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Biological neural networks
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Lecture Artificial intelligence: Artificial neural network. This lecture provides students with content including: computing systems inspired by biological neural networks; consists of several processing elements that receive inputs and deliver outputs; "learn" to perform tasks by considering examples; be able to model nonlinear processes;... Please refer to the detailed content of the lecture!
47p
codabach1016
03-05-2024
2
0
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Deep learning is a subdiscipline of artificial intelligence that uses a machine learning technique called artificial neural networks to extract patterns and make predictions from large data sets. The increasing adoption of deep learning across healthcare domains together with the availability of highly characterised cancer datasets has accelerated research into the utility of deep learning in the analysis of the complex biology of cancer.
17p
vibransone
28-03-2024
3
2
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Ebook "Plant tissue culture engineering" signals a turning point: the recognition that this specialized field of plant science must be integrated with engineering principles in order to develop efficient, cost effective, and large scale applications of these technologies. I am most impressed with the organization of this volume, and the extensive list of chapters contributed by expert authors from around the world who are leading the emergence of this interdisciplinary enterprise.
469p
cotieubac1004
15-03-2024
3
0
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Radiotherapy has been widely used to treat various cancers, but its efcacy depends on the individual involved. Traditional gene-based machine-learning models have been widely used to predict radiosensitivity.
15p
vileonardodavinci
23-12-2023
6
3
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"Artificial neural networks in real life applications" offers an outlook of the most recent works at the field of the Artificial Neural Networks (ANN), including theoretical developments and applications of systems using intelligent characteristics for adaptability.
395p
haojiubujain08
01-11-2023
6
3
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Ebook "The handbook of brain theory and neural networks" includes content: Background - The elements of brain theory and neural networks, road maps - A guided tour of brain theory and neural networks, articles.
1309p
haojiubujain07
20-09-2023
4
3
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Ebook "The neuroProcessor - An integrated interface to biological neural networks" includes content: Introduction, recording from biological neural networks, the neuroprocessor, integrated front end for neuronal recording, mixed signal integrated front end for neuronal recording, algorithms for neuroprocessor spike sorting, mea on chip - in vitro neuronal interfaces, conclusions.
126p
haojiubujain07
20-09-2023
5
3
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Ebook "An introduction to Neural network methods for differential equations" introduces a variety of neural network methods for solving differential equations arising in science and engineering. The emphasis is placed on a deep understanding of the neural network techniques, which has been presented in a mostly heuristic and intuitive manner. This approach will enable the reader to understand the working, efficiency and shortcomings of each neural network technique for solving differential equations.
124p
dieptieuung
19-07-2023
281
274
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Protein classification is a task of paramount importance in various fields of biology. Despite the great momentum of modern implementation of protein classification, machine learning techniques such as Random Forest and Neural Network could not always be used for several reasons: Data collection, unbalanced classification or labelling of the data.
18p
vihagrid
30-01-2023
5
3
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Screening of physicochemical properties should be considered one of the essential steps in the drug discovery pipeline. Among the available methods, biomimetic chromatography with an immobilized artificial membrane is a powerful tool for simulating interactions between a molecule and a biological membrane.
8p
viginny
23-12-2022
6
2
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Basecalling, the computational process of translating raw electrical signal to nucleotide sequence, is of critical importance to the sequencing platforms produced by Oxford Nanopore Technologies (ONT).
10p
vigalileogalilei
27-02-2022
11
1
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Single-cell RNA sequencing (scRNA-seq) offers new opportunities to study gene expression of tens of thousands of single cells simultaneously. We present DeepImpute, a deep neural network-based imputation algorithm that uses dropout layers and loss functions to learn patterns in the data, allowing for accurate imputation.
14p
vielonmusk
30-01-2022
10
0
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Alignment-free methods, more time and memory efficient than alignment-based methods, have been widely used for comparing genome sequences or raw sequencing samples without assembly.
17p
vielonmusk
30-01-2022
10
0
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Recent technological advances have enabled DNA methylation to be assayed at single-cell resolution. However, current protocols are limited by incomplete CpG coverage and hence methods to predict missing methylation states are critical to enable genome-wide analyses. We report DeepCpG, a computational approach based on deep neural networks to predict methylation states in single cells.
13p
vialfrednobel
29-01-2022
20
0
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Chromatin interactions play important roles in regulating gene expression. However, the availability of genome-wide chromatin interaction data is limited. We develop a computational method, chromatin interaction neural network (ChINN), to predict chromatin interactions between open chromatin regions using only DNA sequences.
25p
viarchimedes
26-01-2022
10
0
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Long-read sequencing enables variant detection in genomic regions that are considered difficult-to-map by short-read sequencing. To fully exploit the benefits of longer reads, here we present a deep learning method NanoCaller, which detects SNPs using long-range haplotype information, then phases long reads with called SNPs and calls indels with local realignment.
33p
viarchimedes
26-01-2022
8
0
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Accurate detection of somatic mutations is challenging but critical in understanding cancer formation, progression, and treatment. We recently proposed NeuSomatic, the first deep convolutional neural network-based somatic mutation detection approach, and demonstrated performance advantages on in silico data.
20p
viarchimedes
26-01-2022
15
0
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Models developed using Nanopore direct RNA sequencing data from in vitro synthetic RNA with all adenosine replaced by N6 -methyladenosine (m6 A) are likely distorted due to superimposed signals from saturated m6 A residues.
23p
viarchimedes
26-01-2022
13
0
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Most methods for inferring gene-gene interactions from expression data focus on intracellular interactions. The availability of high-throughput spatial expression data opens the door to methods that can infer such interactions both within and between cells. To achieve this, we developed Graph Convolutional Neural networks for Genes (GCNG).
16p
viarchimedes
26-01-2022
10
0
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Although genome-wide DNA methylomes have demonstrated their clinical value as reliable biomarkers for tumor detection, subtyping, and classification, their direct biological impacts at the individual gene level remain elusive.
27p
viarchimedes
26-01-2022
11
0
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