Biological sequence classification: A review on data and general methods
C Ao, S Jiao, Y Wang, L Yu, Q Zou - Research, 2022 - spj.science.org
With the rapid development of biotechnology, the number of biological sequences has
grown exponentially. The continuous expansion of biological sequence data promotes the …
grown exponentially. The continuous expansion of biological sequence data promotes the …
IF-AIP: a machine learning method for the identification of anti-inflammatory peptides using multi-feature fusion strategy
Background: The most commonly used therapy currently for inflammatory and autoimmune
diseases is nonspecific anti-inflammatory drugs, which have various hazardous side effects …
diseases is nonspecific anti-inflammatory drugs, which have various hazardous side effects …
[HTML][HTML] Databases and computational methods for the identification of piRNA-related molecules: A survey
C Guo, X Wang, H Ren - Computational and Structural Biotechnology …, 2024 - Elsevier
Piwi-interacting RNAs (piRNAs) are a class of small non-coding RNAs (ncRNAs) that play
important roles in many biological processes and major cancer diagnosis and treatment …
important roles in many biological processes and major cancer diagnosis and treatment …
pcPromoter-CNN: a CNN-based prediction and classification of promoters
A promoter is a small region within the DNA structure that has an important role in initiating
transcription of a specific gene in the genome. Different types of promoters are recognized …
transcription of a specific gene in the genome. Different types of promoters are recognized …
Multi-scale context aggregation for strawberry fruit recognition and disease phenotyping
Timely harvesting and disease identification of strawberry fruits is a major concern for
commercial level cultivators. Failing to harvest the grown strawberries can result in the fruit …
commercial level cultivators. Failing to harvest the grown strawberries can result in the fruit …
Unlocking the therapeutic potential of drug combinations through synergy prediction using graph transformer networks
Drug combinations are frequently used to treat cancer to reduce side effects and increase
efficacy. The experimental discovery of drug combination synergy is time-consuming and …
efficacy. The experimental discovery of drug combination synergy is time-consuming and …
[HTML][HTML] TS-m6A-DL: Tissue-specific identification of N6-methyladenosine sites using a universal deep learning model
The most communal post-transcriptional modification, N6-methyladenosine (m6A), is
associated with a number of crucial biological processes. The precise detection of m6A sites …
associated with a number of crucial biological processes. The precise detection of m6A sites …
[HTML][HTML] Identification of piRNA disease associations using deep learning
Piwi-interacting RNAs (piRNAs) play a pivotal role in maintaining genome integrity by
repression of transposable elements, gene stability, and association with various disease …
repression of transposable elements, gene stability, and association with various disease …
An efficient lightweight hybrid model with attention mechanism for enhancer sequence recognition
Enhancers are sequences with short motifs that exhibit high positional variability and free
scattering properties. Identification of these noncoding DNA fragments and their strength are …
scattering properties. Identification of these noncoding DNA fragments and their strength are …
[HTML][HTML] Identifying DNA N4-methylcytosine sites in the rosaceae genome with a deep learning model relying on distributed feature representation
Abstract DNA N4-methylcytosine (4mC), an epigenetic modification found in prokaryotic and
eukaryotic species, is involved in numerous biological functions, including host defense …
eukaryotic species, is involved in numerous biological functions, including host defense …