Rheumatoid Arthritis

CCSynergy: an integrative deep-learning framework enabling context-aware prediction of anti-cancer drug synergy.

Combination therapy is a promising strategy for confronting the complexity of cancer. However, exper...

AgeAnno: a knowledgebase of single-cell annotation of aging in human.

Aging is a complex process that accompanied by molecular and cellular alterations. The identificatio...

In vitro investigation of monoglycerides and zinc glycinate: anti-inflammatory and epithelial barrier function.

The objectives of this study were to investigate the in vitro immune-modulatory effects of monoglyce...

In Vitro Antiviral Activity of a New Indol-3-carboxylic Acid Derivative Against SARS-CoV-2.

The coronavirus disease (COVID-19) pandemic has brought into sharp relief the threat posed by corona...

Recent Trends in Computer-aided Drug Design for Anti-cancer Drug Discovery.

Cancer is considered one of the deadliest diseases globally, and continuous research is being carrie...

Prescreening in oncology trials using medical records. Natural language processing applied on lung cancer multidisciplinary team meeting reports.

Defining profiles of patients that could benefit from relevant anti-cancer treatments is essential. ...

Ensemble biomarkers for guiding anti-angiogenesis therapy for ovarian cancer using deep learning.

Exploring Anti-osteoporosis Medicinal Herbs using Cheminformatics and Deep Learning Approaches.

BACKGROUND: Osteoporosis is a prevalent disease for the aged population. Chinese herbderived natural...

Anti-noise computational imaging using unsupervised deep learning.

Computational imaging enables spatial information retrieval of objects with the use of single-pixel ...

Heavy chain sequence-based classifier for the specificity of human antibodies.

Antibodies specifically bind to antigens and are an essential part of the immune system. Hence, anti...

Computational anti-COVID-19 drug design: progress and challenges.

Vaccines have made gratifying progress in preventing the 2019 coronavirus disease (COVID-19) pandemi...

Identifying multi-functional bioactive peptide functions using multi-label deep learning.

The bioactive peptide has wide functions, such as lowering blood glucose levels and reducing inflamm...

Machine Learning Prediction of Antimicrobial Peptides.

Antibiotic resistance constitutes a global threat and could lead to a future pandemic. One strategy ...

Artificial intelligence and machine learning methods in predicting anti-cancer drug combination effects.

Drug combinations have exhibited promising therapeutic effects in treating cancer patients with less...

Unveiling the immune infiltrate modulation in cancer and response to immunotherapy by MIXTURE-an enhanced deconvolution method.

The accurate quantification of tumor-infiltrating immune cells turns crucial to uncover their role i...

Large-scale comparative review and assessment of computational methods for anti-cancer peptide identification.

Anti-cancer peptides (ACPs) are known as potential therapeutics for cancer. Due to their unique abil...

Artificial Intelligence for Prediction of Anti-VEGF Treatment Burden in Retinal Diseases: Towards Precision Medicine.

A convolutional neural network algorithm for breast tumor detection with magnetic detection electrical impedance tomography.

Breast cancer is a malignant tumor disease for which early detection, diagnosis, and treatment are o...

Dr AFC: drug repositioning through anti-fibrosis characteristic.

Fibrosis is a key component in the pathogenic mechanism of a variety of diseases. These diseases inv...

Machine learning approaches for elucidating the biological effects of natural products.

Covering: 2000 to 2020 Machine learning (ML) is an efficient tool for the prediction of bioactivity ...

Chronic kidney disease in Hepatitis C and its association with liver cirrhosis and viral load: Revealing the importance of hematuria.

Hepatitis C virus (HCV) contributed as a risk factor for chronic kidney disease (CKD). Many studies ...