The precise classification of DNA sequences is pivotal in genomics, holding significant implications for personalized medicine. The stakes are particularly high when classifying key genetic markers such as BRAC, related to breast cancer susceptibility; BRAF, associated with various malignancies; and KRAS, a recognized oncogene. Conventional machine learning techniques often necessitate intricate feature engineering and may not capture the full spectrum of sequence dependencies. To ameliorate these limitations, this study employs an adapted UNet architecture, originally designed for biomedical image segmentation, to classify DNA sequences.The attention mechanism was also tested LONG WITH u-Net architecture to precisely classify DNA sequences

Four  alkaloids  compounds  were  extracted  from  the  fruits  and leaves, of plant known locally as (Anab AI-Thebe Solanum nigram), by various solvents systems, from an earlier study by the researcher. DNA tested its effect in plasmid PBR322 deportation  method using Gel Electrophoresis. Results showed that two of those extract for full effectiveness   digestible   pieces  of  RNA and  DNA  plasmid,   and digestive  partly  of  the  other  alternatives.  That  could  prove  results indicate that this type of alkaloids consist of   biological effectiveness of anti-twnors, through

The fingerprinting DNA method which depends on the unique pattern in this study was employed to detect the hydatid cyst of Echinococcus granulosus and to determine the genetic variation among their strains in different intermediate hosts (cows and sheep).  The unique pattern represents the number of amplified bands and their molecular weights with specialized sequences to one sample which different from the other samples.   Five hydatitd cysts samples  from cows and sheep were  collected, genetic analysis for  isolated DNA was done using PCR technique and  Random Amplified Polymorphic DNA reaction(RAPD) depending on (4) random primers, and the results showed:      

Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a

This  paper represent  the second  step  i n  a molecular clon i ng program ai ming to clone large DNA  fi·agmen ts of the sal t tolerant  bermudagrass (Cyrwdon  dactylon  L.)  DNA  usi ng  the  bacteriophage  (EM13L3) as    a vector.

In th is  work, a yield of about  I 00  g bacteriophage  DNA  per one  liter culture.was obtained  with.a  purity ranging between (1.7-1.8). The vector JJNA  v.as  completely   double   digested   with  the  restriction   enzymes llamHI   and  EcoRI,  followed  by  pu

Autorías: Nuha Mohsin Dhahi, Muhammad Hamza Shihab. Localización: Revista iberoamericana de psicología del ejercicio y el deporte. Nº. 6, 2022. Artículo de Revista en Dialnet.