Jupyter Notebook Research Articles

Detection of Source Code Plagiarism Utilizing an Approach Based on Machine Learning

Academic institutions, which often publish papers and journals, are ideal testing grounds for the efficacy of counterfeit detection methods. Plagiarism occurs when someone uses the words of another writer without giving that writer proper credit. The proliferation of freeware text editors and the increasing availability of scientific materials online have made the detection of plagiarism a pressing concern; however, the detection of plagiarism in the source code presents a particularly difficult problem. Plagiarism detection algorithms for identification systems and software source code have been the subject of numerous academic investigations. The proposed method combines TF-IDF transformations with K-means clustering to achieve a 99.2% accuracy rate when detecting instances of plagiarism in the source code. This is because it groups similar lines of code together. On the other hand, in comparison to the outcomes produced by the random forest algorithm, the ones that it generates are significantly better. The performance of the MOSS system that was already in place was inferior to that of the system that was used for 90% and 80% of the training set. When contrasting the results, some parameters for evaluation that are considered include precision, recall, and F-measure. The proposed system is implemented in Jupyter Notebook 7 and Python. Also, graphic user interface is designed and implemented to give user friendly experience to the users.

TEMPLATES: A Robust Outlier Rejection Method for JWST/NIRSpec Integral Field Spectroscopy

We describe a custom outlier rejection algorithm for JWST/NIRSpec integral field spectroscopy. This method uses a layered sigma clipping approach that adapts clipping thresholds based upon the spatial profile of the science target. We find that this algorithm produces a robust outlier rejection while simultaneously preserving the signal of the science target. Originally developed as a response to unsatisfactory initial performance of the jwst pipeline outlier detection step, this method works either as a standalone solution, or as a supplement to the current pipeline software. Comparing leftover (i.e., not flagged) artifacts with the current pipeline’s outlier detection step, we find that our method results in one fifth as many residual artifacts as the jwst pipeline. However, we find a combination of both methods removes nearly all artifacts—an approach that takes advantage of both our algorithm’s robust outlier rejection and the pipeline’s use of individual dithers. This combined approach is what the TEMPLATES Early Release Science team has converged upon for our NIRSpec observations. Finally, we publicly release the code and Jupyter notebooks for the custom outlier rejection algorithm.

Breast cancer diagnosis through an optimization‐driven multispectral gamma correction (ODMGC)

SummaryThe Optimization‐Driven Multispectral Gamma Correction (ODMGC) algorithm overcomes challenges in gathering subtle information and detecting cancer in dense breast thermograms. This algorithm enhances the accuracy of true positives and true negatives while minimising false negatives and false positives. The ODMGC involves a multi‐step optimisation process that categorises grey‐scale images of breast thermograms based on mean brightness. Then, based on the grey levels of the pixels, we grouped each categorisation into sub‐regions. Followed by each group has undergone individually optimised base enhancement. This process enhances the contrast between cancerous and normal tissues, eliminates over‐ and under‐enhancement, and supports breast tumour diagnosis. The optimised‐based enhancement images serve as a reference point for the histogram specification of the V component of the thermograms in the HSV (Hue, Saturation, and Value) model. Further, we evaluated the proposed model using both qualitative and quantitative measures. Finally, using dimension‐reduced significant Grey‐Level Co‐occurrence Matrix (GLCM) features, we validated the results with a Random Forest (RF) classifier. The algorithm was successfully implemented in MATLAB 2020a, and the classifier was developed in Jupyter Notebook using Python. The subjective comparison confirmed the proposed method's superior resolution in normal and malignant cases. The classifier results showed an accuracy of 96.4%, sensitivity of 98.1%, and specificity of 96.9%.

Prediction of Rheological Parameters of Polymers by Machine Learning Methods

Introduction. All polymer materials and composites based on them are characterized by pronounced rheological properties, the prediction of which is one of the most critical tasks of polymer mechanics. Machine learning methods open up great opportunities in predicting the rheological parameters of polymers. Previously, studies were conducted on the construction of predictive models using artificial neural networks and the CatBoost algorithm. Along with these methods, due to the capability to process data with highly nonlinear dependences between features, machine learning methods such as the k-nearest neighbor method, and the support vector machine (SVM) method, are widely used in related areas. However, these methods have not been applied to the problem discussed in this article before. The objective of the research was to develop a predictive model for evaluating the rheological parameters of polymers using artificial intelligence methods by the example of polyvinyl chloride.Materials and Methods. This paper used k-nearest neighbor method and the support vector machine to determine the rheological parameters of polymers based on stress relaxation curves. The models were trained on synthetic data generated from theoretical relaxation curves constructed using the nonlinear Maxwell-Gurevich equation. The input parameters of the models were the amount of deformation at which the experiment was performed, the initial stress, the stress at the end of the relaxation process, the relaxation time, and the conditional end time of the process. The output parameters included velocity modulus and initial relaxation viscosity coefficient. The models were developed in the Jupyter Notebook environment in Python.Results. New predictive models were built to determine the rheological parameters of polymers based on artificial intelligence methods. The proposed models provided high quality prediction. The model quality metrics in the SVR algorithm were: MAE – 1.67 and 0.72; MSE – 5.75 and 1.21; RMSE – 1.67 and 1.1; MAPE – 8.92 and 7.3 for the parameters of the initial relaxation viscosity and velocity modulus, respectively, with the coefficient of determination R2 – 0.98. The developed models showed an average absolute percentage error in the range of 5.9 – 8.9%. In addition to synthetic data, the developed models were also tested on real experimental data for polyvinyl chloride in the temperature range from 20° to 60°C.Discussion and Conclusion. The approbation of the developed models on real experimental curves showed a high quality of their approximation, comparable to other methods. Thus, the k-nearest neighbor algorithm and SVM can be used to predict the rheological parameters of polymers as an alternative to artificial neural networks and the CatBoost algorithm, requiring less effort to preset adjustment. At the same time, in this research, the SVM method turned out to be the most preferred method of machine learning, since it is more effective in processing a large number of features

PyCoM: a python library for large-scale analysis of residue-residue coevolution data.

Computational methods to detect correlated amino acid positions in proteins have become a valuable tool to predict intra- and inter-residue protein contacts, protein structures, and effects of mutation on protein stability and function. While there are many tools and webservers to compute coevolution scoring matrices, there is no central repository of alignments and coevolution matrices for large-scale studies and pattern detection leveraging on biological and structural annotations already available in UniProt. We present a Python library, PyCoM, which enables users to query and analyze coevolution matrices and sequence alignments of 457622 proteins, selected from UniProtKB/Swiss-Prot database (length ≤ 500 residues), from a precompiled coevolution matrix database (PyCoMdb). PyCoM facilitates the development of statistical analyses of residue coevolution patterns using filters on biological and structural annotations from UniProtKB/Swiss-Prot, with simple access to PyCoMdb for both novice and advanced users, supporting Jupyter Notebooks, Python scripts, and a web API access. The resource is open source and will help in generating data-driven computational models and methods to study and understand protein structures, stability, function, and design. PyCoM code is freely available from https://github.com/scdantu/pycom and PyCoMdb and the Jupyter Notebook tutorials are freely available from https://pycom.brunel.ac.uk.

Abstract 4440: Topographical investigation of metabolites in excised squares (TIMES2): Comprehensive cross-sectional metabolite quantification of pancreatic cancer in vivo

Abstract Purpose: Pancreatic cancer is a lethal malignancy characterized by complex intratumoral metabolic reprogramming and intercellular nutrient sharing between cells in the tumor microenvironment (TME) that promote pancreatic cancer progression. However, this crosstalk, as well as regional variation in perfusion and oxygenation, can lead to metabolic heterogeneity that has not been appreciated by metabolomics of whole tumors. Here we quantify amino acids and tricarboxylic acid cycle (TCA) intermediates using a novel methodology that allows us to portray global tumor metabolite heterogeneity in a tumor. Methods: Human PaTu-8902 or murine HY19636 (from female KPC mice LSL-KrasG12D; p53 L/+, Ptf1a-Cre+) pancreatic cancer cell lines were orthotopically injected into pancreata of NCr nude mice (n=3) or C57BL/6 mice (n=2). Mice were euthanized after 3-5 weeks and tumors were harvested. Tumor slices were further sectioned into 1mm x 1mm x 1mm cubes using a custom-made multisectional slicing device and each cube location was recorded. Each cube was extracted using methanol, water, and chloroform with labelled amino acid standards, derivitized, and resolved using gas chromatography-mass spectrometry (DB-35MS column with Agilent 7890B gas chromatograph coupled to a single quadrupole 5977B mass spectrometer). 22 metabolites (15 amino acids, 5 TCA intermediates, lactate, and pyruvate) were identified by unique fragments and retention time compared to known standards. Peaks were picked using OpenChrom and analyzed using MATLAB. Data was analyzed using Graphpad Prism. Principal Component Analysis (PCA) was visualized using Python on a Jupyter notebook. Results: Both orthotopic human and murine pancreatic tumors demonstrated striking levels of intratumoral metabolite heterogeneity. Glycine, glutamine, and proline were the amino acids with the highest coefficient of variance, while leucine, isoleucine, and serine had the lowest coefficient of variance. α-ketoglutarate and succinate were the TCA intermediates with highest coefficient of variance. Lactate had the lowest coefficient of variance among all examined metabolites. Spatial mapping of each metabolite demonstrated distinct regions with varying abundance levels of metabolites. PCA demonstrated 75% of variance was carried by PC1 and 10% carried by PC2. Conclusions: This study reveals insights into the degree of intratumoral heterogeneity present in pancreatic tumors that illustrate the difficulty of in vivo metabolomics analysis and suggest that high-resolution (single cell) metabolomics techniques will be critical to study metabolism in the complex TME. Citation Format: Peter Yu, Robert Banh, Albert Sohn, Stephen Martis, Douglas Biancur, Keisuke Yamamoto, Elaine Lin, Alec Kimmelman. Topographical investigation of metabolites in excised squares (TIMES2): Comprehensive cross-sectional metabolite quantification of pancreatic cancer in vivo [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4440.

Abstract 4848: An analysis of black women in the Deep South with triple negative breast cancer and the social determinants of health

Abstract Background: Triple negative breast cancer (TNBC) has a higher incidence both of diagnosis and mortality among Black women. This research aims to identify specific social determinants of health among African American women in the Deep South with breast cancer. The biological presentation of disease accounts for approximately forty percent of the Experience with the health care providers, access to greenspace, and the quality of infrastructure, such as sidewalks and streetlights evaluate environmental trends associated with the outcomes of Black women in the Deep South who have been diagnosed with TNBC. Methods: Survey data was derived from the All of Us Research Program Database with inclusion criteria of Black women from Alabama, Georgia, Mississippi, and Louisiana who are 18 or older, born a female, and have been diagnosed with TNBC. Selected questions from the SDoH survey examined infrastructure, interactions with healthcare providers, and perceptions of community dynamics. Participants were identified via the cohort selection tool and further subdivided based on zip codes within the target states. Python was used in Jupyter Notebook to analyze the data the AoURP Workbench, responses were organized according to their corresponding survey questions. Participant responses were then extracted and quantified, facilitating the interpretation of respondents’ answers on a larger scale. Results: Three hundred and twenty women met the criteria of being born female, being black, being diagnosed with TNBC, and living in the Deep South. Of the topics examined, infrastructure was the area where participants’ feedback indicated the largest socioeconomic deficit. Forty-eight and 49%, respectively. Indicated that did not have access to greenspace nor did not live within 10-15-minute walking distance of a transit stop. Thirty percent reported that their doctor or nurse does not listen to what they are saying. Conclusion: Quality of life and interaction with members of the health care team associated with poorer health outcomes for Black women in the Deep South. These preliminary results indicate that those diagnosed with TNBC are impacted by the social determinants of health, specifically, proximity to public transportation, quality healthcare, recreational facilities, and infrastructure which suggests that basic needs are not met. Citation Format: Lauren K. Evans, Ivan T. Jubilee, KiTani Parker Lemieux. An analysis of black women in the Deep South with triple negative breast cancer and the social determinants of health [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4848.

Abstract 7426: The GenePattern ecosystem for cancer bioinformatics

Abstract As the availability of genomic data and analysis tools from large-scale cancer initiatives continues to increase, with single-cell studies adding new dimensions to the potential scientific insights, the need has become more urgent for a software environment that supports the rapid pace of cancer data science. The GenePattern ecosystem, first introduced in 2004 and updated continually to support the changing needs of cancer genomics research, supports the analytical, computational, and reproducibility needs of the world-wide cancer analysis community.The GenePattern server, available at www.genepattern.org, provides hundreds of analysis methods for scientists at all levels of computational sophistication, with the only requirement for use being a web browser. The GenePattern server offers bulk and single-cell RNA-seq, copy number variation, flow cytometry, network analysis, general machine learning, gene set enrichment analysis, proteomics, and many other modalities. Analysis steps can be linked together into pipelines that can then be edited, shared, and made public. All parameters of an analysis, including the code version, are recorded so that an analytical result can be reproduced at any point in the future.The GenePattern Notebook system, notebook.genepattern.org, is an integration of the Jupyter Notebook environment with the GenePattern server. It combines the research narrative capabilities of Jupyter with the non-programming approach and breadth of analyses available in GenePattern. Scientists using GenePattern Notebook can create documents that include richly-formatted text and multimedia, executable code, and GenePattern analyses. A single GenePattern notebook can therefore comprise a sophisticated analytical workflow that runs on multiple remote servers.We have recently expanded GenePattern Notebook into a new notebook environment, Genomics to Notebook, g2nb.org, which adds the ability to include analyses on any public Galaxy server as well as using the Integrative Genomics Viewer (IGV) and Cytoscape within notebooks. Each analysis or visualization appears a cell within a notebook, preserving the accessibility and ease of use of the notebook metaphor while retaining the essential aspects of each tool’s user interface. When run, the entire analysis appears to execute seamlessly within the notebook. The online workspace also includes a library of featured genomic analysis notebooks, including templates for common analysis tasks as well as cancer-specific research scenarios and compute-intensive methods. Scientists can easily copy these notebooks, use them as is, or adapt them for their research purposes. Citation Format: Michael M. Reich, Thorin Tabor, John Liefeld, Edwin Huang, Forrest Kim, Jill P. Mesirov. The GenePattern ecosystem for cancer bioinformatics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7426.

Abstract 3470: Evaluation of the association between sickle cell disease and breast cancer using the All of Us Research Program dataset

Abstract Background: The All of Us Research Program (AoURP) dataset was used to evaluate pharmacological treatment modalities in patients with both breast cancer (BC) and sickle cell disease (SCD). This study aims to identify treatment strategies for sickle cell patients with BC that may inform improved treatment options for patients living with either or both conditions, as there is an imminent need in this patient population. Methods: We investigated the relationship between SCD and BC using the AoURP dataset. Criteria were female patients with a confirmed diagnosis of concomitant SCD and BC were selected. Hematologic, antineoplastics, and immunomodulating agents prescribed to patients were identified. Analysis was conducted in a Jupyter Notebook environment by way of R. Using FDA-approved and off-label indications, medication use patterns were used to reverse-engineer patient information and identify trends between SCD and BC. Results: Nine female patients had concomitant diagnoses of SCD and BC, which aligned with the inclusion criteria. African Americans were significantly represented in this cohort, making up sixty-seven of patients. Twenty-two percent of patients did not indicate race, while eleven percent of patients identified as Caucasian. All patients included in the study were prescribed anemic therapy. Eleven percent of patients were prescribed an FDA-approved medication for SCD. Seventy-eight percent of patients were prescribed hormone-targeted BC medications. Thirty-three percent of patients were prescribed the immunotherapy regimen, Dinutuximab with adjunctive interferon-alpha. Eleven percent of patients were prescribed cytotoxic therapy. Conclusion: Further investigation is warranted for dinutuximab use in BC treatment, specifically the triple negative subtype, as currently no targeted therapeutic options exist, and prognosis is poor. The data suggests dinutuximab with adjunctive interferon-alpha may be promising for triple-negative BC patients with SCD. Citation Format: Ivan T. Jubilee, Lauren K. Evans, KiTani Parker Lemieux. Evaluation of the association between sickle cell disease and breast cancer using the All of Us Research Program dataset [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3470.

Discovering invariant spatial features in electron energy loss spectroscopy images on the mesoscopic and atomic levels

Over the last two decades, Electron Energy Loss Spectroscopy (EELS) imaging with a scanning transmission electron microscope has emerged as a technique of choice for visualizing complex chemical, electronic, plasmonic, and phononic phenomena in complex materials and structures. The availability of the EELS data necessitates the development of methods to analyze multidimensional data sets with complex spatial and energy structures. Traditionally, the analysis of these data sets has been based on analysis of individual spectra, one at a time, whereas the spatial structure and correlations between individual spatial pixels containing the relevant information of the physics of underpinning processes have generally been ignored and analyzed only via the visualization as 2D maps. Here, we develop a machine learning-based approach and workflows for the analysis of spatial structures in 3D EELS data sets using a combination of dimensionality reduction and multichannel rotationally invariant variational autoencoders. This approach is illustrated for the analysis of both the plasmonic phenomena in a system of nanowires and in the core excitations in functional oxides using low loss and core-loss EELS, respectively. The code developed in this manuscript is open sourced and freely available and provided as a Jupyter notebook for the interested reader.

Spreadsheet Modeling and Wrangling with Python

A staple of many spreadsheet-based management science courses is the use of Excel for activities such as model building, sensitivity analysis, goal seeking, and Monte-Carlo simulation. What might those things look like if carried out using Python? We describe a teaching module in which Python is used to do typical Excel-based modeling and data-wrangling tasks. In addition, students are exposed to basic software engineering principles, including project folder structures, version control, object-oriented programming, and other more advanced Python skills, creating deployable packages and documentation. The module is supported with Jupyter notebooks, Python scripts, course web pages that include numerous screencasts, and a few GitHub repositories. All of the supporting materials are permissively licensed and freely accessible. Supplemental Material: The supplemental files are available at https://doi.org/10.1287/ited.2023.0047 .

INVOLVEMENT OF DIFFERENT STAKEHOLDER GROUPS IN ENSURING CHILDREN’S DENTAL PUBLIC HEALTH: ANALYSIS AND PERSPECTIVES

Introduction: Strengthening the dental public health (DPH) of the pediatric population requires prioritizing development with the identification of key stakeholders and their leading functions.
 The aim. To analyze the current state and identify prospects for intra-industry and inter-sectoral cooperation in the process of strengthening the dental public health of the child population to justify ways of optimization.
 Materials and methods. A cross-sectional study using expert assessments involving 15 highly competent experts (competence level above 0.85) was conducted through a specially designed program. Bibliosemantic and sociological methods were employed. Expert assessments and stakeholder mapping were performed, visualizing the «authority-interest» relationship of different interested groups on a two-dimensional model. The results were analyzed using standard statistical methods with the application of Jupyter Notebook (https://jupyter.org/install). Intra-class correlation coefficients were used to assess the agreement of expert opinions. A significance level of p<0.05 was considered critical in testing all statistical hypotheses.
 Results. The graphical analysis allowed for the identification of stakeholders and determination of their roles in the DPH system. Groups with high and low levels of power and interest were identified. The agreement among experts regarding the determination of power and interest was respectively ICC=0.76 (95% CI 0.56 – 0.91) and ICC=0.79 (95% CI 0.59 – 0.92). The necessity of targeted efforts with each group, considering their capabilities and limitations, was emphasized. Key stakeholders included families, communities, family physicians, public health institutions, pediatric dentists, and others. Primary functions aimed at strengthening children’s dental health were attributed to influencing determinants of dental health, intersectoral collaboration and support for relevant initiatives at the local level are essential.
 Conclusions. The importance of developing and implementing programs aimed at improving the dental health of the child population has been established. Emphasis is placed on the significance of collaboration among various stakeholders in supporting a strategic approach to managing DPH.

KCSD-python, reliable current source density estimation with quality control.

Interpretation of extracellular recordings can be challenging due to the long range of electric field. This challenge can be mitigated by estimating the current source density (CSD). Here we introduce kCSD-python, an open Python package implementing Kernel Current Source Density (kCSD) method and related tools to facilitate CSD analysis of experimental data and the interpretation of results. We show how to counter the limitations imposed by noise and assumptions in the method itself. kCSD-python allows CSD estimation for an arbitrary distribution of electrodes in 1D, 2D, and 3D, assuming distributions of sources in tissue, a slice, or in a single cell, and includes a range of diagnostic aids. We demonstrate its features in a Jupyter Notebook tutorial which illustrates a typical analytical workflow and main functionalities useful in validating analysis results.

CurveML: a benchmark for evaluating and training learning-based methods of classification, recognition, and fitting of plane curves

AbstractWe propose CurveML, a benchmark for evaluating and comparing methods for the classification and identification of plane curves represented as point sets. The dataset is composed of 520k curves, of which 280k are generated from specific families characterised by distinctive shapes, and 240k are obtained from Bézier or composite Bézier curves. The dataset was generated starting from the parametric equations of the selected curves making it easily extensible. It is split into training, validation, and test sets to make it usable by learning-based methods, and it contains curves perturbed with different kinds of point set artefacts. To evaluate the detection of curves in point sets, our benchmark includes various metrics with particular care on what concerns the classification and approximation accuracy. Finally, we provide a comprehensive set of accompanying demonstrations, showcasing curve classification, and parameter regression tasks using both ResNet-based and PointNet-based networks. These demonstrations encompass 14 experiments, with each network type comprising 7 runs: 1 for classification and 6 for regression of the 6 defining parameters of plane curves. The corresponding Jupyter notebooks with training procedures, evaluations, and pre-trained models are also included for a thorough understanding of the methodologies employed.

Using Generative AI to Improve the Performance and Interpretability of Rule-Based Diagnosis of Type 2 Diabetes Mellitus

Introduction: Type 2 diabetes mellitus is a major global health concern, but interpreting machine learning models for diagnosis remains challenging. This study investigates combining association rule mining with advanced natural language processing to improve both diagnostic accuracy and interpretability. This novel approach has not been explored before in using pretrained transformers for diabetes classification on tabular data. Methods: The study used the Pima Indians Diabetes dataset to investigate Type 2 diabetes mellitus. Python and Jupyter Notebook were employed for analysis, with the NiaARM framework for association rule mining. LightGBM and the dalex package were used for performance comparison and feature importance analysis, respectively. SHAP was used for local interpretability. OpenAI GPT version 3.5 was utilized for outcome prediction and interpretation. The source code is available on GitHub. Results: NiaARM generated 350 rules to predict diabetes. LightGBM performed better than the GPT-based model. A comparison of GPT and NiaARM rules showed disparities, prompting a similarity score analysis. LightGBM’s decision making leaned heavily on glucose, age, and BMI, as highlighted in feature importance rankings. Beeswarm plots demonstrated how feature values correlate with their influence on diagnosis outcomes. Discussion: Combining association rule mining with GPT for Type 2 diabetes mellitus classification yields limited effectiveness. Enhancements like preprocessing and hyperparameter tuning are required. Interpretation challenges and GPT’s dependency on provided rules indicate the necessity for prompt engineering and similarity score methods. Variations in feature importance rankings underscore the complexity of T2DM. Concerns regarding GPT’s reliability emphasize the importance of iterative approaches for improving prediction accuracy.

AlphaPept: a modern and open framework for MS-based proteomics

In common with other omics technologies, mass spectrometry (MS)-based proteomics produces ever-increasing amounts of raw data, making efficient analysis a principal challenge. A plethora of different computational tools can process the MS data to derive peptide and protein identification and quantification. However, during the last years there has been dramatic progress in computer science, including collaboration tools that have transformed research and industry. To leverage these advances, we develop AlphaPept, a Python-based open-source framework for efficient processing of large high-resolution MS data sets. Numba for just-in-time compilation on CPU and GPU achieves hundred-fold speed improvements. AlphaPept uses the Python scientific stack of highly optimized packages, reducing the code base to domain-specific tasks while accessing the latest advances. We provide an easy on-ramp for community contributions through the concept of literate programming, implemented in Jupyter Notebooks. Large datasets can rapidly be processed as shown by the analysis of hundreds of proteomes in minutes per file, many-fold faster than acquisition. AlphaPept can be used to build automated processing pipelines with web-serving functionality and compatibility with downstream analysis tools. It provides easy access via one-click installation, a modular Python library for advanced users, and via an open GitHub repository for developers.

Using Bernoulli analysis in horse racing game

Horse racing is very popular in East Asia, especially in Hong Kong and Japan. But whether there is a specific way to win in this kind of gambling game is something that many people are curious about. There are many statistical analyzes in the field of mathematics that deal specifically with various forms of gambling. Many researchers also hope to use mathematics to calculate the winning method of horse racing. The aim of this article is to obtain, through mathematical analysis, the correlation between the effect of the weight of the horse on the outcome of a race in a horse race, under the influence of different distances. In the article, purely linear relationships and Bernoulli analysis were used to determine the results, and then Bernoulli analysis was used to obtain the relationship between the weight of the horse and the final result. In the plotting process, I used pythons PyMC3 library to help construct the mathematical model, and jupyter notebook to make the data more intuitive to show.

Abstract A103: Improved cell phenotyping of ovarian cancer tumor microenvironment

Abstract Introduction: Multiplexed imaging at single-cell resolution is becoming widely used to decipher the role of the cellular microenvironment in cancer and other complex diseases. To identify spatial patterns of single cells on a tissue, accurate cell-type phenotyping is a crucial step. We present Tribus, an interactive, semi-supervised classifier that avoids thresholds and manual labeling, with user-friendly visualization of the results and automated quality control reports. Materials and Methods: We developed Tribus, an open-source Python 3 software utilizing self-organizing maps for data clustering with an integrated Napari plug-in for fast visualization. It assigns the cell phenotypes based on user-defined label description tables and median marker expressions, following a hierarchical structure to minimize overspill bias. It has a modular architecture and can be run via command line, Jupyter Notebook, or the Napari interface. For exploring the Tribus performance, we used 1) manually expert annotated datasets with different tissue origins 2) dataset with cluster-based annotation 3) previously unpublished dataset. To ensure usability, we utilized datasets generated by using different multiplexed imaging technologies, and of variable size including large tissue microarrays and whole-slide images. Results: Using Tribus we accurately annotated the cell phenotypes in an expert-labeled Co-detection by indexing (CODEX) dataset, consisting of 8 regions of tissue samples from a single donor, including both healthy colon and small bowel sections. Tribus analysis of a high-grade ovarian cancer (HGSC) tumor microarray dataset generated by tCyCIF revealed a subset of lymphoid cells previously mislabelled using a cluster annotation approach. Finally, we utilized Tribus for deep cell phenotyping on a large whole-slide image tCyCIF dataset consisting of over a million cells from five matched HGSC samples collected before and after neoadjuvant chemotherapy (NACT). In the paired samples we successfully identified cell subtypes crucial for immune surveillance. Our analysis accurately differentiated M1 and M2 macrophage subpopulations. In addition, the analyzed post-NACT samples showed a higher proportion of tumor-infiltrating CD8+ T-cells, interestingly displaying occasional spatial clusters by the tumor borders. Therefore, Tribus has shown a great potential to study the spatial patterns and cell-cell interactions of relevant immune cells in response to neoadjuvant chemotherapy treatment. Conclusion: Overall, Tribus is an accurate, fast and efficient way to analyze different sizes of datasets across different technologies, resulting in accelerated analysis of the spatial tumor microenvironment (TME). The accurate cell-type phenotyping with Tribus uncovered high-resolution cellular phenotypes and their spatial patterns in the TME in ovarian cancer after neoadjuvant chemotherapy. Citation Format: Ziqi Kang, Teodora Farago, Angela Szabo, Inga-Maria Launonen, Fernando Perez, Ella Anttila, Kevin Elias, Julia Casado, Peter Sorger, Anniina Färkkilä. Improved cell phenotyping of ovarian cancer tumor microenvironment [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr A103.

Exploiting Vector Pattern Diversity of Molecular Scaffolds for Cheminformatics Tasks in Drug Discovery.

Chemical diversity is challenging to describe objectively. Despite this, various notions of chemical diversity are used throughout the medicinal chemistry optimization process in drug discovery. In this work, we show the usefulness of considering exploited vectors during different phases of the drug design process to provide a quantitative and objective description of chemical diversity. We have developed a concise and fast approach to enumerate and analyze the exploited vector patterns (EVPs) of molecular compound series, which can then be used in archetypal compound selection tasks, from hit matter identification to hit expansion and lead optimization. We first show that EVPs can be used to assess the progressibility of compounds in a fragment library design exercise. By considering EVPs, we then show how a set of compounds can be prioritized for hit expansion using EVP-based, customizable diversity sampling approaches, reducing the time taken and mitigating human biases. We also show that EVPs are a useful tool to analyze SAR data, offering the chance to uncover correlations between different vectors without predetermining the molecular scaffold structures. The codes used to perform these tasks are presented as easy-to-use Jupyter notebooks, which can be readily adapted for further related tasks.

Graph-theoretical prediction of biological modules in quaternary structures of large protein complexes.

The functional complexity of biochemical processes is strongly related to the interplay of proteins and their assembly into protein complexes. In recent years, the discovery and characterization of protein complexes have substantially progressed through advances in cryo-electron microscopy, proteomics, and computational structure prediction. This development results in a strong need for computational approaches to analyse the data of large protein complexes for structural and functional characterization. Here, we aim to provide a suitable approach, which processes the growing number of large protein complexes, to obtain biologically meaningful information on the hierarchical organization of the structures of protein complexes. We modelled the quaternary structure of protein complexes as undirected, labelled graphs called complex graphs. In complex graphs, the vertices represent protein chains and the edges spatial chain-chain contacts. We hypothesized that clusters based on the complex graph correspond to functional biological modules. To compute the clusters, we applied the Leiden clustering algorithm. To evaluate our approach, we chose the human respiratory complex I, which has been extensively investigated and exhibits a known biological module structure experimentally validated. Additionally, we characterized a eukaryotic group II chaperonin TRiC/CCT and the head of the bacteriophage Φ29. The analysis of the protein complexes correlated with experimental findings and indicated known functional, biological modules. Using our approach enables not only to predict functional biological modules in large protein complexes with characteristic features but also to investigate the flexibility of specific regions and coformational changes. The predicted modules can aid in the planning and analysis of experiments. Jupyter notebooks to reproduce the examples are available on our public GitHub repository: https://github.com/MolBIFFM/PTGLtools/tree/main/PTGLmodulePrediction.