Machine-Learning-Assisted Rational Design of Si─Rhodamine as Cathepsin-pH-Activated Probe for Accurate Fluorescence Navigation.

High-performance fluorescent probes stand as indispensable tools in fluorescence-guided imaging, and are crucial for precise delineation of focal tissue while minimizing unnecessary removal of healthy tissue. Herein, machine-learning-assisted strategy to investigate the current available xanthene dyes is first proposed, and a quantitative prediction model to guide the rational synthesis of novel fluorescent molecules with the desired pH responsivity is constructed. Two novel Si─rhodamine derivatives are successfully achieved and the cathepsin/pH sequentially activated probe Si─rhodamine─cathepsin-pH (SiR─CTS-pH) is constructed.

FAM171B as a Novel Biomarker Mediates Tissue Immune Microenvironment in Pulmonary Arterial Hypertension.

The purpose of this study was to uncover potential diagnostic indicators of pulmonary arterial hypertension (PAH), evaluate the function of immune cells in the pathogenesis of the disease, and find innovative treatment targets and medicines with the potential to enhance prognosis. Gene Expression Omnibus was utilized to acquire the PAH datasets. We recognized differentially expressed genes (DEGs) and investigated their functions utilizing R software.

Prediction of neurotoxins by support vector machine based on multiple feature vectors.

Neurotoxin is a toxin which acts on nerve cells by interacting with membrane proteins. Different neurotoxins have different functions and sources. With much more knowledge of neurotoxins it would be greatly helpful for the development of drug design.

Using the augmented Chou's pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach.

The submitochondria location of a mitochondrial protein is very important for further understanding the structure and function of this protein. Hence, it is of great practical significance to develop an automated and reliable method for timely identifying the submitochondria locations of novel mitochondrial proteins. In this study, a sequence-based algorithm combining the augmented Chou's pseudo amino acid composition (Chou's PseAA) based on auto covariance (AC) is developed to predict protein submitochondria locations and membrane protein types in mitochondria inner membrane.

Local sequence information-based support vector machine to classify voltage-gated potassium channels.

In our previous work, we developed a computational tool, PreK-ClassK-ClassKv, to predict and classify potassium (K+) channels. For K+ channel prediction (PreK) and classification at family level (ClassK), this method performs well. However, it does not perform so well in classifying voltage-gated potassium (Kv) channels (ClassKv).

Fast fourier transform-based support vector machine for prediction of G-protein coupled receptor subfamilies.

Although the sequence information on G-protein coupled receptors (GPCRs) continues to grow, many GPCRs remain orphaned (i.e. ligand specificity unknown) or poorly characterized with little structural information available, so an automated and reliable method is badly needed to facilitate the identification of novel receptors.