Tag: BioInformatics

Price: $51.99
(as of Dec 27,2024 10:03:29 UTC – Details)

ASIN ‏ : ‎ B09Q33LHZ9
Publisher ‏ : ‎ Academic Press (January 8, 2022)
Publication date ‏ : ‎ January 8, 2022
Language ‏ : ‎ English
File size ‏ : ‎ 134522 KB
Text-to-Speech ‏ : ‎ Enabled
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Enabled
Print length ‏ : ‎ 362 pages

Deep Learning in Bioinformatics: Techniques and Applications in Practice

Bioinformatics is a rapidly evolving field that combines biology, computer science, and statistics to analyze and interpret biological data. Deep learning, a subset of machine learning, has gained popularity in recent years due to its ability to extract complex patterns and features from large datasets. In bioinformatics, deep learning techniques have been applied to a wide range of problems, including genomics, proteomics, and drug discovery.

One of the key advantages of deep learning in bioinformatics is its ability to handle high-dimensional data, such as DNA sequences and gene expression profiles. Traditional machine learning algorithms often struggle with these types of data, but deep learning models, such as convolutional neural networks and recurrent neural networks, can effectively capture the underlying patterns in biological data.

In practice, deep learning has been used in bioinformatics for a variety of applications. For example, deep learning models have been developed to predict protein structure and function, identify disease-associated genetic mutations, and classify different types of cancer based on gene expression data. These applications have the potential to revolutionize the field of bioinformatics by providing more accurate and efficient tools for analyzing biological data.

Overall, deep learning in bioinformatics holds great promise for advancing our understanding of complex biological systems and developing new treatments for diseases. By leveraging the power of deep learning techniques, researchers can uncover hidden patterns in biological data that were previously inaccessible, leading to new insights and discoveries in the field of bioinformatics.
#Deep #Learning #Bioinformatics #Techniques #Applications #Practice

Price: $170.00 – $128.55
(as of Dec 17,2024 23:05:14 UTC – Details)

Publisher ‏ : ‎ Academic Press; 1st edition (February 2, 2022)
Language ‏ : ‎ English
Paperback ‏ : ‎ 380 pages
ISBN-10 ‏ : ‎ 0128238224
ISBN-13 ‏ : ‎ 978-0128238226
Item Weight ‏ : ‎ 1.71 pounds
Dimensions ‏ : ‎ 7.52 x 0.86 x 9.25 inches

Deep Learning in Bioinformatics: Techniques and Applications in Practice

Deep learning, a subset of machine learning, has revolutionized the field of bioinformatics by providing powerful tools for analyzing complex biological data. In this post, we will explore some of the key techniques used in deep learning for bioinformatics and discuss their applications in real-world scenarios.

One of the most common deep learning techniques used in bioinformatics is convolutional neural networks (CNNs). CNNs are particularly well-suited for analyzing biological data such as DNA sequences and protein structures, as they can automatically learn relevant features from the input data. CNNs have been used for tasks such as predicting protein structure and function, identifying genetic variations, and classifying gene expression patterns.

Another important technique in deep learning for bioinformatics is recurrent neural networks (RNNs). RNNs are designed to handle sequential data, making them ideal for analyzing time-series data such as gene expression profiles or protein interactions. RNNs have been used for tasks such as predicting gene expression levels, identifying regulatory elements in DNA sequences, and predicting protein-protein interactions.

In addition to CNNs and RNNs, other deep learning techniques such as generative adversarial networks (GANs) and reinforcement learning are also being applied in bioinformatics. GANs can be used to generate realistic biological data, such as synthetic DNA sequences or protein structures, which can be used to augment existing datasets. Reinforcement learning can be used to optimize experimental designs or drug discovery pipelines, by automatically learning optimal strategies based on feedback from the environment.

In practice, deep learning techniques in bioinformatics have been successfully applied to a wide range of problems, including drug discovery, personalized medicine, and precision agriculture. For example, deep learning models have been used to predict drug-target interactions, identify biomarkers for disease diagnosis, and optimize crop yields based on environmental conditions.

Overall, deep learning has the potential to revolutionize the field of bioinformatics by enabling researchers to analyze and interpret large-scale biological data more effectively. By leveraging the power of deep learning techniques, researchers can uncover new insights into complex biological processes and develop innovative solutions to pressing challenges in healthcare, agriculture, and beyond.
#Deep #Learning #Bioinformatics #Techniques #Applications #Practice