Supervised Learning Approach Research Articles

MTCSNet: One-Stage Learning and Two-Point Labeling are Sufficient for Cell Segmentation.

Deep convolution neural networks have been widely used in medical image analysis, such as lesion identification in whole-slide images, cancer detection, and cell segmentation, etc. However, it is often inevitable that researchers try their best to refine annotations so as to enhance the model performance, especially for cell segmentation task. Weakly supervised learning can greatly reduce the workload of annotations, while there is still a huge performance gap between the weakly and fully supervised learning approaches. In this work, we propose a weakly-supervised cell segmentation method, namely Multi-Task Cell Segmentation Network (MTCSNet), for multi-modal medical images, including pathological, brightfield, fluorescent, phase-contrast and differential interference contrast images. MTCSNet is learnt in a single-stage training manner, where only two annotated points for each cell provide supervision information, and the first one is the centroid, the second one is its boundary. Additionally, five auxiliary tasks are elaborately designed to train the network, including two pixel-level classifications, a pixel-level regression, a local temperature scaling and an instance-level distance regression task, which is proposed to regress the distances between the cell centroid and its boundaries in eight orientations. The experimental results indicate that our method outperforms all state-of-the-art weakly-supervised cell segmentation approaches on public multi-modal medical image datasets. The promising performance also shows that a single-stage learning with two-point labeling approach are sufficient for cell segmentation, instead of fine contour delineation. The codes are available at: https://github.com/binging512/MTCSNet.

A Scalable and Adaptable Supervised Learning Approach for Solving the Traveling Salesman Problems

A Scalable and Adaptable Supervised Learning Approach for Solving the Traveling Salesman Problems

The Parameter Optimization of Support Vector Machine with Genetic Algorithm in Risk Early Warning Models

Machine Learning algorithms are widely used by lenders in risk early warning models. With Machine Learning, the risk levels of individual and corporate customers are determined at the account and customer level. Lenders want to manage risk by evaluating the payment performance of customer or account with the help of Machine Learning algorithms. Banks, which have an important place among lenders, develop risk early warning models with the help of learning algorithms using customer information. In the development process of risk early warning models, while banks generally use customer information and credit bureau information for the individual segment, they use financial, non-financial and behaviour-based information for the corporate segment. In this study, it is planned to develop a risk early model for customers in corporate service segment. For the customers of corporate service segment, Balance Sheet and Income Statement items were used and the financial ratios were calculated for risk early warning models. In the development of risk early warning models, Mutual Information method was used as a novel feature selection approach and Support Vector Machine method (linear function, radial basis function and sigmoid function) was used as a supervised learning approach. By changing the neighbourhood metric (k), important patterns were discovered with the Mutual Information method in feature selection process. The optimal C and gamma parameters for Support Vector Machine models have been tried to be determined with the Genetic Algorithm, which is among the Meta-Heuristic algorithms. In order to find the optimal metrics in this study, the metric values for all parameters of the SVM model (function specific) have been kept quite wide. In this dataset of corporate service customers, the small neighbourhood metric has been found to have a significant impact on model learning and performance.

Application of simulation and machine learning in supply chain management: A synthesis of the literature using the Sim-ML literature classification framework

Stochastic modeling techniques, such as discrete-event and agent-based simulation, are widely used in supply chain management (SCM) for capturing real-world uncertainties. Over the last decade, data-driven approaches like machine learning (ML) have also gained prominence in SCM, employing methods such as supervised learning (SL), unsupervised learning (UL), and reinforcement learning (RL). As supply chains grow in complexity, hybrid models combining simulation (Sim) and ML are becoming increasingly common, and the field stands to gain from a structured review of this literature. Towards this, we developed the Sim-ML Literature Classification Framework, which includes a hierarchical taxonomy comprising five SC criteria, 22 Sim-ML classes and over 75 Sim-ML subclasses. We applied this framework to synthesize 99 papers, revealing significant diversity in how Sim-ML models are used to address supply chain challenges. Key findings include the recognition of the breadth of study objectives, identifying various forms of model hybridization achieved by combining discrete/continuous simulation techniques with SL, UL, and RL approaches, and the data flow mechanisms such as sequential and feedback methods employed by the simulation and ML elements of the hybrid model. Our findings also identify some gaps in the literature; for example, optimization is rarely incorporated into Sim-ML models. Also, most studies present Sim-ML models for addressing problems in general supply chains, likely due to the lack of access to industrial data. The review also highlights that Industry 4.0 technologies, such as digital twins and blockchain, are underrepresented in current research, as are topics like sustainability and transportation. These gaps suggest significant opportunities for future research. We provide guidelines for practitioners on applying Sim-ML models to manage supply chain drivers, mitigate the impact of disruptions, and integrate emerging technologies. Our review serves as a valuable resource for researchers, practitioners, and students interested in leveraging Sim-ML approaches in SCM.

DNASimCLR: a contrastive learning-based deep learning approach for gene sequence data classification

BackgroundThe rapid advancements in deep neural network models have significantly enhanced the ability to extract features from microbial sequence data, which is critical for addressing biological challenges. However, the scarcity and complexity of labeled microbial data pose substantial difficulties for supervised learning approaches. To address these issues, we propose DNASimCLR, an unsupervised framework designed for efficient gene sequence data feature extraction.ResultsDNASimCLR leverages convolutional neural networks and the SimCLR framework, based on contrastive learning, to extract intricate features from diverse microbial gene sequences. Pre-training was conducted on two classic large scale unlabelled datasets encompassing metagenomes and viral gene sequences. Subsequent classification tasks were performed by fine-tuning the pretrained model using the previously acquired model. Our experiments demonstrate that DNASimCLR is at least comparable to state-of-the-art techniques for gene sequence classification. For convolutional neural network-based approaches, DNASimCLR surpasses the latest existing methods, clearly establishing its superiority over the state-of-the-art CNN-based feature extraction techniques. Furthermore, the model exhibits superior performance across diverse tasks in analyzing biological sequence data, showcasing its robust adaptability.ConclusionsDNASimCLR represents a robust and database-agnostic solution for gene sequence classification. Its versatility allows it to perform well in scenarios involving novel or previously unseen gene sequences, making it a valuable tool for diverse applications in genomics.

Convolutional Neural Networks (CNNs) for detection of Eye Diseases in an Effective Manner

Introduction: This thesis concentrates on advancing a deep learning system for the precise classification of eye illnesses. Employing the Convolutional Neural Network (CNN) algorithm and Tkinter for GUI, the interface safeguards an intuitive and user-friendly experience. The system effectively identifies various eye conditions, including glaucoma, cataracts, diabetic retinopathy, among others. Objectives: The main goal of this thesis is to offer a reliable and efficient solution for classifying eye diseases through deep learning techniques by addressing the critical need for timely and accurate diagnosis. The significant contributions of this thesis include: Publication of new datasets beneficial to researchers in the field. Enhancement of a robust segmentation approach, applicable to various retinal conditions, including diabetic retinopathy. Methods: A supervised learning approach adopted to train the CNN algorithm with an extensive dataset of eye images. The system achieved an impressive accuracy in classifying eye diseases, underscoring its effectiveness in identification and diagnosis. Development of a hybrid characteristic extraction method from segmented objects, utilized by the classifier for detecting glaucoma and other eye diseases. Results: This system will be able to detect and diagnose the diseases which are related with Eys which will help patients to take care of eyes from further complications. Furthermore, with advancements in portable retinal cameras, integrating the proposed method can facilitate the diagnosis of glaucoma and other eye diseases, thereby enhancing the detection rate, especially in growing nations. Conclusions: The developed system significantly enhances the accuracy and efficiency of eye disease diagnosis, enabling early detection and treatment. The integration of Tkinter for GUI and the color-blind test feature improves the user experience, making the system more accessible to a broader audience. This thesis underscores the potential of deep learning techniques in medical diagnosis, presenting a valuable tool for healthcare professionals and patients alike.

Automatic detection of epileptic seizure based on one dimensional cascaded convolutional autoencoder with adaptive window-thresholding

Objective. Identifying the seizure occurrence period (SOP) in extended EEG recordings is crucial for neurologists to diagnose seizures effectively. However, many existing computer-aided diagnosis systems for epileptic seizure detection (ESD) primarily focus on distinguishing between ictal and interictal states in EEG recordings. This focus has limited their application in clinical settings, as these systems typically rely on supervised learning approaches that require labeled data.Approach. To address this, our study introduces an unsupervised learning framework for ESD using a 1D- cascaded convolutional autoencoder (1D-CasCAE). In this approach, EEG recordings from selected patients in the CHB-MIT datasets are first segmented into 5 s epochs. Eight informative channels are chosen based on the correlation coefficient and Shannon entropy. The 1D-CasCAE is designed to autonomously learn the characteristic patterns of interictal (non-seizure) segments through downsampling and upsampling processes. The integration of adaptive thresholding and a moving window significantly enhances the model's robustness, enabling it to accurately identify ictal segments in long EEG recordings.Main results. Experimental results demonstrate that the proposed 1D-CasCAE effectively learns normal EEG signal patterns and efficiently detects anomalies (ictal segments) using reconstruction errors. When compared with other leading methods in anomaly detection, our model exhibits superior performance, as evidenced by its average Gmean, sensitivity, specificity, precision, and false positive rate scores of 98.00% ± 3.51%, 94.94% ± 6.92%, 99.60% ± 0.30%, 79.92% ± 13.56% and 0.0044 ± 0.0030 h-1respectively for a typical patient in CHB-MIT datasets.Significance. The developed model framework can be employed in clinical settings, replacing the manual inspection process of EEG signals by neurologists. Furthermore, the proposed automated system can adapt to each patient's SOP through the use of variable time windows for seizure detection.

Diagnosis of high‐speed railway ballastless track arching based on unsupervised learning framework

AbstractVehicle‐mounted detection methods have been widely applied in the maintenance of high‐speed railways (HSRs), providing feasibility for diagnosing ballastless track arching. However, applying detection data faces several key limitations: (1) The threshold mostly requires manual setting, making recognition accuracy highly subjective; (2) the extensive workload of manual inspections makes it challenging to label detection data, hindering the application of supervised learning approaches. To address these problems, this paper utilizes the longitudinal level irregularity data obtained from vehicle‐mounted detection, employing the concept of unsupervised learning for dimensionality reduction, combined with clustering algorithms and minimal label fine‐tuning, to design two frameworks: the fully unsupervised framework (FUF) and the few‐shot fine‐tuned framework (FFF). Experiments on dynamic detection data from a Chinese HSR line were conducted, comparing the performance of data dimensionality reduction, clustering, and classification under different strategy combinations. The results show that the improved variational autoencoder significantly enhances the performance of the encoder in dimensionality reduction, facilitating better feature extraction; the FUF achieves effective clustering outcomes without any labeled samples and its adjusted rand index score exceeded 0.8, showcasing its robustness and applicability in scenarios with no prior annotations; the FFF requires only a small number of labeled samples (labeling ratio of 5%) and achieves excellent performance, with metrics such as accuracy exceeding 0.85, thus greatly reducing the reliance on labeled data. This study offers a novel method for solving engineering issues with limited labeled data, providing an efficient solution for identifying track arching defects and advancing railway infrastructure monitoring.

Joint path planning and power allocation of a cellular-connected UAV using apprenticeship learning via deep inverse reinforcement learning

This paper investigates an interference-aware joint path planning and power allocation mechanism for a cellular-connected unmanned aerial vehicle (UAV) in a sparse suburban environment. The UAV’s goal is to fly from an initial point and reach a destination point by moving along the cells to guarantee the required quality of service (QoS). In particular, the UAV aims to maximize its uplink throughput and minimize interference to the ground user equipment (UEs) connected to neighboring cellular base stations (BSs), considering both the shortest path and limitations on flight resources. Expert knowledge is used to experience the scenario and define the desired behavior for the sake of the agent (i.e., UAV) training. To solve the problem, an apprenticeship learning method is utilized via inverse reinforcement learning (IRL) based on both Q-learning and deep reinforcement learning (DRL). The performance of this method is compared to learning from a demonstration technique called behavioral cloning (BC) using a supervised learning approach. Simulation and numerical results show that the proposed approach can achieve expert-level performance. We also demonstrate that, unlike the BC technique, the performance of our proposed approach does not degrade in unseen situations.

Seismic data interpolation with a recurrent inference mechanism

Abstract Seismic data interpolation is a significant procedure in seismic data processing as the highly complete data are extremely useful for the subsequent imaging and interpretation workflows. Recently, numerous deep learning-based techniques have been utilized to help improve the reconstruction quality. Supervised learning approaches can predict the results in a second, but they depend heavily on the training data, which therefore limits the generalized applications. By contrast, unsupervised learning methods are applicable to any data, but they need much more computational time and prior knowledge to be implemented. Especially, the recovery of aliased and consecutively missing data is incredibly challenging. To solve this problem, we propose a novel framework for seismic interpolation using a recurrent inference mechanism (SIRIM). Integrating the advantages of supervised and unsupervised learning paradigms, we build a specific convolutional recurrent inference network such that it can learn the dynamic priors when plugged in the physics-informed iterative algorithm, as well as directly reconstruct various types of incomplete data. We validate SIRIM in comparison with some traditional and learning-based methods. The performance on synthetic and field data illustrates the effectiveness and robustness of our proposed approach, which outperforms baseline methods in terms of aliased and consecutively missing data.

Conditional Similarity Triplets Enable Covariate-Informed Representations of Single-Cell Data.

Single-cell technologies enable comprehensive profiling of diverse immune cell-types through the measurement of multiple genes or proteins per cell. In order to translate data from immune profiling assays into powerful diagnostics, machine learning approaches are used to compute per-sample immunological summaries, or featurizations that can be used as inputs to models for outcomes of interest. Current supervised learning approaches for computing per-sample representations are optimized based only on the outcome variable to be predicted and do not take into account clinically-relevant covariates that are likely to also be measured. Here we expand the optimization problem to also take into account such additional patient covariates to directly inform the learned per-sample representations. To do this, we introduce CytoCoSet, a set-based encoding method, which formulates a loss function with an additional triplet term penalizing samples with similar covariates from having disparate embedding results in per-sample representations. Overall, incorporating clinical covariates leads to improved prediction of clinical phenotypes.

Seagrass classification using unsupervised curriculum learning (UCL)

Seagrass ecosystems are pivotal in marine environments, serving as crucial habitats for diverse marine species and contributing significantly to carbon sequestration. Accurate classification of seagrass species from underwater images is imperative for monitoring and preserving these ecosystems. This paper introduces Unsupervised Curriculum Learning (UCL) to seagrass classification using the DeepSeagrass dataset. UCL progressively learns from simpler to more complex examples, enhancing the model's ability to discern seagrass features in a curriculum-driven manner. Experiments employing state-of-the-art deep learning architectures, convolutional neural networks (CNNs), show that UCL achieved overall 90.12 % precision and 89 % recall, which significantly improves classification accuracy and robustness, outperforming some traditional supervised learning approaches like SimCLR, and unsupervised approaches like Zero-shot CLIP. The methodology of UCL involves four main steps: high-dimensional feature extraction, pseudo-label generation through clustering, reliable sample selection, and fine-tuning the model. The iterative UCL framework refines CNN's learning of underwater images, demonstrating superior accuracy, generalization, and adaptability to unseen seagrass and background samples of undersea images. The findings presented in this paper contribute to the advancement of seagrass classification techniques, providing valuable insights into the conservation and management of marine ecosystems. The code and dataset are made publicly available and can be assessed here: https://github.com/nabid69/Unsupervised-Curriculum-Learning—UCL.

Pretrained subtraction and segmentation model for coronary angiograms

This study introduces a novel self-supervised learning method for single-frame subtraction and vessel segmentation in coronary angiography, addressing the scarcity of annotated medical samples in AI applications. We pretrain a U-Net model on a large dataset of unannotated coronary angiograms using an image-to-image translation framework, then fine-tune it on a limited set of manually annotated samples. The pretrained model excels at comprehensive single-frame subtraction, outperforming existing DSA methods. Fine-tuning with just 40 samples yields a Dice coefficient of 0.828 for vessel segmentation. On the public XCAD dataset, our model sets a new state-of-the-art benchmark with a Dice coefficient of 0.755, surpassing both unsupervised and supervised learning approaches. This method achieves robust single-frame subtraction and demonstrates that combining pretraining with minimal fine-tuning enables accurate coronary vessel segmentation with limited manual annotations. We successfully apply this approach to assist physicians in visualizing potential vascular stenosis sites during coronary angiography. Code, dataset, and a live demo will be available available at: https://github.com/newfyu/DeepSA.

Performance evaluation of semi-supervised learning frameworks for multi-class weed detection.

Precision weed management (PWM), driven by machine vision and deep learning (DL) advancements, not only enhances agricultural product quality and optimizes crop yield but also provides a sustainable alternative to herbicide use. However, existing DL-based algorithms on weed detection are mainly developed based on supervised learning approaches, typically demanding large-scale datasets with manual-labeled annotations, which can be time-consuming and labor-intensive. As such, label-efficient learning methods, especially semi-supervised learning, have gained increased attention in the broader domain of computer vision and have demonstrated promising performance. These methods aim to utilize a small number of labeled data samples along with a great number of unlabeled samples to develop high-performing models comparable to the supervised learning counterpart trained on a large amount of labeled data samples. In this study, we assess the effectiveness of a semi-supervised learning framework for multi-class weed detection, employing two well-known object detection frameworks, namely FCOS (Fully Convolutional One-Stage Object Detection) and Faster-RCNN (Faster Region-based Convolutional Networks). Specifically, we evaluate a generalized student-teacher framework with an improved pseudo-label generation module to produce reliable pseudo-labels for the unlabeled data. To enhance generalization, an ensemble student network is employed to facilitate the training process. Experimental results show that the proposed approach is able to achieve approximately 76% and 96% detection accuracy as the supervised methods with only 10% of labeled data in CottonWeedDet3 and CottonWeedDet12, respectively. We offer access to the source code (https://github.com/JiajiaLi04/SemiWeeds), contributing a valuable resource for ongoing semi-supervised learning research in weed detection and beyond.

Similarity searching for wafer bin maps by measuring shape, location, and size similarities of defect patterns

A wafer bin map (WBM) is a visual representation of the spatial distribution of defective chips on a wafer. WBMs showing specific defect patterns are usually a result of process-assignable causes; thus, it is important to identify them to eliminate assignable causes. With advances in semiconductor manufacturing technology, identifying new defect patterns, and diagnosing their causes have become critical. However, most existing methods for WBM analysis use a supervised learning approach, which only detects previously known defect patterns. The similarity-search approach is a suitable alternative for defining new defect patterns. The proposed method uses an unsupervised approach to search for similar WBMs by measuring the similarities of three spatial features of defect patterns–shape, location, and size–which are useful for defining new defect patterns and diagnosing their causes. These three similarities are achieved using tensor voting and the mountain function for shape similarity, Euclidean distance for location similarity, and a combination of defect count and average radius for size similarity. The overall similarity was assessed using the weighted average of the three similarities. The weights are determined by quantifying the uncertainty of each similarity based on information entropy theory to better distinguish between similar patterns. The experimental results demonstrate the effectiveness of the proposed method compared to existing methods and highlight its capability to identify and describe the spatial features of defect patterns.

Twitter spam drift detection by semi supervised learning approach using YATSI algorithm

Twitter spam drift detection by semi supervised learning approach using YATSI algorithm

Simple, Efficient, and Scalable Structure-Aware Adapter Boosts Protein Language Models.

Fine-tuning pretrained protein language models (PLMs) has emerged as a prominent strategy for enhancing downstream prediction tasks, often outperforming traditional supervised learning approaches. As a widely applied powerful technique in natural language processing, employing parameter-efficient fine-tuning techniques could potentially enhance the performance of PLMs. However, the direct transfer to life science tasks is nontrivial due to the different training strategies and data forms. To address this gap, we introduce SES-Adapter, a simple, efficient, and scalable adapter method for enhancing the representation learning of PLMs. SES-Adapter incorporates PLM embeddings with structural sequence embeddings to create structure-aware representations. We show that the proposed method is compatible with different PLM architectures and across diverse tasks. Extensive evaluations are conducted on 2 types of folding structures with notable quality differences, 9 state-of-the-art baselines, and 9 benchmark data sets across distinct downstream tasks. Results show that compared to vanilla PLMs, SES-Adapter improves downstream task performance by a maximum of 11% and an average of 3%, with significantly accelerated convergence speed by a maximum of 1034% and an average of 362%, the training efficiency is also improved by approximately 2 times. Moreover, positive optimization is observed even with low-quality predicted structures. The source code for SES-Adapter is available at https://github.com/tyang816/SES-Adapter.

OXidative Stress PREDictor: A Supervised Learning Approach for Annotating Cellular Oxidative Stress States in Inflammatory Cells

Oxidative stress, characterized by an imbalance between reactive oxygen species (ROS) and antioxidants, plays a pivotal role in inflammatory responses associated with both chronic diseases and acute injuries. In this study, OXidative Stress PREDictor (OxSpred), a supervised learning model tailored to accurately annotate the oxidative stress state of innate immune cells at the single‐cell level, is introduced. Compared to the traditional gene‐set‐variation‐analysis‐based enrichment method, OxSpred demonstrates superior accuracy with an area under the receiver operating characteristic curve of 0.89 and offers interpretable embeddings with significant biological relevance. Using the predicted ROS states, precise elucidation and interpretation of the roles of novel innate immune cell subtypes can be achieved. Overall, OxSpred enhances the utility of single‐cell transcriptomic datasets by providing a robust in silico method for determining intracellular oxidative stress states, thereby enriching the understanding of innate immune cell functions during inflammation.

Inferring Taxonomic Affinities and Genetic Distances Using Morphological Features Extracted from Specimen Images: a Case Study with a Bivalve dataset.

Reconstructing the tree of life and understanding the relationships of taxa are core questions in evolutionary and systematic biology. The main advances in this field in the last decades were derived from molecular phylogenetics; however, for most species, molecular data are not available. Here, we explore the applicability of two deep learning methods - supervised classification approaches and unsupervised similarity learning - to infer organism relationships from specimen images. As a basis, we assembled an image dataset covering 4144 bivalve species belonging to 74 families across all orders and subclasses of the extant Bivalvia, with molecular phylogenetic data being available for all families and a complete taxonomic hierarchy for all species. The suitability of this dataset for deep learning experiments was evidenced by an ablation study resulting in almost 80% accuracy for identifications on the species level. Three sets of experiments were performed using our dataset. First, we included taxonomic hierarchy and genetic distances in a supervised learning approach to obtain predictions on several taxonomic levels simultaneously. Here, we stimulated the model to consider features shared between closely related taxa to be more critical for their classification than features shared with distantly related taxa, imprinting phylogenetic and taxonomic affinities into the architecture and training procedure. Second, we used transfer learning and similarity learning approaches for zero-shot experiments to identify the higher-level taxonomic affinities of test species that the models had not been trained on. The models assigned the unknown species to their respective genera with approximately 48% and 67% accuracy. Lastly, we used unsupervised similarity learning to infer the relatedness of the images without prior knowledge of their taxonomic or phylogenetic affinities. The results clearly showed similarities between visual appearance and genetic relationships at the higher taxonomic levels. The correlation was 0.6 for the most species-rich subclass (Imparidentia), ranging from 0.5 to 0.7 for the orders with the most images. Overall, the correlation between visual similarity and genetic distances at the family level was 0.78. However, fine-grained reconstructions based on these observed correlations, such as sister-taxa relationships, require further work. Overall, our results broaden the applicability of automated taxon identification systems and provide a new avenue for estimating phylogenetic relationships from specimen images.

Development of a logic regression-based approach for the discovery of host- and niche-informative biomarkers in Escherichia coli and their application for microbial source tracking.

Microbial source tracking leverages a wide range of approaches designed to trace the origins of fecal contamination in aquatic environments. Although source tracking methods are typically employed within the laboratory setting, computational techniques can be leveraged to advance microbial source tracking methodology. Herein, we present a logic regression-based supervised learning approach for the discovery of source-informative genetic markers within intergenic regions across the Escherichia coli genome that can be used for source tracking. With just single intergenic loci, logic regression was able to identify highly source-specific (i.e., exceeding 97.00%) biomarkers for a wide range of host and niche sources, with sensitivities reaching as high as 30.00%-50.00% for certain source categories, including pig, sheep, mouse, and wastewater, depending on the specific intergenic locus analyzed. Restricting the source range to reflect the most prominent zoonotic sources of E. coli transmission (i.e., bovine, chicken, human, and pig) allowed for the generation of informative biomarkers for all host categories, with specificities of at least 90.00% and sensitivities between 12.50% and 70.00%, using the sequence data from key intergenic regions, including emrKY-evgAS, ibsB-(mdtABCD-baeSR), ompC-rcsDB, and yedS-yedR, that appear to be involved in antibiotic resistance. Remarkably, we were able to use this approach to classify 48 out of 113 river water E. coli isolates collected in Northwestern Sweden as either beaver, human, or reindeer in origin with a high degree of consensus-thus highlighting the potential of logic regression modeling as a novel approach for augmenting current source tracking efforts.IMPORTANCEThe presence of microbial contaminants, particularly from fecal sources, within water poses a serious risk to public health. The health and economic burden of waterborne pathogens can be substantial-as such, the ability to detect and identify the sources of fecal contamination in environmental waters is crucial for the control of waterborne diseases. This can be accomplished through microbial source tracking, which involves the use of various laboratory techniques to trace the origins of microbial pollution in the environment. Building on current source tracking methodology, we describe a novel workflow that uses logic regression, a supervised machine learning method, to discover genetic markers in Escherichia coli, a common fecal indicator bacterium, that can be used for source tracking efforts. Importantly, our research provides an example of how the rise in prominence of machine learning algorithms can be applied to improve upon current microbial source tracking methodology.