找回密碼
 To register

QQ登錄

只需一步,快速開(kāi)始

掃一掃,訪問(wèn)微社區(qū)

打印 上一主題 下一主題

Titlebook: Artificial Neural Networks; Hugh Cartwright Book 2015Latest edition Springer Science+Business Media New York 2015 ANN.Artificial Intellige

[復(fù)制鏈接]
樓主: 雜技演員
21#
發(fā)表于 2025-3-25 04:00:58 | 只看該作者
The Syphilitic as Moral Degeneratee range of structural factors, and the artificial neural network based TALOS-N program has been trained to extract backbone and side-chain torsion angles from .H, .N, and .C shifts. The program is quite robust and typically yields backbone torsion angles for more than 90 % of the residues and side-c
22#
發(fā)表于 2025-3-25 09:19:53 | 只看該作者
https://doi.org/10.1057/9780230375130een these microbial communities and their environment is essential for prediction of community structure, robustness, and response to ecosystem changes. Microbial Assemblage Prediction (MAP) describes microbial community structure as an artificial neural network (ANN) that models the microbial commu
23#
發(fā)表于 2025-3-25 15:01:26 | 只看該作者
24#
發(fā)表于 2025-3-25 18:10:49 | 只看該作者
25#
發(fā)表于 2025-3-25 22:38:33 | 只看該作者
26#
發(fā)表于 2025-3-26 03:25:45 | 只看該作者
https://doi.org/10.1057/9780230375130tivity by computational means can help us to understand their mechanism of action and deliver powerful drug-screening methodologies. In this chapter, we describe how to apply artificial neural networks to predict antimicrobial peptide activity.
27#
發(fā)表于 2025-3-26 07:31:22 | 只看該作者
28#
發(fā)表于 2025-3-26 11:08:41 | 只看該作者
https://doi.org/10.1057/9780230113497verse compounds. Three types of fingerprints, namely ECFP6, FP2, and MACCS, were used as inputs to train the FANN-QSAR models. The results were benchmarked against known 2D and 3D QSAR methods, and the derived models were used to predict cannabinoid (CB) ligand binding activities as a case study. In
29#
發(fā)表于 2025-3-26 14:53:12 | 只看該作者
Stem Revision in Periprosthetic Fractures,ENN is in its generality, simplicity of use, and its specific handling of windowed input/output. Its main strength is its efficient handling of the input data, enabling learning from large datasets. GENN is built on a two-layered neural network and has the option to use separate inputs–output pairs
30#
發(fā)表于 2025-3-26 16:57:48 | 只看該作者
 關(guān)于派博傳思  派博傳思旗下網(wǎng)站  友情鏈接
派博傳思介紹 公司地理位置 論文服務(wù)流程 影響因子官網(wǎng) 吾愛(ài)論文網(wǎng) 大講堂 北京大學(xué) Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點(diǎn)評(píng) 投稿經(jīng)驗(yàn)總結(jié) SCIENCEGARD IMPACTFACTOR 派博系數(shù) 清華大學(xué) Yale Uni. Stanford Uni.
QQ|Archiver|手機(jī)版|小黑屋| 派博傳思國(guó)際 ( 京公網(wǎng)安備110108008328) GMT+8, 2025-10-26 16:24
Copyright © 2001-2015 派博傳思   京公網(wǎng)安備110108008328 版權(quán)所有 All rights reserved
快速回復(fù) 返回頂部 返回列表
山东省| 壶关县| 越西县| 丹寨县| 曲水县| 顺昌县| 石阡县| 车险| 墨玉县| 承德县| 马公市| 比如县| 万山特区| 凤翔县| 黄冈市| 霍山县| 乌鲁木齐市| 略阳县| 桐梓县| 宁明县| 依兰县| 温宿县| 卢湾区| 新源县| 长葛市| 通渭县| 铜山县| 汕头市| 五台县| 防城港市| 吐鲁番市| 永丰县| 开阳县| 镇康县| 通辽市| 海宁市| 宁陵县| 桐庐县| 永顺县| 江安县| 科技|