National age-specific mortality trends for cervical and breast cancers in urban-rural areas of China from 2009 to 2021: a population-based analysis.

Background: Cervical and breast cancers are among the top 4 leading causes of cancer-related mortality in women. This study aimed to examine age-specific temporal trends in mortality for cervical and breast cancers in urban and rural areas of China from 2009 to 2021.

Methods: Age-specific mortality data for cervical and breast cancers among Chinese women aged 20-84 years were obtained from China's National Disease Surveillance Points system spanning the years 2009 to 2021.

A computational method to predict topologically associating domain boundaries combining histone Marks and sequence information.

Background: The three-dimensional (3D) structure of chromatins plays significant roles during cell differentiation and development. Hi-C and other 3C-based technologies allow us to look deep into the chromatin architectures. Many studies have suggested that topologically associating domains (TAD), as the structure and functional unit, are conserved across different organs.

[Effect of LDHA Knockdown by siRNA on Migration and Invasion of ErbB2 Overexpressing Breast Cancer Cell Line].

Objective: To investigate the effect of small interfering RNA of lactate dehydrogenase A (si) on migration and invasion of epidermal growth factor receptor 2 (ErbB2) over expressing breast cancer cell line SK-BR-3, MDA-MB-453 and its molecular mechanism.

Methods: SK-BR-3 and MDA-MB-453 cells were transfected with si to interfere with the expression of LDHA. The transfection of scramble siRNA was used as negative control.

Prediction of nicotinamide adenine dinucleotide interacting sites based on ensemble support vector machine.

Nicotinamide adenine dinucleotide (NAD) plays an important role in cellular metabolism and acts as hydrideaccepting and hydride-donating coenzymes in energy production. Identification of NAD protein interacting sites can significantly aid in understanding the NAD dependent metabolism and pathways, and it could further contribute useful information for drug development. In this study, a computational method is proposed to predict NAD-protein interacting sites using the sequence information and structure-based information.

QSAR study for cytotoxicity of diterpenoid tanshinones.

A quantitative structure-activity relationship (QSAR) of a series of tanshinone compounds with cytotoxicity against murine leukemia cell lines P-388 has been studied using density functional theory (DFT) method combined with statistical analysis. Four main independent factors contributing to the cytotoxicity including the maximum molecular electrostatic potential at the SAS surface (SAS (max)), the average nucleophilic superdelocalizability (ANS), the dihedral between ring A and B (u) and the net atomic charge of C (12) (Q(C (12))) were selected by stepwise multiple regression method, then the QSAR equation was established via multiple linear regression (MLR) analysis. These descriptors accounted for 74.

A novel method to predict protein-protein interactions based on the information of protein-protein interaction networks and protein sequence.

Protein-protein interactions (PPIs) are crucial to most biochemical processes in human beings. Although many human PPIs have been identified by experiments, the number is still limited compared to the available protein sequences of human organisms. Recently, many computational methods have been proposed to facilitate the recognition of novel human PPIs.

Discrimination of parotid neoplasms from the normal parotid gland by use of Raman spectroscopy and support vector machine.

Preoperative diagnosis of neoplasms in the parotid gland is essential for successful surgical treatment. The purpose of this study is to apply Raman spectroscopy in order to distinguish the spectral differences between pleomorphic adenoma and Warthin tumor from that of normal parotid gland tissues. Furthermore we establish the diagnostic model of the Raman spectra of neoplasms in parotid gland by employing support vector machine (SVM) with Gaussian radial basis function.

On the relation between residue flexibility and residue interactions in proteins.

B-factor from X-ray crystal structure can well measure protein structural flexibility, which plays an important role in different biological processes, such as catalysis, binding and molecular recognition. Understanding the essence of flexibility can be helpful for the further study of the protein function. In this study, we attempted to correlate the flexibility of a residue to its interactions with other residues by representing the protein structure as a residue contact network.

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.

A semi-supervised learning based method: Laplacian support vector machine used in diabetes disease diagnosis.

Pattern recognition methods could be of great help to disease diagnosis. In this study, a semi-supervised learning based method, Laplacian support vector machine (LapSVM), was used in diabetes diseases prediction. The diabetes disease dataset used in this article is Pima Indians diabetes dataset obtained from the UCI Repository of Machine Learning Databases and all patients in the dataset are females at least 21 years old of Pima Indian heritage.

[Near infrared Raman spectral character and diagnostic value of squamous cell carcinoma of oral mucosa].

Objective: To evaluate the value of the near infrared Raman spectroscope in diagnosing oral squamous cell carcinoma (OSCC).

Methods: Near infrared Raman spectra of ten normal mucosa, twenty OSCC and thirty oral leukoplakia (OLK) cases were collected in the research. Based on the previous researches, the information of the subtracted spectra of compared group was gained by the characteristic band in them.

An ensemble classifier of support vector machines used to predict protein structural classes by fusing auto covariance and pseudo-amino acid composition.

The purpose of this article is to identify protein structural classes by using support vector machine (SVM) ensemble classifier, which is very efficient in enhancing prediction performance. Firstly, auto covariance (AC) and pseudo-amino acid composition (PseAAC) were used in protein representation. AC focuses on adjacent effects and PseAA composition takes sequence order patterns into account.

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.

Analyzing functional similarity of protein sequences with discrete wavelet transform.

This paper applies discrete wavelet transform (DWT) with various protein substitution models to find functional similarity of proteins with low identity. A new metric, 'S' function, based on the DWT is proposed to measure the pair-wise similarity. We also develop a segmentation technique, combined with DWT, to handle long protein sequences.