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Pathology Pathology

Application of artificial neural networks in detection and diagnosis of gastrointestinal and liver tumors.

In World journal of clinical cases

As a form of artificial intelligence, artificial neural networks (ANNs) have the advantages of adaptability, parallel processing capabilities, and non-linear processing. They have been widely used in the early detection and diagnosis of tumors. In this article, we introduce the development, working principle, and characteristics of ANNs and review the research progress on the application of ANNs in the detection and diagnosis of gastrointestinal and liver tumors.

Mao Wei-Bo, Lyu Jia-Yu, Vaishnani Deep K, Lyu Yu-Man, Gong Wei, Xue Xi-Ling, Shentu Yang-Ping, Ma Jun


Artificial intelligence, Artificial neural network, Deep learning, Gastrointestinal tumor, Tumor detection

General General

Predictive models for stage and risk classification in head and neck squamous cell carcinoma (HNSCC).

In PeerJ

Machine learning techniques are increasingly used in the analysis of high throughput genome sequencing data to better understand the disease process and design of therapeutic modalities. In the current study, we have applied state of the art machine learning (ML) algorithms (Random Forest (RF), Support Vector Machine Radial Kernel (svmR), Adaptive Boost (AdaBoost), averaged Neural Network (avNNet), and Gradient Boosting Machine (GBM)) to stratify the HNSCC patients in early and late clinical stages (TNM) and to predict the risk using miRNAs expression profiles. A six miRNA signature was identified that can stratify patients in the early and late stages. The mean accuracy, sensitivity, specificity, and area under the curve (AUC) was found to be 0.84, 0.87, 0.78, and 0.82, respectively indicating the robust performance of the generated model. The prognostic signature of eight miRNAs was identified using LASSO (least absolute shrinkage and selection operator) penalized regression. These miRNAs were found to be significantly associated with overall survival of the patients. The pathway and functional enrichment analysis of the identified biomarkers revealed their involvement in important cancer pathways such as GP6 signalling, Wnt signalling, p53 signalling, granulocyte adhesion, and dipedesis. To the best of our knowledge, this is the first such study and we hope that these signature miRNAs will be useful for the risk stratification of patients and the design of therapeutic modalities.

Kumar Sugandh, Patnaik Srinivas, Dixit Anshuman


Biomarker, Head and neck cancer, Machine learning, TNM stage, mRNA, microRNA

Surgery Surgery

Artificial intelligence in gastric cancer: Application and future perspectives.

In World journal of gastroenterology ; h5-index 103.0

Gastric cancer is the fourth leading cause of cancer-related mortality across the globe, with a 5-year survival rate of less than 40%. In recent years, several applications of artificial intelligence (AI) have emerged in the gastric cancer field based on its efficient computational power and learning capacities, such as image-based diagnosis and prognosis prediction. AI-assisted diagnosis includes pathology, endoscopy, and computerized tomography, while researchers in the prognosis circle focus on recurrence, metastasis, and survival prediction. In this review, a comprehensive literature search was performed on articles published up to April 2020 from the databases of PubMed, Embase, Web of Science, and the Cochrane Library. Thereby the current status of AI-applications was systematically summarized in gastric cancer. Moreover, future directions that target this field were also analyzed to overcome the risk of overfitting AI models and enhance their accuracy as well as the applicability in clinical practice.

Niu Peng-Hui, Zhao Lu-Lu, Wu Hong-Liang, Zhao Dong-Bing, Chen Ying-Tai


Artificial intelligence, Deep learning, Gastric cancer, Image-based diagnosis, Machine learning, Prognosis prediction

Surgery Surgery

The C terminus of DJ-1 determines its homodimerization, MGO detoxification activity and suppression of ferroptosis.

In Acta pharmacologica Sinica

DJ-1 is a multifunctional protein associated with cancers and autosomal early-onset Parkinson disease. Besides the well-documented antioxidative stress activity, recent studies show that DJ-1 has deglycation enzymatic activity and anti-ferroptosis effect. It has been shown that DJ-1 forms the homodimerization, which dictates its antioxidative stress activity. In this study, we investigated the relationship between the dimeric structure of DJ-1 and its newly reported activities. In HEK293T cells with Flag-tagged and Myc-tagged DJ-1 overexpression, we performed deletion mutations and point mutations, narrowed down the most critical motif at the C terminus. We found that the deletion mutation of the last three amino acids at the C terminus of DJ-1 (DJ-1 ΔC3) disrupted its homodimerization with the hydrophobic L187 residue being of great importance for DJ-1 homodimerization. In addition, the ability in methylglyoxal (MGO) detoxification and deglycation was almost abolished in the mutation of DJ-1 ΔC3 and point mutant L187E compared with wild-type DJ-1 (DJ-1 WT). We also showed the suppression of erastin-triggered ferroptosis in DJ-1-/- mouse embryonic fibroblast cells was abolished by ΔC3 and L187E, but partially diminished by V51C. Thus, our results demonstrate that the C terminus of DJ-1 is crucial for its homodimerization, deglycation activity, and suppression of ferroptosis.

Jiang Li, Chen Xiao-Bing, Wu Qian, Zhu Hai-Ying, Du Cheng-Yong, Ying Mei-Dan, He Qiao-Jun, Zhu Hong, Yang Bo, Cao Ji


C terminus, DJ-1, DJ-1−/− mouse embryonic fibroblast cells, HEK293T cells, deglycation, ferroptosis, homodimerization, methylglyoxal (MGO) detoxification

General General

Machine learning-guided discovery and design of non-hemolytic peptides.

In Scientific reports ; h5-index 158.0

Reducing hurdles to clinical trials without compromising the therapeutic promises of peptide candidates becomes an essential step in peptide-based drug design. Machine-learning models are cost-effective and time-saving strategies used to predict biological activities from primary sequences. Their limitations lie in the diversity of peptide sequences and biological information within these models. Additional outlier detection methods are needed to set the boundaries for reliable predictions; the applicability domain. Antimicrobial peptides (AMPs) constitute an extensive library of peptides offering promising avenues against antibiotic-resistant infections. Most AMPs present in clinical trials are administrated topically due to their hemolytic toxicity. Here we developed machine learning models and outlier detection methods that ensure robust predictions for the discovery of AMPs and the design of novel peptides with reduced hemolytic activity. Our best models, gradient boosting classifiers, predicted the hemolytic nature from any peptide sequence with 95-97% accuracy. Nearly 70% of AMPs were predicted as hemolytic peptides. Applying multivariate outlier detection models, we found that 273 AMPs (~ 9%) could not be predicted reliably. Our combined approach led to the discovery of 34 high-confidence non-hemolytic natural AMPs, the de novo design of 507 non-hemolytic peptides, and the guidelines for non-hemolytic peptide design.

Plisson Fabien, Ramírez-Sánchez Obed, Martínez-Hernández Cristina


General General

Accelerated knowledge discovery from omics data by optimal experimental design.

In Nature communications ; h5-index 260.0

How to design experiments that accelerate knowledge discovery on complex biological landscapes remains a tantalizing question. We present an optimal experimental design method (coined OPEX) to identify informative omics experiments using machine learning models for both experimental space exploration and model training. OPEX-guided exploration of Escherichia coli's populations exposed to biocide and antibiotic combinations lead to more accurate predictive models of gene expression with 44% less data. Analysis of the proposed experiments shows that broad exploration of the experimental space followed by fine-tuning emerges as the optimal strategy. Additionally, analysis of the experimental data reveals 29 cases of cross-stress protection and 4 cases of cross-stress vulnerability. Further validation reveals the central role of chaperones, stress response proteins and transport pumps in cross-stress exposure. This work demonstrates how active learning can be used to guide omics data collection for training predictive models, making evidence-driven decisions and accelerating knowledge discovery in life sciences.

Wang Xiaokang, Rai Navneet, Merchel Piovesan Pereira Beatriz, Eetemadi Ameen, Tagkopoulos Ilias