DeepVISP: Deep Learning for Virus Site Integration Prediction and Motif Discovery

Application of artificial intelligence in cancer diagnosis and tumor nanomedicine

Cancer is a major health concern due to its high incidence and mortality rates. Advances in cancer research, particularly in artificial intelligence (AI) and deep learning, have shown significant progress. The swift evolution of AI in healthcare, especially in tools like computer-aided diagnosis, has the potential to revolutionize early cancer detection. This technology offers improved speed, accuracy, and sensitivity, bringing a transformative impact on cancer diagnosis, treatment, and management. This paper provides a concise overview of the application of artificial intelligence in the realms of medicine and nanomedicine, with a specific emphasis on the significance and challenges associated with cancer diagnosis. It explores the pivotal role of AI in cancer diagnosis, leveraging structured, unstructured, and multimodal fusion data. Additionally, the article delves into the applications of AI in nanomedicine sensors and nano-oncology drugs. The fundamentals of deep learning and convolutional neural networks are clarified, underscoring their relevance to AI-driven cancer diagnosis. A comparative analysis is presented, highlighting the accuracy and efficiency of traditional methods juxtaposed with AI-based approaches. The discussion not only assesses the current state of AI in cancer diagnosis but also delves into the challenges faced by AI in this context. Furthermore, the article envisions the future development direction and potential application of artificial intelligence in cancer diagnosis, offering a hopeful prospect for enhanced cancer detection and improved patient prognosis.

Identification of transcriptionally-active human papillomavirus integrants through nanopore sequencing reveals viable targets for gene therapy against cervical cancer

Integration of the human papillomavirus (HPV) genome into the cellular genome is a key event that leads to constitutive expression of viral oncoprotein E6/E7 and drives the progression of cervical cancer. However, HPV integration patterns differ on a case-by-case basis among related malignancies. Next-generation sequencing technologies still face challenges for interrogating HPV integration sites. In this study, utilizing Nanopore long-read sequencing, we identified 452 and 108 potential integration sites from the cervical cancer cell lines (CaSki and HeLa) and five tissue samples, respectively. Based on long Nanopore chimeric reads, we were able to analyze the methylation status of the HPV long control region (LCR), which controls oncogene E6/E7 expression, and to identify transcriptionally-active integrants among the numerous integrants. As a proof of concept, we identified an active HPV integrant in between RUNX2 and CLIC5 on chromosome 6 in the CaSki cell line, which was supported by ATAC-seq, H3K27Ac ChIP-seq, and RNA-seq analysis. Knockout of the active HPV integrant, by the CRISPR/Cas9 system, dramatically crippled cell proliferation and induced cell senescence. In conclusion, identifying transcriptionally-active HPV integrants with Nanopore sequencing can provide viable targets for gene therapy against HPV-associated cancers.

Deep learning for detecting and elucidating human T-cell leukemia virus type 1 integration in the human genome

Human T-cell leukemia virus type 1 (HTLV-1), a retrovirus, is the causative agent for adult T cell leukemia/lymphoma and many other human diseases. Accurate and high throughput detection of HTLV-1 virus integration sites (VISs) across the host genomes plays a crucial role in the prevention and treatment of HTLV-1-associated diseases. Here, we developed DeepHTLV, the first deep learning framework for VIS prediction de novo from genome sequence, motif discovery, and cis-regulatory factor identification. We demonstrated the high accuracy of DeepHTLV with more efficient and interpretive feature representations. Decoding the informative features captured by DeepHTLV resulted in eight representative clusters with consensus motifs for potential HTLV-1 integration. Furthermore, DeepHTLV revealed interesting cis-regulatory elements in regulation of VISs that have significant association with the detected motifs. Literature evidence demonstrated nearly half (34) of the predicted transcription factors enriched with VISs were involved in HTLV-1-associated diseases. DeepHTLV is freely available at https://github.com/bsml320/DeepHTLV.

NetBCE: An Interpretable Deep Neural Network for Accurate Prediction of Linear B-cell Epitopes

Identification of B-cell epitopes (BCEs) plays an essential role in the development of peptide vaccines and immuno-diagnostic reagents, as well as antibody design and production. In this work, we generated a large benchmark dataset comprising 124,879 experimentally supported linear epitope-containing regions in 3567 protein clusters from over 1.3 million B cell assays. Analysis of this curated dataset showed large pathogen diversity covering 176 different families. The accuracy in linear BCE prediction was found to strongly vary with different features, while all sequence-derived and structural features were informative. To search more efficient and interpretive feature representations, a ten-layer deep learning framework for linear BCE prediction, namely NetBCE, was developed. NetBCE achieved high accuracy and robust performance with the average area under the curve (AUC) value of 0.8455 in five-fold cross-validation through automatically learning the informative classification features. NetBCE substantially outperformed the conventional machine learning algorithms and other tools, with more than 22.06% improvement of AUC value compared to other tools using an independent dataset. Through investigating the output of important network modules in NetBCE, epitopes and non-epitopes tended to be presented in distinct regions with efficient feature representation along the network layer hierarchy. The NetBCE is freely available at https://github.com/bsml320/NetBCE.

Designing optimal convolutional neural network architecture using differential evolution algorithm

Convolutional neural networks (CNNs) are deep learning models used widely for solving various tasks like computer vision and speech recognition. CNNs are developed manually based on problem-specific domain knowledge and tricky settings, which are laborious, time consuming, and challenging. To solve these, our study develops an improved differential evolution of convolutional neural network (IDECNN) algorithm to design CNN layer architectures for image classification. Variable-length encoding is utilized to represent the flexible layer architecture of a CNN model in IDECNN. An efficient heuristic mechanism is proposed in IDECNN to evolve CNN architecture through mutation and crossover to prevent premature convergence during the evolutionary process. Eight well-known imaging datasets were utilized. The results showed that IDECNN could design suitable architecture compared with 20 existing CNN models. Finally, CNN architectures are applied to pneumonia and coronavirus disease 2019 (COVID-19) X-ray biomedical image data. The results demonstrated the usefulness of the proposed approach to generate a suitable CNN model.

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Introduce DE algorithm to automatically design CNN architectures

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Variable-length encoding strategy is proposed to encode each CNN model

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For the DE framework, two CNN architectures undergo a refinement difference approach

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Design a heuristic mechanism for mutation operation to evolve CNN architectures

Convolutional neural networks (CNNs) are a class of deep learning (DL) methods that have demonstrated improved performance in various computer vision tasks. With the growing popularity of CNNs, several CNN architectures have been introduced with a large number of design options that are problem dependent. In most situations, the constructed CNN model performs well on the dataset used to train it. There is no guarantee that the designed CNN model can achieve sufficient classification accuracy for other datasets. Designing an appropriate CNN model architecture for a particular problem requires human interaction and trial-and-error procedures, which are laborious and time consuming. This study uses an improved differential evolution of convolutional neural network (IDECNN) technique to automatically construct effective CNN architectures for several image classification problems, which mitigates the issues found with manually designed CNN models.

KCOSS: an ultra-fast k-mer counter for assembled genome analysis

MOTIVATION

The k-mer frequency in whole genome sequences provides researchers with an insightful perspective on genomic complexity, comparative genomics, metagenomics and phylogeny. The current k-mer counting tools are typically slow, and they require large memory and hard disk for assembled genome analysis.

RESULTS

We propose a novel and ultra-fast k-mer counting algorithm, KCOSS, to fulfill k-mer counting mainly for assembled genomes with segmented Bloom filter, lock-free queue, lock-free thread pool and cuckoo hash table. We optimize running time and memory consumption by recycling memory blocks, merging multiple consecutive first-occurrence k-mers into C-read, and writing a set of C-reads to disk asynchronously. KCOSS was comparatively tested with Jellyfish2, CHTKC and KMC3 on seven assembled genomes and three sequencing datasets in running time, memory consumption, and hard disk occupation. The experimental results show that KCOSS counts k-mer with less memory and disk while having a shorter running time on assembled genomes. KCOSS can be used to calculate the k-mer frequency not only for assembled genomes but also for sequencing data.

AVAILABILITYAND IMPLEMENTATION

The KCOSS software is implemented in C++. It is freely available on GitHub: https://github.com/kcoss-2021/KCOSS.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.