Data Analytics in Bioinformatics

403 405

404 406

405 407

406 408

407 409

408 410

409 411

410 412

411 413

412 414

413 415

414 416

415 417

416 418

417 419

418 420

419 421

420 422

421 423

422 424

423 425

424 426

425 427

426 428

427 429

428 431

429 432

430 433

431 434

432 435

433 436

434 437

435 438

436 439

437 440

438 441

439 442

440 443

441 444

442 445

443 446

444 447

445 448

446 449

447 450

448 451

449 452

450 453

451 454

452 455

453 456

454 457

455 459

456 461

457 462

458 463

459 464

460 465

461 466

462 467

463 468

464 469

465 470

466 471

467 472

468 473

469 474

470 475

471 476

472 477

473 478

474 479

475 480

476 481

477 482

478 483

479 484

480 485

481 486

482 487

483 488

484 489

485 490

486 491

487 492

488 493

489 494

490 495

491 496

492 497

493 498

494 499

495 500

496 501

497 502

498 503

499 504

500 505

501 507

502 508

503 509

504 510

505 511

506 512

507 513

508 514

Scrivener Publishing

100 Cummings Center, Suite 541J

Beverly, MA 01915-6106

Publishers at Scrivener

Martin Scrivener ( martin@scrivenerpublishing.com)

Phillip Carmical ( pcarmical@scrivenerpublishing.com)

Edited by

Rabinarayan Satpathy

Tanupriya Choudhury

Suneeta Satpathy

Sachi Nandan Mohanty

and

Xiaobo Zhang

This edition first published 2021 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA

© 2021 Scrivener Publishing LLC

For more information about Scrivener publications please visit www.scrivenerpublishing.com.

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.

Wiley Global Headquarters

111 River Street, Hoboken, NJ 07030, USA

For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.

Limit of Liability/Disclaimer of Warranty

While the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials, or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read.

Library of Congress Cataloging-in-Publication Data

ISBN 978-1-119-78553-8

Cover image: Pixabay.Com

Cover design by Russell Richardson

Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines

Printed in the USA

10 9 8 7 6 5 4 3 2 1

Machine learning has become increasingly popular in recent decades due to its well-defined algorithms and techniques that enable computers to learn and solve real-life problems which are difficult, time-consuming, and tedious to solve traditionally. Regarded as a subdomain of artificial intelligence, it has a gamut of applications in the field of healthcare, medical diagnosis, bioinformatics, natural language processing, stock market analysis and many more. Recently, there has been an explosion of heterogeneous biological data requiring analysis, retrieval of useful patterns, management and proper storage. Moreover, there is the additional challenge of developing automated tools and techniques that can deal with these different kinds of outsized data in order to translate and transform computational modelling of biological systems and its correlated disciplinary data for further classification, clustering, prediction and decision-making.

Machine learning has justified its potential with its application in extracting relevant information in various biological domains like bioinformatics. It has been successful in dealing with and finding efficient solutions for complex biomedical problems. Prior to the application of machine learning, traditional mathematical as well as statistical models were used along with the domain of expert intelligence to carry out investigations and experiments manually, using instruments, hands and eyes, etc. But such conventional methods alone are not enough to deal with large volumes of different types of biological data. Hence, the application of machine learning techniques has become the need of the hour in research in order to find a solution to complex bioinformatics applications for both the disciplines of computer science and biology. With this in mind, this book has been designed with a number of chapters from eminent researchers who relate and explain the machine learning techniques and their application to various bioinformatics problems such as classification and prediction of disease, feature selection, dimensionality reduction, gene selection, etc. Since the chapters are based on progressive collaborative research work on a broad range of topics and implementations, it will be of interest to both students and researchers from the computer science as well as biological domains.

This edited book is compiled using four sections, with the first section rationalizing the applications of machine learning techniques in bioinformatics with introductory chapters. The subsequent chapters in the second section flows with machine learning technological applications for dimensionality reduction, feature & gene selection, plant disease analysis & prediction as well as cluster analysis. Further, the third section of the book brings together a variety of machine learning research applications to healthcare domain. Then the book dives into the concluding remarks of machine learning applications to stock market behavioural analysis and prediction.