and pairwise comparison, are effective in distinguishing high-performing companies from others, with the pairwise comparison system showing greater robustness against LLM-greenwashed responses.
Joint Registration and Conformal Prediction for Partially Observed Functional Data
Authors: Fangyi Wang, Sebastian Kurtek, Yuan Zhang
Date: 2025-02-20 00:00:00
Tasks: Conformal Prediction, Prediction, Prediction Intervals

Predicting missing segments in partially observed functions is challenging due to infinite-dimensionality, complex dependence within and across observations, and irregular noise. These challenges are further exacerbated by the existence of two distinct sources of variation in functional data, termed amplitude (variation along the $y$-axis) and phase (variation along the $x$-axis). While registration can disentangle them from complete functional data, the process is more difficult for partial observations. Thus, existing methods for functional data prediction often ignore phase variation. Furthermore, they rely on strong parametric assumptions, and require either precise model specifications or computationally intensive techniques, such as bootstrapping, to construct prediction intervals. To tackle this problem, we propose a unified registration and prediction approach for partially observed functions under the conformal prediction framework, which separately focuses on the amplitude and phase components. By leveraging split conformal methods, our approach integrates registration and prediction while ensuring exchangeability through carefully constructed predictor-response pairs. Using a neighborhood smoothing algorithm, the framework produces pointwise prediction bands with finite-sample marginal coverage guarantees under weak assumptions. The method is easy to implement, computationally efficient, and suitable for parallelization. Numerical studies and real-world data examples clearly demonstrate the effectiveness and practical utility of the proposed approach.
Knowledge Graphs: The Future of Data Integration and Insightful Discovery
Authors: Saher Mohamed, Kirollos Farah, Abdelrahman Lotfy, Kareem Rizk, Abdelrahman Saeed, Shahenda Mohamed, Ghada Khouriba, Tamer Arafa
Date: 2024-12-17 00:00:00
Tasks: Chatbot, Data Integration, Knowledge Base Completion, Knowledge Graphs, named-entity-recognition, Named Entity Recognition, Question Answering

Knowledge graphs are an efficient method for representing and connecting information across various concepts, useful in reasoning, question answering, and knowledge base completion tasks. They organize data by linking points, enabling researchers to combine diverse information sources into a single database. This interdisciplinary approach helps uncover new research questions and ideas. Knowledge graphs create a web of data points (nodes) and their connections (edges), which enhances navigation, comprehension, and utilization of data for multiple purposes. They capture complex relationships inherent in unstructured data sources, offering a semantic framework for diverse entities and their attributes. Strategies for developing knowledge graphs include using seed data, named entity recognition, and relationship extraction. These graphs enhance chatbot accuracy and include multimedia data for richer information. Creating high-quality knowledge graphs involves both automated methods and human oversight, essential for accurate and comprehensive data representation.
The Process of Categorical Clipping at the Core of the Genesis of Concepts in Synthetic Neural Cognition
Authors: Michael Pichat William Pogrund, Armanush Gasparian, Paloma Pichat, Samuel Demarchi, Michael Veillet-Guillem, Martin Corbet, Théo Dasilva
Date: 2025-01-21 00:00:00

This article investigates, within the field of neuropsychology of artificial intelligence, the process of categorical segmentation performed by language models. This process involves, across different neural layers, the creation of new functional categorical dimensions to analyze the input textual data and perform the required tasks. Each neuron in a multilayer perceptron (MLP) network is associated with a specific category, generated by three factors carried by the neural aggregation function: categorical priming, categorical attention, and categorical phasing. At each new layer, these factors govern the formation of new categories derived from the categories of precursor neurons. Through a process of categorical clipping, these new categories are created by selectively extracting specific subdimensions from the preceding categories, constructing a distinction between a form and a categorical background. We explore several cognitive characteristics of this synthetic clipping in an exploratory manner: categorical reduction, categorical selectivity, separation of initial embedding dimensions, and segmentation of categorical zones.
Logic.py: Bridging the Gap between LLMs and Constraint Solvers
Authors: Pascal Kesseli, Peter O'Hearn, Ricardo Silveira Cabral
Date: 2025-02-17 00:00:00
Tasks: Language Modeling, Language Modelling

We present a novel approach to formalise and solve search-based problems using large language models, which significantly improves upon previous state-of-the-art results. We demonstrate the efficacy of this approach on the logic puzzles benchmark ZebraLogicBench. Instead of letting the LLM attempt to directly solve the puzzles, our method prompts the model to formalise the problem in a logic-focused domain-specific language (DSL) called Logic.py. This formalised representation is then solved using a constraint solver, leveraging the strengths of both the language model and the solver. Our approach achieves a remarkable 65% absolute improvement over the baseline performance of Llama 3.1 70B on ZebraLogicBench, setting a new state-of-the-art with an accuracy of over 90%. This significant advancement demonstrates the potential of combining language models with domain-specific languages and auxiliary tools on traditionally challenging tasks for LLMs.
One for All: A General Framework of LLMs-based Multi-Criteria Decision Making on Human Expert Level
Authors: Hui Wang, Fafa Zhang, Chaoxu Mu
Date: 2025-02-17 00:00:00
Tasks: All, Decision Making

Multi-Criteria Decision Making~(MCDM) is widely applied in various fields, using quantitative and qualitative analyses of multiple levels and attributes to support decision makers in making scientific and rational decisions in complex scenarios. However, traditional MCDM methods face bottlenecks in high-dimensional problems. Given the fact that Large Language Models~(LLMs) achieve impressive performance in various complex tasks, but limited work evaluates LLMs in specific MCDM problems with the help of human domain experts, we further explore the capability of LLMs by proposing an LLM-based evaluation framework to automatically deal with general complex MCDM problems. Within the framework, we assess the performance of various typical open-source models, as well as commercial models such as Claude and ChatGPT, on 3 important applications, these models can only achieve around 60\% accuracy rate compared to the evaluation ground truth. Upon incorporation of Chain-of-Thought or few-shot prompting, the accuracy rates rise to around 70\%, and highly depend on the model. In order to further improve the performance, a LoRA-based fine-tuning technique is employed. The experimental results show that the accuracy rates for different applications improve significantly to around 95\%, and the performance difference is trivial between different models, indicating that LoRA-based fine-tuned LLMs exhibit significant and stable advantages in addressing MCDM tasks and can provide human-expert-level solutions to a wide range of MCDM challenges.