Skin cancer, particularly melanoma, is a severe health threat that necessitates early detection for effective treatment. This research introduces a skin lesion classification system that harnesses the capabilities of three advanced deep learning models: VGG16, Inception-V3, and ResNet-50. By integrating these models into an ensemble, the system leverages their individual strengths to improve classification accuracy and robustness. Every model in the ensemble brings its distinctive contribution, having undergone pre-training on ImageNet and subsequent fine-tuning using dermoscopic images. We evaluated our ensemble approach using the ISIC 2018 dataset, a standard benchmark for skin lesion classification. By employing a weighted averaging method to merge predictions from the three models, our ensemble model demonstrated superior performance. The results show an improvement in classification accuracy, achieving an accuracy of 91% on the original dataset and 97% on the dataset balanced by oversampling compared to the individual models. This system was then evaluated using the HAM10000 dataset. The results using the HAM10000 dataset show an improvement in classification accuracy, achieving an accuracy of 90% on the original dataset and 96% on the dataset balanced by oversampling compared to the individual models. This enhanced performance highlights the effectiveness of our ensemble method in capturing diverse features of skin lesions, leading to more accurate diagnoses. Our findings suggest that this approach can significantly assist dermatologists in early and precise skin cancer detection, potentially improving patient outcomes.

Background: Protein-protein interactions (PPIs) play key roles in various cellular functions. In addition, some critical inter-species interactions such as host-pathogen interactions and pathogenicity occur through PPIs. Phytopathogenic bacteria infect hosts through attachment to host tissue, enzyme secretion, exopolysaccharides production, toxins release, iron acquisition, and effector proteins secretion. Many such mechanisms involve some kind of protein-protein interaction in hosts. Our first aim was to predict the whole protein interaction pairs (interactome) of Xanthomonas oryzae pathovar oryzae (Xoo) that is an important pathogenic bacterium that causes bacterial blight (BB) in rice. We developed a detection protocol to find possibly interacting proteins in its host using whole genome PPI prediction algorithms. The second aim was to build a DB server and a bioinformatic procedure for finding target proteins in Xoo for developing pesticides that block host-pathogen protein interactions within critical biochemical pathways. Description: A PPI network in Xoo proteome was predicted by bioinformatics algorithms: PSIMAP, PEIMAP, and iPfam. We present the resultant species specific interaction network and host-pathogen interaction, XooNET. It is a comprehensive predicted initial PPI data for Xoo. XooNET can be used by experimentalists to pick up protein targets for blocking pathological interactions. XooNET uses most of the major types of PPI algorithms. They are: 1) Protein Structural Interactome MAP (PSIMAP), a method using structural domain of SCOP, 2) Protein Experimental Interactome MAP (PEIMAP), a common method using public resources of experimental protein interaction information such as HPRD, BIND, DIP, MINT, IntAct, and BioGrid, and 3) Domain-domain interactions, a method using Pfam domains such as iPfam. Additionally, XooNET provides information on network properties of the Xoo interactome. Conclusion: XooNET is an open and free public database server for protein interaction information for Xoo. It contains 4,538 proteins and 26,932 possible interactions consisting of 18,503 (PSIMAP), 3,118 (PEIMAP), and 8,938 (iPfam) pairs. In addition, XooNET provides 3,407 possible interaction pairs between two sets of proteins; 141 Xoo proteins that are predicted as membrane proteins and rice proteomes. The resultant interacting partners of a query protein can be easily retrieved by users as well as the interaction networks in graphical web interfaces. XooNET is freely available from http://bioportal.kobic.kr/XooNET/.

Globally, breast cancer (BC) is considered a major cause of death among women. Therefore, researchers have used various machine and deep learning-based methods for its early and accurate detection using X-ray, MRI, and mammography image modalities. However, the machine learning model requires domain experts to select an optimal feature, obtains a limited accuracy, and has a high false positive rate due to handcrafting features extraction. The deep learning model overcomes these limitations, but these models require large amounts of training data and computation resources, and further improvement in the model performance is needed. To do this, we employ a novel framework called the Ensemble-based Channel and Spatial Attention Network (ECS-A-Net) to automatically classify infected regions within BC images. The proposed framework consists of two phases: in the first phase, we apply different augmentation techniques to enhance the size of the input data, while the second phase includes an ensemble technique that parallelly leverages modified SE-ResNet50 and InceptionV3 as a backbone for feature extraction, followed by Channel Attention (CA) and Spatial Attention (SA) modules in a series manner for more dominant feature selection. To further validate the ECS-A-Net, we conducted extensive experiments between several competitive state-of-the-art (SOTA) techniques over two benchmarks, including DDSM and MIAS, where the proposed model achieved 96.50% accuracy for the DDSM and 95.33% accuracy for the MIAS datasets. Additionally, the experimental results demonstrated that our network achieved a better performance using various evaluation indicators, including accuracy, sensitivity, and specificity among other methods.

This study addresses challenges in skin cancer detection, particularly issues like class imbalance and the varied appearance of lesions, which complicate segmentation and classification tasks. The research employs deep learning ensemble models for both segmentation (using U-Net, SegNet, and DeepLabV3) and classification (using VGG16, ResNet-50, and Inception-V3). The ISIC dataset is balanced through oversampling in classification, and preprocessing techniques such as data augmentation and post-processing are applied in segmentation to increase robustness. The ensemble model outperformed individual models, achieving a Dice Coefficient of 0.93, an IoU of 0.90, and an accuracy of 0.95 for segmentation, with 90% accuracy on the original dataset and 99% on the balanced dataset for classification. The use of ensemble models and balanced datasets proved highly effective in improving the accuracy and reliability of automated skin lesion analysis, supporting dermatologists in early detection efforts.

The early and accurate detection of skin cancer can reduce mortality rates and improve patient outcomes, but requires advanced diagnostics. The integration of artificial intelligence (AI) into healthcare enables the precise and timely detection of skin cancer. However, significant challenges remain including the difficulty in differentiating visually similar skin conditions and the limitations of diverse, representative datasets. In this study, we proposed DCAN-Net, a novel deep-learning framework designed for the early detection of skin cancer. The model leverages an efficient backbone architecture optimized for capturing diverse skin patterns, utilizing carefully tuned parameters to enhance the discrimination capabilities and refine the extracted features using modified attention modules, thereby prioritizing relevant foreground information while minimizing background noise. Furthermore, the Grad-CAM explainable AI method was employed, highlighting the most salient features within dermatoscopic images. The fused optimal feature representations significantly enhanced the dermatoscopic image analysis. When evaluated on the HAM10000 dataset, DCAN-Net achieved a precision, recall, F1-score, and accuracy of 97.00%, 97.57%, 97.10%, and 97.57%, respectively. Moreover, the application of advanced data augmentation techniques mitigated data imbalance issues and reduced false-positive and false-negative rates across the original and augmented datasets. These findings demonstrate the potential of DCAN-Net for improving clinical outcomes and advancing AI-driven skin cancer diagnostics.

Genomics & Informatics (NLM title abbreviation: Genomics Inform) is the official journal of the Korea Genome Organization. Text corpus for this journal annotated with various levels of linguistic information would be a valuable resource as the process of information extraction requires syntactic, semantic, and higher levels of natural language processing. In this study, we publish our new corpus called GNI Corpus version 1.0, extracted and annotated from full texts of Genomics & Informatics, with NLTK (Natural Language ToolKit)-based text mining script. The preliminary version of the corpus could be used as a training and testing set of a system that serves a variety of functions for future biomedical text mining.